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Open AccessEditorial

Preface of the ETLTC 2025 Conference Series

by Debopriyo Roy, George Fragulis and Peter Ilic

Eng. Proc. 2025, 107(1), 1; https://doi.org/10.3390/engproc2025107001 - 20 Aug 2025

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Abstract

We are pleased to present the proceedings of the ETLTC–ICETM2025 International Conference Series, held from January 20 to 26, 2025, in Aizuwakamatsu, Japan, and hosted in hybrid format by the University of Aizu [...] Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 958 KB

Open AccessProceeding Paper

Exploring the Limits of LLMs in Simulating Partisan Polarization with Confirmation Bias Prompts

by Masashi Sakurai, Kento Ueta and Yasuhiro Hashimoto

Eng. Proc. 2025, 107(1), 2; https://doi.org/10.3390/engproc2025107002 - 20 Aug 2025

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Abstract

In this study, we investigate the potential of large language models (LLMs) to simulate partisan political polarization through conversation experiments. While previous research has demonstrated that LLM agents fail to reproduce human-like partisan polarization due to their inherent biases, we hypothesized that incorporating [...] Read more.

In this study, we investigate the potential of large language models (LLMs) to simulate partisan political polarization through conversation experiments. While previous research has demonstrated that LLM agents fail to reproduce human-like partisan polarization due to their inherent biases, we hypothesized that incorporating confirmation bias prompts could help overcome these limitations. We conducted conversation simulations between LLM agents assigned Democratic and Republican ideologies, analyzing both intra-party and inter-party interactions. Results without confirmation bias prompts revealed that agents, particularly those with Republican ideologies, tended to shift toward Democratic positions, failing to replicate human partisan behavior. However, when confirmation bias prompts were introduced, agents maintained their initial political stances more consistently, especially in intra-party conversations. While some tendency toward moderation remained in cross-party discussions, the magnitude of position shifts was significantly reduced. These findings suggest that confirmation bias prompts can effectively mitigate LLMs’ inherent biases in partisan simulations, though additional refinements may be needed to fully replicate human polarization dynamics. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 1985 KB

Open AccessProceeding Paper

Enhancing the Haar Cascade Algorithm for Robust Detection of Facial Features in Complex Conditions Using Area Analysis and Adaptive Thresholding

by Dayne Fradejas, Vince Harley Gaba, Analyn Yumang and Ericson Dimaunahan

Eng. Proc. 2025, 107(1), 3; https://doi.org/10.3390/engproc2025107003 - 21 Aug 2025

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Abstract

Facial features are critical visual indicators for understanding what a person is experiencing, providing valuable insights into their emotions and physical states. However, accurately detecting these features under diverse conditions remains a significant challenge, especially in computationally constrained environments. This paper presents a [...] Read more.

Facial features are critical visual indicators for understanding what a person is experiencing, providing valuable insights into their emotions and physical states. However, accurately detecting these features under diverse conditions remains a significant challenge, especially in computationally constrained environments. This paper presents a facial feature extraction method designed to identify regions of interest for detecting facial cues, with a focus on improving the accuracy of eye and mouth detection. Addressing the limitations of standard Haar cascade classifiers, particularly in challenging scenarios such as droopy eyes, red eyes, and droopy mouths, this method introduces a correction algorithm rooted in normal human facial anatomy, emphasizing symmetry and consistent feature placement. By integrating this correction algorithm with a feature-based refinement process, the proposed approach enhances detection accuracy from 67.22% to 96.11%. Through this method, the accurate detection of facial features like the eyes and mouth is significantly improved, offering a lightweight and efficient solution for real-time applications while maintaining computational efficiency. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 1341 KB

Open AccessProceeding Paper

Lost by Over-Management: Adaptive Notification Model for Handling Weakly Planned Activities

by Angelita Gozaly and Evgeny Pyshkin

Eng. Proc. 2025, 107(1), 4; https://doi.org/10.3390/engproc2025107004 - 21 Aug 2025

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Abstract

The study explores the scenarios and approach to the design of the software for managing notifications about the fuzzily planned activities. Though many such scenarios can be solved by using traditional time and activity management tools such as organizers, diaries, planners, or schedulers, [...] Read more.

The study explores the scenarios and approach to the design of the software for managing notifications about the fuzzily planned activities. Though many such scenarios can be solved by using traditional time and activity management tools such as organizers, diaries, planners, or schedulers, practical situations often arise when people tend to avoid overmanagement for real-life situations, when the plans might be flexible, and the planned activities might depend on location, contextual, and time information, which may not necessarily be well known or configured in advance. In this contribution, we describe examples of such situations and define the concept of soft planning. Following the principles of the human-driven design paradigm, we conducted a small-scale survey to gather insights into user preferences and identify drawbacks of existing digitalized activity planning and decision-making tools, often based on configurable notification management software. The findings reveal that while notifications are useful, users often encounter issues such as information overload, lack of contextual awareness, and disruptions caused by the notifications arriving at inconvenient or even inappropriate times. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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13 pages, 1130 KB

Open AccessProceeding Paper

Impact of Technological Tools on Mathematics Pedagogy: Data-Driven Insights into Educators’ Practices in Math Classrooms

by Lailani Pabilario

Eng. Proc. 2025, 107(1), 5; https://doi.org/10.3390/engproc2025107005 - 21 Aug 2025

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Abstract

Teaching with technology enhances instructional effectiveness and student engagement, particularly in mathematics, accounting, and ICT education. Digital learning creates an interactive environment that fosters deeper understanding and keeps learners updated with current trends. For teachers, it offers tools to assess student strengths and [...] Read more.

Teaching with technology enhances instructional effectiveness and student engagement, particularly in mathematics, accounting, and ICT education. Digital learning creates an interactive environment that fosters deeper understanding and keeps learners updated with current trends. For teachers, it offers tools to assess student strengths and weaknesses better, guiding them to develop targeted interventions. However, successful technology integration depends on educators’ digital skills, an area where many still face challenges. This paper aims to assess teachers’ technological and pedagogical proficiency and identify barriers to integration. The study employed a mixed-method approach with 60 teacher respondents selected through stratified random sampling from both urban and rural schools. Data was collected through online interviews, classroom observations, and pre- and post-survey questionnaires focusing on confidence, competence, and willingness to use technology. Thematic analysis and paired sample t-tests using SPSS v.20 revealed a significant improvement in teachers’ technological skills following an intervention program. It also identified both internal and external factors hindering technological integration in the classroom. Findings emphasize that sustained support and training are essential for effective technology use in the classroom and recommend that school administrators embed technology in curriculum planning to enhance both instruction and extension activities. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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11 pages, 208 KB

Open AccessProceeding Paper

A Comprehensive Analysis on Computational Thinking in Education: Open Issues and Challenges

by Jethro Jarvis Roy Jyrwa, Chandra Jayaraman and Alwin Joseph

Eng. Proc. 2025, 107(1), 6; https://doi.org/10.3390/engproc2025107006 - 21 Aug 2025

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Abstract

Computational thinking (CT) is a cognitive approach for solving problems using the concepts of algorithmic thinking, decomposition of a problem into components, identifying patterns among commonly occurring activities, and abstraction. CT promotes interdisciplinary learning and enhances problem-solving and logical reasoning abilities. In this [...] Read more.

Computational thinking (CT) is a cognitive approach for solving problems using the concepts of algorithmic thinking, decomposition of a problem into components, identifying patterns among commonly occurring activities, and abstraction. CT promotes interdisciplinary learning and enhances problem-solving and logical reasoning abilities. In this study, a comprehensive analysis of the current issues and challenges of applying CT in the educational landscape is presented with a focus on the various assessment tools and their implementation in teaching methods. The study identifies the various techniques that can be used by educators to evaluate the skills of students based on their ability to solve problems that require CT. A systematic review of the available literature and related works was conducted to analyze their importance in CT, as well as their issues and challenges. This study finds that there is a need for a unified definition and implementation guidelines on CT. The available assessment tools mainly focus on programming constructs, leaving little room for evaluating abstract concepts as challenges in the field; hence, designing and developing assessment mechanisms are also required for effective implementation of CT in an academic context. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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13 pages, 1002 KB

Open AccessProceeding Paper

Transforming Language Learning with AI: Adaptive Systems, Engagement, and Global Impact

by Thi Kim Anh Vo

Eng. Proc. 2025, 107(1), 7; https://doi.org/10.3390/engproc2025107007 - 21 Aug 2025

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Abstract

Artificial Intelligence (AI) is transforming language education through adaptive learning, automated assessments, and interactive tutoring. This study analyzes 80 peer-reviewed articles (2020–2024) to explore AI’s pedagogical and ethical dimensions. Findings show that AI-driven learning boosts engagement and proficiency via real-time feedback, yet challenges [...] Read more.

Artificial Intelligence (AI) is transforming language education through adaptive learning, automated assessments, and interactive tutoring. This study analyzes 80 peer-reviewed articles (2020–2024) to explore AI’s pedagogical and ethical dimensions. Findings show that AI-driven learning boosts engagement and proficiency via real-time feedback, yet challenges such as algorithmic bias, data privacy, and teacher adaptation remain. This paper proposes a responsible AI integration framework, emphasizing educator–technologist collaboration, professional development, and ethical governance. Addressing these concerns requires robust policies and continued research to maximize benefits while minimizing risks in AI-enhanced education. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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15 pages, 1506 KB

Open AccessProceeding Paper

Artificial Intelligence for Historical Manuscripts Digitization: Leveraging the Lexicon of Cyril

by Stavros N. Moutsis, Despoina Ioakeimidou, Konstantinos A. Tsintotas, Konstantinos Evangelidis, Panagiotis E. Nastou and Antonis Tsolomitis

Eng. Proc. 2025, 107(1), 8; https://doi.org/10.3390/engproc2025107008 - 21 Aug 2025

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Abstract

Artificial intelligence (AI) is a cutting-edge and revolutionary technology in computer science that has the potential to completely transform a wide range of disciplines, including the social sciences, the arts, and the humanities. Therefore, since its significance has been recognized in engineering and [...] Read more.

Artificial intelligence (AI) is a cutting-edge and revolutionary technology in computer science that has the potential to completely transform a wide range of disciplines, including the social sciences, the arts, and the humanities. Therefore, since its significance has been recognized in engineering and medicine, history, literature, paleography, and archaeology have recently embraced AI as new opportunities have arisen for preserving ancient manuscripts. Acknowledging the importance of digitizing archival documents, this paper explores the use of advanced technologies during this process, showing how these are employed at each stage and how the unique challenges inherent in past scripts are addressed. Our study is based on Cyril’s Lexicon, a Byzantine-era dictionary of great historical and linguistic significance in Greek territory. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 637 KB

Open AccessProceeding Paper

Enhancing Cognitive and Metacognitive Domains of Autistic Children Using Machine Learning

by Dilmi Tharaki, Yashika Rupasinghe, Piyathma Ruhunage, Ama Pehesarani and Samadhi Chathuranga Rathnayake

Eng. Proc. 2025, 107(1), 9; https://doi.org/10.3390/engproc2025107009 - 21 Aug 2025

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Abstract

ASD poses special difficulty in both cognitive and metacognitive development, necessitating specialized educational strategies. This research proposes LearnMate, a web-based application powered by machine learning techniques that aims to improve the abilities of children with autism. Utilizing classification models learned from medical data, [...] Read more.

ASD poses special difficulty in both cognitive and metacognitive development, necessitating specialized educational strategies. This research proposes LearnMate, a web-based application powered by machine learning techniques that aims to improve the abilities of children with autism. Utilizing classification models learned from medical data, LearnMate forecasts skill acquisition and suggests personalized learning activities according to the strengths and developmental requirements of the child. The system permits instructors to monitor progress through real-time feedback, enabling adaptive learning approaches. Pilot application to more than 100 children showed significant gains in their skills. The results demonstrate the immense potential for change through machine learning in special education to facilitate data-driven, personalized learning opportunities that enhance the capabilities of both autistic students and teachers. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 707 KB

Open AccessProceeding Paper

Stance and Engagement: How Community Notes Influence HPV Vaccine Conversations on X in Japan

by Kento Ueta, Masashi Sakurai and Yasuhiro Hashimoto

Eng. Proc. 2025, 107(1), 10; https://doi.org/10.3390/engproc2025107010 - 22 Aug 2025

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Abstract

This study examines the impact of Community Notes on users’ stance and posting behavior regarding the human papillomavirus (HPV) vaccine on X. Unlike previous research focusing on affected posts and authors, this study analyzes users who viewed Community Notes and their posting behavior [...] Read more.

This study examines the impact of Community Notes on users’ stance and posting behavior regarding the human papillomavirus (HPV) vaccine on X. Unlike previous research focusing on affected posts and authors, this study analyzes users who viewed Community Notes and their posting behavior before and after exposure. We analyzed posts related to the HPV vaccine using X’s official Community Notes dataset (January 2021–July 2024). Posts were classified as “Support,” “Oppose,” or “Neutral” using a large language model (GPT-4o, OpenAI), and changes in stance and posting frequency were evaluated. Findings show that 73% of users maintained their stance after viewing Community Notes. However, posting frequency increased sharply immediately after the note was added, especially among opposing users. This suggests that, since most Community Notes support vaccination, opposing users may have actively responded by posting critical responses. This study contributes by examining viewer behavior, not just post authors. However, limitations include GPT-4o’s classification accuracy and the restricted scope of topics and users covered in this study. Future research should improve the evaluation of Community Notes by verifying users who viewed Community Notes and enhancing stance classification through better prompts and model comparisons. Additionally, expanding the analysis beyond the HPV vaccine will help assess the broader applicability of the findings. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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13 pages, 1923 KB

Open AccessProceeding Paper

Leveraging LSTM Neural Networks for Advanced Harassment Detection: Insights into Insults and Defamation in the U-Tapis Module

by Gerald Imanuel Wijaya and Marlinda Vasty Overbeek

Eng. Proc. 2025, 107(1), 11; https://doi.org/10.3390/engproc2025107011 - 22 Aug 2025

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Abstract

The prevalence of online harassment necessitates sophisticated automated systems that can accurately classify offensive content. In this work, we present a text classification system based on Long Short-Term Memory (LSTM) networks to categorize text into Neutral, Insult, and Defamation classes, thereby providing a [...] Read more.

The prevalence of online harassment necessitates sophisticated automated systems that can accurately classify offensive content. In this work, we present a text classification system based on Long Short-Term Memory (LSTM) networks to categorize text into Neutral, Insult, and Defamation classes, thereby providing a more granular understanding of abusive behavior in digital environments. The system was evaluated using two labeled datasets—150 samples generated by ChatGPT and 1000 samples from internet sources—achieving an accuracy of 85% on both. Notably, the model demonstrated strong performance in identifying Defamation, exhibiting high precision and recall. These findings underscore the effectiveness of LSTM networks in capturing complex linguistic features, highlighting their potential for improving content moderation tools and curbing online harassment. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 417 KB

Open AccessProceeding Paper

Autism Spectrum Disorder Classification in Children Using Eye-Tracking Data and Machine Learning

by Nikolaos Kaloforidis, Konstantinos-Filippos Kollias, Panagiotis Radoglou-Grammatikis, Panagiotis Sarigiannidis and George F. Fragulis

Eng. Proc. 2025, 107(1), 12; https://doi.org/10.3390/engproc2025107012 - 22 Aug 2025

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Abstract

Early Autism Spectrum Disorder (ASD) detection is important for early intervention. This study investigates the potential of eye-tracking (ET) data combined with machine learning (ML) models to classify ASD and Typically Developed (TD) children. Using a publicly available dataset, five ML models were [...] Read more.

Early Autism Spectrum Disorder (ASD) detection is important for early intervention. This study investigates the potential of eye-tracking (ET) data combined with machine learning (ML) models to classify ASD and Typically Developed (TD) children. Using a publicly available dataset, five ML models were evaluated: Support Vector Machine (SVM), Random Forest, Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Random Forest improved with Convolutional Filters (ConvRF). The models were trained and tested using a set of evaluation metrics, including accuracy, precision, recall, F1-score, and ROC Area Under the Curve (AUC). Among these, the ConvRF model attained superior performance, achieving a recall of 90% and an AUC of 88%, indicating its robustness in identifying ASD children. These results highlight the model’s effectiveness in ensuring high sensitivity, which is critical for early ASD detection. This study shows the promise of combining ML and eye-tracking technology as accessible non-invasive tools for enhancing early ASD detection, resulting in timely and personalized interventions. Limitations and recommendations for future research are also included. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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10 pages, 2374 KB

Open AccessProceeding Paper

Design and Development of RDI Monitoring System of RSU’s Funded Research Projects

by Preexcy B. Tupas, Nova Marie F. Rosas, Ana G. Gervacio and Garry Vanz V. Blancia

Eng. Proc. 2025, 107(1), 13; https://doi.org/10.3390/engproc2025107013 - 22 Aug 2025

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Abstract

This paper presents the design, development, and evaluation of the REDI Monitoring System, a web-based platform aimed at enhancing the management and monitoring of funded research projects at Romblon State University (RSU). The system provides streamlined functionalities for proposal creation, submission, collaborator management, [...] Read more.

This paper presents the design, development, and evaluation of the REDI Monitoring System, a web-based platform aimed at enhancing the management and monitoring of funded research projects at Romblon State University (RSU). The system provides streamlined functionalities for proposal creation, submission, collaborator management, and administrative oversight, tailored to the needs of both students and faculty members. The development process adhered to established software engineering standards to ensure robustness and usability. A comprehensive testing phase was conducted with 50 participants, including students and faculty, following the ISO/IEC/IEEE 29119 software testing framework. Results demonstrated high user satisfaction, with over 90% of participants finding the system user-friendly and reliable. Minor areas for improvement were identified in notification delivery and interface responsiveness for faculty users. The REDI Monitoring System presents an effective and efficient solution that supports RSU’s research administration processes, fostering greater collaboration and transparency in funded research activities. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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7 pages, 292 KB

Open AccessProceeding Paper

User Acceptance of IBON (Image-Based Ornithological Identification) Monitoring in a Mobile Platform: A TAM-Based Study

by Preexcy B. Tupas, Juniel G. Lucidos, Alexander A. Hernandez and Rossian V. Perea

Eng. Proc. 2025, 107(1), 14; https://doi.org/10.3390/engproc2025107014 - 22 Aug 2025

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Abstract

This study investigates user acceptance of the IBON Monitoring system, a mobile app that uses image recognition to identify bird species. Using the Technology Acceptance Model (TAM), it surveyed 100 faculty and students at Romblon State University to assess factors like perceived usefulness, [...] Read more.

This study investigates user acceptance of the IBON Monitoring system, a mobile app that uses image recognition to identify bird species. Using the Technology Acceptance Model (TAM), it surveyed 100 faculty and students at Romblon State University to assess factors like perceived usefulness, ease of use, computer literacy, and self-efficacy. Results showed that usefulness and ease of use significantly influence user attitudes and intentions. The findings suggest actionable recommendations for improving IBON system adoption, including training programs to enhance computer literacy and self-efficacy and strategies to demonstrate the system’s relevance to user needs. Future research should explore additional external factors, such as cultural influences and user experience design, and conduct longitudinal studies to assess sustained use and impact on biodiversity monitoring outcomes. This study underscores the importance of fostering user acceptance to maximize the potential of innovative technologies like IBON Monitoring in advancing biodiversity conservation efforts. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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13 pages, 558 KB

Open AccessProceeding Paper

A Proposal of “Echo Read” as an Interactive AI Reading System to Support Rereading

by Yulana Watanabe, Yuuha Tokomoto and Eiichi Yubune

Eng. Proc. 2025, 107(1), 15; https://doi.org/10.3390/engproc2025107015 - 25 Aug 2025

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Abstract

This study investigates how rereading and different annotation styles affect the reading experience, proposing a system called Echo Read. It compares traditional literary annotations with SNS-style ones and enables mode switching among plain text, traditional, and SNS annotations. A theoretical framework views [...] Read more.

This study investigates how rereading and different annotation styles affect the reading experience, proposing a system called Echo Read. It compares traditional literary annotations with SNS-style ones and enables mode switching among plain text, traditional, and SNS annotations. A theoretical framework views reading as a multidimensional process across temporal and spatial axes. An experiment with participants reading under three annotation modes collects data via surveys, logs, and interviews. This study aims to show how annotations enhance comprehension, reflection, and social engagement, contributing to new understandings of digital reading and practical applications in education and culture. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 922 KB

Open AccessProceeding Paper

FairCXRnet: A Multi-Task Learning Model for Domain Adaptation in Chest X-Ray Classification for Low Resource Settings

by Aminu Musa, Rajesh Prasad, Mohammed Hassan, Mohamed Hamada and Saratu Yusuf Ilu

Eng. Proc. 2025, 107(1), 16; https://doi.org/10.3390/engproc2025107016 - 22 Aug 2025

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Abstract

Medical imaging analysis plays a pivotal role in modern healthcare, with physicians relying heavily on radiologists for disease diagnosis. However, many hospitals face a shortage of radiologists, leading to long queues at radiology centers and delays in diagnosis. Advances in artificial intelligence (AI) [...] Read more.

Medical imaging analysis plays a pivotal role in modern healthcare, with physicians relying heavily on radiologists for disease diagnosis. However, many hospitals face a shortage of radiologists, leading to long queues at radiology centers and delays in diagnosis. Advances in artificial intelligence (AI) have made it possible for AI models to analyze medical images and provide insights similar to those of radiologists. Despite their successes, these models face significant challenges that hinder widespread adoption. One major issue is the inability of AI models to generalize data from new populations, as performance tends to degrade when evaluated on datasets with different or shifted distributions, a problem known as domain shift. Additionally, the large size of these models requires substantial computational resources for training and deployment. In this study, we address these challenges by investigating domain shifts using ChestXray-14 and a Nigerian chest X-ray dataset. We propose a multi-task learning (MTL) approach that jointly trains the model on both datasets for two tasks, classification and segmentation, to minimize the domain gap. Furthermore, we replace traditional convolutional layers in the backbone model (Densenet-201) architecture with depthwise separable convolutions, reducing the model’s number of parameters and computational requirements. Our proposed model demonstrated remarkable improvements in both accuracy and AUC, achieving 93% accuracy and 96% AUC when tested across both datasets, significantly outperforming traditional transfer learning methods. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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10 pages, 769 KB

Open AccessProceeding Paper

Smart Irrigation Based on Soil Moisture Sensors with Photovoltaic Energy for Efficient Agricultural Water Management: A Systematic Literature Review

by Abdul Rasyid Sidik, Akbar Tawakal, Gumilar Surya Sumirat and Panji Narputro

Eng. Proc. 2025, 107(1), 17; https://doi.org/10.3390/engproc2025107017 - 25 Aug 2025

Cited by 1 | Viewed by 1964

Abstract

A smart irrigation system based on soil moisture sensors supported by photovoltaic energy is an innovation to address water use efficiency in the agricultural sector, especially in remote areas. This technology utilizes photovoltaic panels as a renewable energy source to operate water pumps, [...] Read more.

A smart irrigation system based on soil moisture sensors supported by photovoltaic energy is an innovation to address water use efficiency in the agricultural sector, especially in remote areas. This technology utilizes photovoltaic panels as a renewable energy source to operate water pumps, while soil moisture sensors provide real-time data that is used to automatically manage irrigation according to plant needs. This technology not only increases the efficiency of water and energy use but also supports environmental conservation by reducing dependence on fossil fuels. This research was conducted using a Systematic Literature Review (SLR) approach guided by the PRISMA framework to analyze trends, benefits, and challenges in implementing this technology. The analysis results show that this system offers various advantages, including energy efficiency, reduced carbon emissions, and ease of management through the integration of Internet of Things (IoT) technology. Several challenges remain, such as high initial investment costs, limited network access, and obstacles. Technical matters related to installation and maintenance. Various solutions have been proposed, including providing subsidies for small farmers, implementing radiofrequency modules, and using modular designs to simplify implementation. This study contributes to the development of a conceptual framework that can be adapted to various geographic and socio-economic conditions. Potential further developments include the integration of artificial intelligence and additional sensors to increase efficiency and support the sustainability of the agricultural sector globally. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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6 pages, 298 KB

Open AccessProceeding Paper

An ML Framework for the Early Detection and Prediction of Hypertension: Enhancing Diagnostic Accuracy

by Muhammad Areeb, Attique Ur Rehman and Alun Sujjada

Eng. Proc. 2025, 107(1), 18; https://doi.org/10.3390/engproc2025107018 - 25 Aug 2025

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Abstract

A major worldwide health problem, hypertension can result in serious consequences such as stroke, renal failure, and cardiovascular illnesses if it is not identified and treated promptly. Reducing death rates and facilitating prompt therapies need the early identification of hypertension. This research examines [...] Read more.

A major worldwide health problem, hypertension can result in serious consequences such as stroke, renal failure, and cardiovascular illnesses if it is not identified and treated promptly. Reducing death rates and facilitating prompt therapies need the early identification of hypertension. This research examines if there are ways ML could enhance early identification of hypertension. Therefore, hypertension is still considered a global public health problem, and one of the most important preventive goals is its timely and accurate diagnosis. Leveraging a 99.92% accuracy rate, the present study therefore proposes a novel ML framework that significantly dwarfs the currently documented best accuracy of 99.5%. This achievement of correctly identifying the essentiality of hypertension in establishing our recommended paradigm highlights the robustness and trustworthiness of the proposed actions to ensure timely treatment and enhance patients’ quality of life the largest amount. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 1332 KB

Open AccessProceeding Paper

U-Tapis: A Hybrid Approach to Melting Word Error Detection and Correction with Damerau-Levenshtein Distance and RoBERTa

by Prudence Tendy and Marlinda Vasty Overbeek

Eng. Proc. 2025, 107(1), 19; https://doi.org/10.3390/engproc2025107019 - 25 Aug 2025

Viewed by 213

Abstract

In the current digital era, the demand for rapid news delivery increases the risk of linguistic errors, including inaccuracies in the usage of melting words. This research introduces the U-Tapis application, a platform designed to detect and correct such errors using the Damerau-Levenshtein [...] Read more.

In the current digital era, the demand for rapid news delivery increases the risk of linguistic errors, including inaccuracies in the usage of melting words. This research introduces the U-Tapis application, a platform designed to detect and correct such errors using the Damerau-Levenshtein Distance algorithm and the RoBERTa model. The system achieved an average recommendation accuracy of 92.84%, with performance ranging from 91.30% to 95.45% across 3000 news articles. Despite its effectiveness, the system faces limitations, such as the static nature of its dataset, which does not update dynamically with new entries in the Indonesian Language Dictionary, and its tendency to flag all words with “me-” and “pe-” prefixes, regardless of context. These challenges highlight opportunities for future enhancements to improve the platform’s adaptability and precision. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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11 pages, 527 KB

Open AccessProceeding Paper

Communitech: Empowering Barangay Officials in Rural Areas Through Enhanced Computer Skills

by Joan F. Ferranco, Ana G. Gervacio and Charevel F. Ferranco

Eng. Proc. 2025, 107(1), 20; https://doi.org/10.3390/engproc2025107020 - 25 Aug 2025

Viewed by 431

Abstract

As communities rely on technology for information dissemination, Barangays must keep pace. Training Barangay officials, a key source of local information, enhances their ability to promote and communicate efficiently. The project offered capability training in Records Management, Basic Computer Maintenance, and Multimedia Technology. [...] Read more.

As communities rely on technology for information dissemination, Barangays must keep pace. Training Barangay officials, a key source of local information, enhances their ability to promote and communicate efficiently. The project offered capability training in Records Management, Basic Computer Maintenance, and Multimedia Technology. Objectives included the following: providing knowledge on ICT, streamlining document processes, introducing multimedia tools, conducting hands-on training, and evaluating outputs. A framework was adapted to guide the project, from needs assessment to implementation. The training averaged a 4.64 rating, indicating success. This model supports digital literacy for marginalized sectors and can be replicated in other municipalities for greater impact. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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11 pages, 463 KB

Open AccessProceeding Paper

A Deep Convolutional Neural Network-Based Model for Aspect and Polarity Classification in Hausa Movie Reviews

by Umar Ibrahim, Abubakar Yakubu Zandam, Fatima Muhammad Adam, Aminu Musa, Mohamed Hassan, Mohamed Hamada and Muhammad Shamsu Usman

Eng. Proc. 2025, 107(1), 21; https://doi.org/10.3390/engproc2025107021 - 26 Aug 2025

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Abstract

Aspect-based sentiment analysis (ABSA) plays a pivotal role in understanding the nuances of sentiment expressed in text, particularly in the context of diverse languages and cultures. This paper presents a novel deep convolutional neural network (CNN)-based model tailored for aspect and polarity classification [...] Read more.

Aspect-based sentiment analysis (ABSA) plays a pivotal role in understanding the nuances of sentiment expressed in text, particularly in the context of diverse languages and cultures. This paper presents a novel deep convolutional neural network (CNN)-based model tailored for aspect and polarity classification in Hausa movie reviews, as Hausa is an underrepresented language with limited resources and presence in sentiment analysis research. One of the primary implications of this work is the creation of a comprehensive Hausa ABSA dataset, which addresses a significant gap in the availability of resources for sentiment analysis in underrepresented languages. This dataset fosters a more inclusive sentiment analysis landscape and advances research in languages with limited resources. The collected dataset was first preprocessed using Sci-Kit Learn to perform TF-IDF transformation for extracting feature word vector weights. Aspect-level feature ontology words within the analyzed text were derived, and the sentiment of the reviewed texts was manually annotated. The proposed model combines convolutional neural networks (CNNs) with an attention mechanism to aid aspect word prediction. The model utilizes sentences from the corpus and feature words as vector inputs to enhance prediction accuracy. The proposed model leverages the advantages of the convolutional and attention layers to extract contextual information and sentiment polarities from Hausa movie reviews. The performance demonstrates the applicability of such models to underrepresented languages. With 91% accuracy on aspect term extraction and 92% on sentiment polarity classification, the model excels in aspect identification and sentiment analysis, offering insights into specific aspects of interest and their associated sentiments. The proposed model outperformed traditional machine models in both aspect word and polarity prediction. Through the creation of the Hausa ABSA dataset and the development of an effective model, this study makes significant advances in ABSA research. It has wide-ranging implications for the sentiment analysis field in the context of underrepresented languages. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 452 KB

Open AccessProceeding Paper

Integrating Serious Games in Primary Education: A Comprehensive Analysis

by Argyro Sachinidou, Ioannis Antoniadis and George F. Fragulis

Eng. Proc. 2025, 107(1), 22; https://doi.org/10.3390/engproc2025107022 - 26 Aug 2025

Viewed by 800

Abstract

The significant development of technology has greatly influenced crucial sectors of society, including health, economy, public health, and business. Technological tools have become essential in daily life, impacting the educational process across all age groups. Previous research has demonstrated the pervasive integration of [...] Read more.

The significant development of technology has greatly influenced crucial sectors of society, including health, economy, public health, and business. Technological tools have become essential in daily life, impacting the educational process across all age groups. Previous research has demonstrated the pervasive integration of technology into everyday activities, emphasizing the compelling attraction that screens and mobile devices provide, particularly among younger generations. However, earlier studies have often overlooked the detailed impact and practical applications of these technologies within the educational sector, particularly through computer games. This study employs a comprehensive analysis of scientific articles available on the internet, examining global research on the use of computer games in education. The research methods include a systematic review of publications, focusing on primary education while also considering other educational levels to provide a holistic view. The analytical approach highlights the practices employed during the implementation of educational computer games and their effects on the learning process. The major findings reveal that educational computer games have become a highly popular pedagogical method, effectively capturing the interest of both students and educators. The study underscores the growing demand for these educational tools and the promise of continuous improvements and additions to this type of teaching. The results suggest that integrating computer games into education not only enhances engagement but also signifies a progressive shift in teaching methodologies, paving the way for innovative educational practices. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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11 pages, 818 KB

Open AccessProceeding Paper

Analysis of the Role of Temperature and Current Density in Hydrogen Production via Water Electrolysis: A Systematic Literature Review

by Panji Narputro, Prastiyo Effendi, Iqbal Maulana Akbar and Saefur Rahman

Eng. Proc. 2025, 107(1), 23; https://doi.org/10.3390/engproc2025107023 - 26 Aug 2025

Viewed by 1628

Abstract

The production of hydrogen through water electrolysis has emerged as a promising alternative to decarbonizing the energy sector, especially when integrated with renewable energy sources. Among the key operational parameters that affect electrolysis performance, temperature and current density play a critical role in [...] Read more.

The production of hydrogen through water electrolysis has emerged as a promising alternative to decarbonizing the energy sector, especially when integrated with renewable energy sources. Among the key operational parameters that affect electrolysis performance, temperature and current density play a critical role in determining the energy efficiency, hydrogen yield and durability of the system. The study presents a Systematic Literature Review (SLR) that includes peer-reviewed publications from 2018 to 2025, focusing on the effects of temperature and current density across a variety of electrolysis technologies, including alkaline (AEL), proton exchange membrane (PEMEL), and solid oxide electrolysis cells (SOEC). A total of seven high-quality studies were selected following the PRISMA 2020 framework. The results show that high temperatures improve electrochemical kinetics and reduce excess potential, especially in PEM and SOEC systems, but can also accelerate component degradation. Higher current densities increase hydrogen production rates but lead to lower Faradaic efficiency and increased material stress. The optimal operating range was identified for each type of electrolysis, with PEMEL performing best at 60–80 °C and 500–1000 mA/cm², and SOEC at >750 °C. In addition, system-level studies emphasize the importance of integrating hydrogen production with flexible generation and storage infrastructure. The review highlights several research gaps, including the need for dynamic modeling, multi-parameter control strategies, and techno-economic assessments. These findings provide a basic understanding for optimizing hydrogen electrolysis systems in low-carbon energy architectures. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 1259 KB

Open AccessProceeding Paper

Anomaly Detection in Geothermal Steam Production Time Series Using Singular Spectrum Analysis

by Keiya Azuma and Yasuhiro Hashimoto

Eng. Proc. 2025, 107(1), 24; https://doi.org/10.3390/engproc2025107024 - 25 Aug 2025

Viewed by 304

Abstract

Geothermal power generation offers a high availability factor and independence from weather conditions, yet steam production in geothermal wells often declines over time due to factors such as pressure depletion and scale deposition. To enable early detection of production anomalies and optimize maintenance, [...] Read more.

Geothermal power generation offers a high availability factor and independence from weather conditions, yet steam production in geothermal wells often declines over time due to factors such as pressure depletion and scale deposition. To enable early detection of production anomalies and optimize maintenance, this paper proposes an anomaly detection framework based on Singular Spectrum Analysis (SSA). First, a Butterworth low-pass filter reduces high-frequency noise; then, SSA decomposes the time series, focusing on the largest singular value’s corresponding vectors. An anomaly score measures the deviation between current and historical singular vectors, and Non-Maximum Suppression (NMS) aggregates consecutive peaks to reduce false positives. We apply this method to 14 years of data from nine geothermal wells, comparing two threshold strategies: a unified threshold and well-specific thresholds. Results show that while a unified threshold simplifies deployment, individual thresholds can improve detection in certain wells, underscoring the impact of well characteristics and class imbalance. Our findings demonstrate that SSA-based anomaly detection, combined with NMS and threshold optimization, can effectively support maintenance decisions in geothermal power plants. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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14 pages, 3720 KB

Open AccessProceeding Paper

A Novel Data-Driven Framework for Automated Migraines Classification Using Ensemble Learning

by Muhammad Owais Butt, Azka Mir and Alun Sujjada

Eng. Proc. 2025, 107(1), 25; https://doi.org/10.3390/engproc2025107025 - 26 Aug 2025

Viewed by 344

Abstract

Migraines are recurring and highly painful headaches with multiple associated symptoms that severely affect millions of people around the world. This condition is considered quite serious from a neurologist’s perspective because it is highly debilitating. Effective treatment of migraines begins with its diagnosis [...] Read more.

Migraines are recurring and highly painful headaches with multiple associated symptoms that severely affect millions of people around the world. This condition is considered quite serious from a neurologist’s perspective because it is highly debilitating. Effective treatment of migraines begins with its diagnosis but the subjective nature of clinical evaluations along with class imbalance in patient datasets makes this very complicated. This paper attempts to tackle these issues by developing a machine-learning framework for automated migraines classification by utilizing a Kaggle dataset of 400 samples with 23 independent attributes and 1 dependent attribute representing different types of migraines. Our framework starts with a detailed cleansing of the data, which includes filtering out all missing values. Then, through the use of SMOTE (Synthetic Minority Oversampling Technique), the issue of an imbalanced dataset is tackled. This is followed by optimized feature selection through forward selection and cross-validation with Naïve Bayes. Supervised machine-learning classifiers such as Random Forest (RF), decision tree (DT), K-nearest Neighbors (KNN), and Naïve Bayes (NB) are evaluated and voted on to predict the outcome. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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9 pages, 209 KB

Open AccessProceeding Paper

AI Detection in Academia: How Indian Universities Can Safeguard Academic Integrity

by Akash Gupta, Harsh Mahaseth and Arushi Bajpai

Eng. Proc. 2025, 107(1), 26; https://doi.org/10.3390/engproc2025107026 - 26 Aug 2025

Viewed by 1746

Abstract

In recent times, the use of Artificial Intelligence (AI) technologies like ChatGPT-4o within the education sector has become an undisputed fact. AI has transformed the education sector, offering tools that enhance student research and writing. However, the use of AI raises concerns with [...] Read more.

In recent times, the use of Artificial Intelligence (AI) technologies like ChatGPT-4o within the education sector has become an undisputed fact. AI has transformed the education sector, offering tools that enhance student research and writing. However, the use of AI raises concerns with respect to academic integrity, originality, and authenticity. Indian Universities regulate traditional plagiarism with anti-plagiarism detection systems. Some Indian Universities have also subscribed to AI plagiarism detection systems, but not all of them have subscribed to AI plagiarism detection. The majority of Indian Universities are not sufficiently prepared to identify AI-generated content that is contextually relevant and original, thus bypassing these traditional checks. This study stresses the urgent need for the University Grants Commission (UGC) to introduce advanced AI detection systems across Indian universities. Unlike regular plagiarism checkers, these tools can identify unique writing patterns that suggest AI-generated content. Without such measures, universities risk students using AI to complete assignments and research dishonestly. Through this research, the authors will examine the ethical concerns surrounding AI in academia and highlight the importance of clear guidelines to ensure responsible use. Colleges and universities need proper policies to regulate AI-generated work in student submissions. This study will compare how India and other countries handle AI detection in education, elaborating on the challenges of dealing with AI-generated content. The paper will propose a structured framework for Indian universities, including the use of AI detection tools, ethical guidelines, and awareness programmes to help students use AI responsibly while maintaining academic integrity in a changing educational system. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

5 pages, 368 KB

Open AccessProceeding Paper

Literature Study of the Potential Natural Oil Extracts from Plants as Bio Lubricants Using Local Resources in Indonesia

by Agung Nugraha, Naya Achmad Lajuari, Muhammad Andi Fazar Hermawan, Lazuardi Akmal Islami and Sivakumar Nallappan Sellappan

Eng. Proc. 2025, 107(1), 27; https://doi.org/10.3390/engproc2025107027 - 27 Aug 2025

Viewed by 1148

Abstract

Lubricants are useful for reducing the negative impacts of friction. An engine that is not properly lubricated will easily wear out, make noise, and produce excessive heat. The use of conventional petroleum-based lubricants still dominates, but the sustainability of fossil resources and the [...] Read more.

Lubricants are useful for reducing the negative impacts of friction. An engine that is not properly lubricated will easily wear out, make noise, and produce excessive heat. The use of conventional petroleum-based lubricants still dominates, but the sustainability of fossil resources and the environmental impacts they have are major concerns. Therefore, the development of lubricants based on natural materials, or bio lubricants, is increasingly gaining attention. This paper aims to analyze various studies that have been conducted related to bio lubricants, especially those based on Indonesian natural resources. With the plant resources available in Indonesia, this research can be developed by utilizing the local wealth that is available, especially in abundance in Sukabumi City or Regency. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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14 pages, 685 KB

Open AccessProceeding Paper

Predictive Analysis of Voice Pathology Using Logistic Regression: Insights and Challenges

by Divya Mathews Olakkengil and Sagaya Aurelia P

Eng. Proc. 2025, 107(1), 28; https://doi.org/10.3390/engproc2025107028 - 27 Aug 2025

Viewed by 497

Abstract

Voice pathology diagnosis is essential for the timely detection and management of voice disorders, which can significantly impact an individual’s quality of life. This study employed logistic regression to evaluate the predictive power of variables that include age, severity, loudness, breathiness, pitch, roughness, [...] Read more.

Voice pathology diagnosis is essential for the timely detection and management of voice disorders, which can significantly impact an individual’s quality of life. This study employed logistic regression to evaluate the predictive power of variables that include age, severity, loudness, breathiness, pitch, roughness, strain, and gender on a binary diagnosis outcome (Yes/No). The analysis was performed on the Perceptual Voice Qualities Database (PVQD), a comprehensive dataset containing voice samples with perceptual ratings. Two widely used voice quality assessment tools, CAPE-V (Consensus Auditory-Perceptual Evaluation of Voice) and GRBAS (Grade, Roughness, Breathiness, Asthenia, Strain), were employed to annotate voice qualities, ensuring systematic and clinically relevant perceptual evaluations. The model revealed that age (odds ratio: 1.033, p < 0.001), loudness (odds ratio: 1.071, p = 0.005), and gender (male) (odds ratio: 1.904, p = 0.043) were statistically significant predictors of voice pathology. In contrast, severity and voice quality-related features like breathiness, pitch, roughness, and strain did not show statistical significance, suggesting their limited predictive contributions within this model. While the results provide valuable insights, the study underscores notable limitations of logistic regression. The model assumes a linear relationship between the independent variables and the log odds of the outcome, which restricts its ability to capture complex, non-linear patterns within the data. Additionally, logistic regression does not inherently account for interactions between predictors or feature dependencies, potentially limiting its performance in more intricate datasets. Furthermore, a fixed classification threshold (0.5) may lead to misclassification, particularly in datasets with imbalanced classes or skewed predictor distributions. These findings highlight that although logistic regression serves as a useful tool for identifying significant predictors, its results are dataset-dependent and cannot be generalized across diverse populations. Future research should validate these findings using heterogeneous datasets and employ advanced machine learning techniques to address the limitations of logistic regression. Integrating non-linear models or feature interaction analyses may enhance diagnostic accuracy, ensuring more reliable and robust voice pathology predictions. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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7 pages, 572 KB

Open AccessProceeding Paper

The Effect of UV Light in Accelerating IoT-Based Hydroponic Plant Growth

by Riyan, Isep Teddy Kurniawan, Muhammad Irsyad Fauzan and Trisiani Dewi Hendrawati

Eng. Proc. 2025, 107(1), 29; https://doi.org/10.3390/engproc2025107029 - 27 Aug 2025

Viewed by 449

Abstract

Hydroponic agriculture based on the Internet of Things (IoT) is an innovative solution to face the challenges of land limitations and climate uncertainty. This study aims to analyze the role of IoT in accelerating the growth of hydroponic plants through monitoring and automation [...] Read more.

Hydroponic agriculture based on the Internet of Things (IoT) is an innovative solution to face the challenges of land limitations and climate uncertainty. This study aims to analyze the role of IoT in accelerating the growth of hydroponic plants through monitoring and automation of the planting environment, as well as evaluating its impact on productivity, especially for the planting process in land with minimal sunlight. The system integrates sensors to monitor environmental parameters such as pH, temperature, and humidity, which are then processed in real-time to optimize nutrient delivery and irrigation. The results show that the use of IoT in hydroponic systems is able to significantly improve the quality and quantity of crop yields compared to conventional methods. However, there are several challenges in implementation, such as high initial costs, limited infrastructure in certain areas, and potential cybersecurity threats. Nonetheless, innovation and collaboration opportunities between the public and private sectors can accelerate the adoption of these technologies in sustainable agriculture. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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10 pages, 399 KB

Open AccessProceeding Paper

A Systematic Literature Study on IoT-Based Water Turbidity Monitoring: Innovation in Waste Management

by Fawwaz Muhammad, Wildan Nasrullah, Rio Alfatih and Trisiani Dewi Hendrawati

Eng. Proc. 2025, 107(1), 30; https://doi.org/10.3390/engproc2025107030 - 27 Aug 2025

Viewed by 557

Abstract

Water quality monitoring is an important step in maintaining environmental sustainability and public health. Water turbidity is one of the main parameters in assessing water quality, because a high level of turbidity can indicate pollution that is harmful to aquatic ecosystems and humans. [...] Read more.

Water quality monitoring is an important step in maintaining environmental sustainability and public health. Water turbidity is one of the main parameters in assessing water quality, because a high level of turbidity can indicate pollution that is harmful to aquatic ecosystems and humans. In the digital era, Internet of Things (IoT) technology has been applied to improve the effectiveness of real-time monitoring of water turbidity. This study aims to examine IoT-based water turbidity monitoring strategies and technologies using the Systematic Literature Review (SLR) method with the PRISMA protocol. In the process of searching for literature, this study identified 222 articles from the Scopus database, which, after going through the screening stage based on relevance, document type, and accessibility, resulted in seven main articles for further analysis. The results of the review show that the utilization of IoT sensors and wireless communication enables real-time monitoring of water turbidity, improves early detection of pollution, and improves effectiveness in water monitoring. However, challenges such as data security, sensor reliability, and communication network stability still need to be overcome to ensure the system works optimally. This study confirms that IoT can be a more efficient and sustainable solution in monitoring water turbidity. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 1258 KB

Open AccessProceeding Paper

Visualization of Rainfall Using Participatory Mobile Sensing for Crop Cultivation Support

by Yuki Inoue, Masayuki Higashino, Takao Kawamura and Mitsuru Tsubo

Eng. Proc. 2025, 107(1), 31; https://doi.org/10.3390/engproc2025107031 - 27 Aug 2025

Viewed by 158

Abstract

In drylands, farmers practicing rain-dependent agriculture sow their seeds in accordance with the onset of the rainy season. Thus, the onset of the rainy season is extremely important, but inexperienced farmers cannot determine when it begins. To support farmers in determining the optimal [...] Read more.

In drylands, farmers practicing rain-dependent agriculture sow their seeds in accordance with the onset of the rainy season. Thus, the onset of the rainy season is extremely important, but inexperienced farmers cannot determine when it begins. To support farmers in determining the optimal sowing date, we propose a method that combines GSMaP estimates with actual measurements from a mobile application to present rainfall data and evaluate its usefulness. In the proposed method, GSMaP is used to visualize estimated rainfall data, but satellite-based estimates can differ from ground-based actual measurements. If farmers own smartphones, they can use a mobile application to record actual rainfall measurements. This allows farmers to selectively incorporate both estimated and actual rainfall data into their cultivation plans. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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7 pages, 347 KB

Open AccessProceeding Paper

Stroke Prediction Using Machine Learning Algorithms

by Nayab Kanwal, Sabeen Javaid and Dhita Diana Dewi

Eng. Proc. 2025, 107(1), 32; https://doi.org/10.3390/engproc2025107032 - 27 Aug 2025

Viewed by 314

Abstract

Stroke is a major global cause of death and disability, and improving outcomes requires early prediction. Although class imbalance in datasets causes biased predictions and inferior classification accuracy, machine learning (ML) techniques have shown potential in stroke prediction. We used the Synthetic Minority [...] Read more.

Stroke is a major global cause of death and disability, and improving outcomes requires early prediction. Although class imbalance in datasets causes biased predictions and inferior classification accuracy, machine learning (ML) techniques have shown potential in stroke prediction. We used the Synthetic Minority Oversampling Technique (SMOTE) to balance datasets and lessen bias in order to address these problems. Furthermore, we suggested a method that combines a linear discriminant analysis (LDA) model for classification with an autoencoder for feature extraction. A grid search approach was used to optimize the hyperparameters of the LDA model. We used criteria like accuracy, sensitivity, specificity, AUC (area under the curve), and ROC (Receiver Operating Characteristic) to guarantee a strong evaluation. With 98.51% sensitivity, 97.56% specificity, 99.24% accuracy, and 98.00% balanced accuracy, our model demonstrated remarkable performance, indicating its potential to improve stroke prediction and aid in clinical decision-making. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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10 pages, 515 KB

Open AccessProceeding Paper

Utilization of Solar Panels in Various Applications: A Systematic Literature Review

by Robby Pahlevi David, Irwan Faisal, Viery Bagja Alamsyah and Panji Narputro

Eng. Proc. 2025, 107(1), 33; https://doi.org/10.3390/engproc2025107033 - 27 Aug 2025

Viewed by 325

Abstract

The utilization of renewable energy, particularly solar panels, has rapidly developed as a solution to reduce dependence on fossil fuels and carbon emissions. This study examines the application of solar panels across various sectors, including transportation, residential, commercial, industrial, and agricultural, using a [...] Read more.

The utilization of renewable energy, particularly solar panels, has rapidly developed as a solution to reduce dependence on fossil fuels and carbon emissions. This study examines the application of solar panels across various sectors, including transportation, residential, commercial, industrial, and agricultural, using a systematic literature review (SLR) approach. The results indicate that solar panels provide significant benefits in supporting energy sustainability, such as high efficiency in electric vehicles, carbon emission reduction in the transportation sector, and energy cost savings in commercial buildings. In the agricultural sector, solar panels are used for irrigation and crop storage. Additionally, technological advancements such as bifacial panels and integration with energy storage systems enhance efficiency and application flexibility. However, challenges such as high initial costs, location limitations, and technological efficiency remain major barriers. Through an analysis of the advantages and disadvantages of three types of solar panels (monocrystalline, polycrystalline, and thin-film), this study provides strategic guidance for selecting the most suitable technology for specific needs. The study concludes that the adoption of solar panels can be accelerated through technological innovation, cost reduction, and government policy support. With optimal utilization, solar panels have significant potential to drive the transition toward sustainable energy in the future. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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6 pages, 342 KB

Open AccessProceeding Paper

Detection of Bank Transaction Fraud Using Machine Learning

by Muhammad Sami, Azka Mir and Gina Purnama Insany

Eng. Proc. 2025, 107(1), 34; https://doi.org/10.3390/engproc2025107034 - 28 Aug 2025

Viewed by 2390

Abstract

Bank transaction fraud detection has emerged as an important area of research in the economic sector, driven by the developing sophistication of fraudulent activities and the considerable economic losses they entail. This paper reviews numerous methodologies and technologies employed in the real-time identification [...] Read more.

Bank transaction fraud detection has emerged as an important area of research in the economic sector, driven by the developing sophistication of fraudulent activities and the considerable economic losses they entail. This paper reviews numerous methodologies and technologies employed in the real-time identification and mitigation of fraudulent transactions, including traditional statistical techniques, machine learning algorithms and advanced artificial intelligence strategies. It enhances the need to combine anomaly detection structures with behavioral analytics to enhance detection accuracy while addressing challenges like data privacy, the need to balance false positives and negatives and the need for adaptive systems. By evaluating the most recent developments and case studies, this study provides a comprehensive assessment of what is happening in bank transaction fraud detection and presents future directions for enhancing safety features. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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7 pages, 312 KB

Open AccessProceeding Paper

AI as Modern Technology for Home Security Systems: A Systematic Literature Review

by Rizki Muhammad, Muhammad Syailendra Aditya Sagara, Yaunarius Molang Teluma and Fikri Arif Wicaksana

Eng. Proc. 2025, 107(1), 35; https://doi.org/10.3390/engproc2025107035 - 28 Aug 2025

Viewed by 1164

Abstract

The growing demand for innovative home security solutions has accelerated the integration of advanced technologies to enhance safety, convenience, and operational efficiency. Artificial intelligence (AI) has become a pivotal element in revolutionizing home security systems by enabling real-time threat detection, automated surveillance, and [...] Read more.

The growing demand for innovative home security solutions has accelerated the integration of advanced technologies to enhance safety, convenience, and operational efficiency. Artificial intelligence (AI) has become a pivotal element in revolutionizing home security systems by enabling real-time threat detection, automated surveillance, and intelligent decision-making. This study employs a systematic literature review (SLR) to explore recent advancements in AI-driven technologies, such as machine learning, computer vision, natural language processing, and the Internet of Things (IoT). These innovations enhance security by providing features like facial recognition, anomaly detection, voice-activated controls, and predictive analysis, delivering more accurate and responsive security solutions. Furthermore, this study addresses challenges related to data privacy, cybersecurity threats, and cost considerations while emphasizing AI’s potential to deliver scalable, efficient, and user-friendly systems. The findings demonstrate AI’s vital role in the evolution of home security technologies, paving the way for smarter and safer living environments. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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17 pages, 2153 KB

Open AccessProceeding Paper

Building-Integrated Photovoltaics: A Bibliometric Review of Key Developments and Knowledge Gaps

by Panji Narputro, Marina Artiyasa, Paikun, Utamy Sukmayu Saputri, Dio Damas Permadi, Muhammad Hidayat, Nita Kurnita Sari and Sofa Lailatul Marifah

Eng. Proc. 2025, 107(1), 36; https://doi.org/10.3390/engproc2025107036 - 27 Aug 2025

Viewed by 585

Abstract

Building-Integrated Photovoltaics (BIPV) is a transformative approach to sustainable energy, which integrates photovoltaic systems as integral elements of building structures, such as facades, roofs, and windows. This bibliometric review aims to comprehensively analyze the evolution, trends, and challenges in BIPV research by referencing [...] Read more.

Building-Integrated Photovoltaics (BIPV) is a transformative approach to sustainable energy, which integrates photovoltaic systems as integral elements of building structures, such as facades, roofs, and windows. This bibliometric review aims to comprehensively analyze the evolution, trends, and challenges in BIPV research by referencing more than 10,000 publications indexed in Scopus. Key findings highlight the growing importance of cross-disciplinary collaboration in engineering, architecture, and environmental science to improve BIPV efficiency, aesthetic integration, and economic viability. Despite substantial progress, challenges remain, including high initial costs, regulatory limitations, and the need for innovative materials and energy storage solutions. Emerging trends underscore the potential of BIPV in urban planning and sustainability initiatives, supported by increased collaboration and international adoption in regions with supportive policies. This review identifies research gaps in cost-effective production, adaptive materials, and integrated energy management solutions, which offer future pathways for BIPV innovation. This review serves as a reference for academics, practitioners, and policymakers aiming to advance the adoption of BIPV, contributing to global efforts towards energy sustainability and low-carbon urban development. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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7 pages, 182 KB

Open AccessProceeding Paper

Evaluation of AI Models for Phishing Detection Using Open Datasets

by Nur Aniyansyah, Rina Rina, Sarah Puspitasari and Adhitia Erfina

Eng. Proc. 2025, 107(1), 37; https://doi.org/10.3390/engproc2025107037 - 28 Aug 2025

Viewed by 336

Abstract

Phishing is a form of cyber-attack that aims to steal sensitive information by impersonating a trusted entity. To overcome this threat, various artificial intelligence (AI) methods have been developed to improve the effectiveness of phishing detection. This study evaluates three machine learning models, [...] Read more.

Phishing is a form of cyber-attack that aims to steal sensitive information by impersonating a trusted entity. To overcome this threat, various artificial intelligence (AI) methods have been developed to improve the effectiveness of phishing detection. This study evaluates three machine learning models, namely Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), using an open dataset containing phishing and non-phishing URLs. The research process includes data preprocessing stages such as cleaning, normalization, categorical feature encoding, feature selection, and dividing the dataset into training and test data. The trained models are then evaluated using accuracy, precision, recall, F1-score, and comparison score metrics to determine the best model in phishing classification. The evaluation results show that the Random Forest model has the best performance with higher accuracy and generalization of 98.64% compared to Decision Tree which is only 98.37% and SVM 92.67%. Decision Tree has advantages in speed and interpretability but is susceptible to overfitting. SVM shows good performance on high-dimensional datasets but is less efficient in computing time. Based on the research results, Random Forest is recommended as the most optimal model for machine learning-based phishing detection. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

5 pages, 305 KB

Open AccessProceeding Paper

Variation in Current Density of Aluminum Scrap-Based Propeller Anodization to Increase Surface Hardness

by Rifani Putri Nayla, Paulus Dara Bani, Hilmi Udzmatillah, Lazuardi Akmal Islami and Sivakumar Nallappan Sellappan

Eng. Proc. 2025, 107(1), 38; https://doi.org/10.3390/engproc2025107038 - 28 Aug 2025

Viewed by 1049

Abstract

Aluminum has the advantages of being lightweight and rust-resistant, and having high strength and durability. Aluminum scrap is a recycled material and is reused in its production process, for example, for propellers. Because it is used in conditions that require good durability, a [...] Read more.

Aluminum has the advantages of being lightweight and rust-resistant, and having high strength and durability. Aluminum scrap is a recycled material and is reused in its production process, for example, for propellers. Because it is used in conditions that require good durability, a coating that can increase the hardness and strength of aluminum is introduced. This study used the anodization method with a H₂SO₄ electrolyte medium and variations in current density of 0.03 A/cm², 0.035 A/cm², and 0.04 A/cm². The anodization time was 45 min. It was found that the hardness of the specimen increased from the initial hardness of 189 HL. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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10 pages, 204 KB

Open AccessProceeding Paper

Setting the Boundaries for the Use of AI in Indian Arbitration

by Akash Gupta, Arushi Bajpai and Samanvi Narang

Eng. Proc. 2025, 107(1), 39; https://doi.org/10.3390/engproc2025107039 - 1 Sep 2025

Viewed by 831

Abstract

If an arbitrator employs the use of AI to draft an arbitral award, or legal counsel uses AI and the data are leaked within that process, what is the legal consequence, and what will be the ethical concerns and enforceability issues? As artificial [...] Read more.

If an arbitrator employs the use of AI to draft an arbitral award, or legal counsel uses AI and the data are leaked within that process, what is the legal consequence, and what will be the ethical concerns and enforceability issues? As artificial intelligence (AI) is used in every field, it has undoubtedly been used within the legal domain. However, its use should be regulated and balanced as there is an adjudication involved between the parties to decide the rights and obligations of the parties. In recent times, AI in arbitration has revolutionized dispute resolution by enhancing efficiency, automating legal research, and expediting case management. However, its application has a different set of challenges attached to it, particularly concerning due process, algorithmic bias, evidentiary integrity, and the enforceability of AI-assisted arbitral awards. This paper critically examines these legal implications, assessing how AI aligns with Indian arbitration laws and international frameworks. It further explores regulatory safeguards, the balanced and ethical use of AI, and the evolving role of arbitrators and counsels in the era of AI. By addressing these concerns, this paper aims to provide a comprehensive analysis of AI’s impact on the legal landscape of arbitration in India. To conclude, this paper proposes an expressed provision within the Arbitration and Conciliation Act, 1996, with respect to disclosure related to the ethical use of AI. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

13 pages, 250 KB

Open AccessProceeding Paper

Incorporation of Scratch Programming and Algorithmic Resource Design in Primary Education

by Fatimazahra Ouahouda, Achtaich Khadija and Naceur Achtaich

Eng. Proc. 2025, 107(1), 40; https://doi.org/10.3390/engproc2025107040 - 1 Sep 2025

Viewed by 426

Abstract

This paper examines the integration of Scratch programming software into primary education to enrich learning experiences and promote essential programming skills. It examines gender differences in attitudes towards programming, explores game-based learning (GBL) in the Curriculum for Excellence (CfE) in Scotland, and addresses [...] Read more.

This paper examines the integration of Scratch programming software into primary education to enrich learning experiences and promote essential programming skills. It examines gender differences in attitudes towards programming, explores game-based learning (GBL) in the Curriculum for Excellence (CfE) in Scotland, and addresses the design of algorithmic resources in France. Through qualitative analysis, it assesses theeffectiveness of Scratch in teaching and learning, thereby contributing to improvements in the educational program and the programming curriculum in primary schools. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

10 pages, 217 KB

Open AccessProceeding Paper

Gamified Learning in Education: How Online Quizzes like Kahoot Transform Classroom Dynamics

by Harsh Mahaseth, Arushi Bajpai and Akash Gupta

Eng. Proc. 2025, 107(1), 41; https://doi.org/10.3390/engproc2025107041 - 1 Sep 2025

Viewed by 1204

Abstract

Online quizzes, such as Kahoot, are innovative tools reshaping modern education by boosting student engagement, enhancing memory retention, and encouraging collaboration. This study explores their role as a modern extension of the Socratic Method, highlighting their ability to combat challenges like reduced attention [...] Read more.

Online quizzes, such as Kahoot, are innovative tools reshaping modern education by boosting student engagement, enhancing memory retention, and encouraging collaboration. This study explores their role as a modern extension of the Socratic Method, highlighting their ability to combat challenges like reduced attention spans, exam anxiety, and unhealthy competition. With real-time feedback and gamified elements, quizzes make learning interactive and enjoyable, breaking the monotony of lectures. While technical limitations like time constraints and over-reliance on digital tools are noted, the findings advocate a balanced approach. Online quizzes foster inclusivity, improve learning outcomes, and prepare students for a tech-driven future. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

13 pages, 2865 KB

Open AccessProceeding Paper

Graph Neural Networks for Drug–Drug Interaction Prediction—Predicting Safe Drug Pairings with AI

by Uzair Nisar, Humaira Ashraf, NZ Jhanjhi, Farzeen Ashfaq, Uswa Ihsan and Arny Lattu

Eng. Proc. 2025, 107(1), 42; https://doi.org/10.3390/engproc2025107042 - 1 Sep 2025

Viewed by 493

Abstract

At present, polypharmacy—which is the use of several medications to treat a single case at the same time—has become a fairly common medical practice, particularly in chronic illnesses or with older patients. But this relatively ‘faster’ form of treatment brings the problem of [...] Read more.

At present, polypharmacy—which is the use of several medications to treat a single case at the same time—has become a fairly common medical practice, particularly in chronic illnesses or with older patients. But this relatively ‘faster’ form of treatment brings the problem of cumulative polypharmacy, which occurs when there is an increase in drug–drug interactions (DDIs) due to the large number of medicines taken. While the aftermath, such as the reduction in strength of medication taken or catastrophic and fatal responses to certain drugs, is clearly not worth the initial effort put into trying to ease the condition, attempting to resolve these issues requires excessive research. With these difficulties in mind, we describe our research that uses graph neural networks (GNNs) focused on DDI prediction by modeling drugs and their interactions in the form of graphs. The research is divided into two parts. In this research, the relevant literature is reviewed in order to understand how modern GNN-based algorithms can be applied for the detection of optimal drugs. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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11 pages, 1332 KB

Open AccessProceeding Paper

Comparative Analysis of Action Recognition Techniques: Exploring Two-Stream CNNs, C3D, LSTM, I3D, Attention Mechanisms, and Hybrid Models

by Arshiya, Gursharan Singh, Arun Malik and Nugraha

Eng. Proc. 2025, 107(1), 43; https://doi.org/10.3390/engproc2025107043 - 1 Sep 2025

Viewed by 285

Abstract

Action recognition actions in video are sophisticated processes that demand more and more explicitly captured spatial and temporal information. This paper gives a comparison of several advanced techniques for action recognition using the UCF101 dataset. We look at two-stream convolutional networks, 3D convolutional [...] Read more.

Action recognition actions in video are sophisticated processes that demand more and more explicitly captured spatial and temporal information. This paper gives a comparison of several advanced techniques for action recognition using the UCF101 dataset. We look at two-stream convolutional networks, 3D convolutional networks, long short-term memory networks, two-stream inflated 3D convolutional networks, attention mechanisms, and hybrid models. Their methods have been examined for each of the proposed options along with their architectures, as well as their pros and cons. The results of our experiments have revealed the performance of these approaches on the UCF101 dataset, including a focus on the tradeoffs between computational efficiency, data requirements, and recognition accuracy. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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7 pages, 1564 KB

Open AccessProceeding Paper

Explainable Artificial Intelligence for Object Detection in the Automotive Sector

by Marios Siganos, Panagiotis Radoglou-Grammatikis, Thomas Lagkas, Vasileios Argyriou, Sotirios Goudos, Konstantinos E. Psannis, Konstantinos-Filippos Kollias, George F. Fragulis and Panagiotis Sarigiannidis

Eng. Proc. 2025, 107(1), 44; https://doi.org/10.3390/engproc2025107044 - 1 Sep 2025

Viewed by 794

Abstract

In the automotive domain, object detection is pivotal for enhancing safety and autonomy through the identification of various objects of interest. However, insights into the influential image pixels in the detection process are often lacking. Recognizing these significant regions within the image not [...] Read more.

In the automotive domain, object detection is pivotal for enhancing safety and autonomy through the identification of various objects of interest. However, insights into the influential image pixels in the detection process are often lacking. Recognizing these significant regions within the image not only enriches our qualitative understanding of the model’s functionality but also empowers us to refine and optimize its performance. Employing Explainable Artificial Intelligence (XAI), we present an XAI component in this paper. This component explains the predictions made by a pre-trained object detection model for a given image by generating heatmaps that highlight the most critical regions in the image for the detected objects. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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13 pages, 3910 KB

Open AccessProceeding Paper

Grading Support System for Pear Fruit Using Edge Computing

by Ryo Ito, Shutaro Konuma and Tatsuya Yamazaki

Eng. Proc. 2025, 107(1), 45; https://doi.org/10.3390/engproc2025107045 - 1 Sep 2025

Viewed by 695

Abstract

Le Lectier pears (hereafter, Pears) are graded based on appearance, requiring farmers to inspect tens of thousands in a short time before shipment. To assist in this process, a grading support system was developed. The existing cloud-based system used mobile devices to capture [...] Read more.

Le Lectier pears (hereafter, Pears) are graded based on appearance, requiring farmers to inspect tens of thousands in a short time before shipment. To assist in this process, a grading support system was developed. The existing cloud-based system used mobile devices to capture images and analyzed them with Convolutional Neural Networks (CNNs) and texture-based algorithms. However, communication delays and algorithm inefficiencies resulted in a 30 s execution time, posing a problem. This paper proposes an edge computing-based system using Mask R-CNN for appearance deterioration detection. Processing on edge servers reduces execution time to 5–10 s, and 39 out of 51 Pears are accurately detected. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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10 pages, 951 KB

Open AccessProceeding Paper

Predicting Course Engagement with Machine Learning Techniques

by Fayez Zulfiqar Ali, Rizwan Ayazuddin and Imam Sanjaya

Eng. Proc. 2025, 107(1), 46; https://doi.org/10.3390/engproc2025107046 - 1 Sep 2025

Viewed by 107

Abstract

Online Courses are one of the most popular ways to learn, but the technology used has a vital effect on the learner. In this study, we will research the prediction of students’ course engagement. International surveys show that students have a 70% interest [...] Read more.

Online Courses are one of the most popular ways to learn, but the technology used has a vital effect on the learner. In this study, we will research the prediction of students’ course engagement. International surveys show that students have a 70% interest in joining online learning, and just 30% of students are interested in traditional learning. However, keeping students engaged is one of the most difficult tasks, since low engagement contributes to lower learning outcomes and higher dropout rates. We studied more than 15 papers of existing research, and were able to achieve a 96% accuracy rate, which is a very welcome improvement on previous results. This paper examines machine learning algorithms, including Decision Trees, Random Forest, Gradient Booster, Naive Bayes, and K-Nearest Neighbors (KNN), to efficiently predict engagement during online courses. By systematically examining existing published research studies, we identify gaps and limitations of existing methods, such as problems with variant datasets, chances of overtraining, and a lack of accessibility to real-time engagement data. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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8 pages, 1506 KB

Open AccessProceeding Paper

Fe₃O₄ Magnetic Biochar Derived from Pecan Nutshell for Arsenic Removal Performance Analysis Based on Fuzzy Decision Network

by Sasirot Khamkure, Chidentree Treesatayapun, Victoria Bustos-Terrones, Lourdes Díaz-Jimenéz, Daniella-Esperanza Pacheco-Catalán, Audberto Reyes-Rosas, Prócoro Gamero-Melo and Alejandro Zermeño-González

Eng. Proc. 2025, 107(1), 47; https://doi.org/10.3390/engproc2025107047 - 1 Sep 2025

Viewed by 605

Abstract

This study evaluates Fe₃O₄ magnetic biochar synthesized from pecan nutshells for arsenic removal. Surface modification with Fe₃O₄ significantly enhanced arsenic adsorption selectivity and efficiency compared to raw biomass (PM). Synthesis variables (precursor type, particle size, Fe/precursor ratio, [...] Read more.

This study evaluates Fe₃O₄ magnetic biochar synthesized from pecan nutshells for arsenic removal. Surface modification with Fe₃O₄ significantly enhanced arsenic adsorption selectivity and efficiency compared to raw biomass (PM). Synthesis variables (precursor type, particle size, Fe/precursor ratio, N₂) and adsorption conditions (such as concentration, pH, agitation) were investigated. The modified biochar achieved >90% arsenic removal efficiency under various conditions, demonstrating the modification’s critical role. A fuzzy decision network was employed to analyze experimental results and identify optimal conditions for maximizing performance. This approach effectively leverages knowledge for scenario-specific optimization, offering a sustainable strategy for advanced water treatment materials. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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6 pages, 736 KB

Open AccessProceeding Paper

Analyzing and Predicting Alcohol or Non-Alcoholic Cocktails

by Hifza Khan, Attique Ur Rehman and Anggun Fergina

Eng. Proc. 2025, 107(1), 48; https://doi.org/10.3390/engproc2025107048 - 2 Sep 2025

Viewed by 386

Abstract

Using a structured dataset, this study investigates the use of machine learning algorithms to analyze and forecast several properties of cocktails. Cocktails’ names, classifications, ingredients, alcoholic contents, glass types, and preparation guidelines are all included in the dataset. Based on the components, we [...] Read more.

Using a structured dataset, this study investigates the use of machine learning algorithms to analyze and forecast several properties of cocktails. Cocktails’ names, classifications, ingredients, alcoholic contents, glass types, and preparation guidelines are all included in the dataset. Based on the components, we created algorithms to categorize cocktails as either alcoholic or nonalcoholic, forecast their category, and suggest different kinds of glasses. The results give useful tools for customization in the beverage business, as well as information about cocktail trends. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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18 pages, 1153 KB

Open AccessProceeding Paper

Improved YOLOv5 Lane Line Real Time Segmentation System Integrating Seg Head Network

by Qu Feilong, Navid Ali Khan, N. Z. Jhanjhi, Farzeen Ashfaq and Trisiani Dewi Hendrawati

Eng. Proc. 2025, 107(1), 49; https://doi.org/10.3390/engproc2025107049 - 2 Sep 2025

Viewed by 311

Abstract

With the rise in motor vehicles, driving safety is a major concern, and autonomous driving technology plays a key role in enhancing safety. Vision-based lane departure warning systems are essential for accurate navigation, focusing on lane line detection. This paper reviews the development [...] Read more.

With the rise in motor vehicles, driving safety is a major concern, and autonomous driving technology plays a key role in enhancing safety. Vision-based lane departure warning systems are essential for accurate navigation, focusing on lane line detection. This paper reviews the development of such systems and highlights the limitations of traditional image processing. To improve lane line detection, a dataset from Roboflow Universe will be used, incorporating techniques like priority pixels, least squares fitting for positioning, and a Kalman filter for tracking. YOLOv5 will be enhanced with a di-versified branch block (DBB) for better multi-scale feature extraction and an improved segmentation head inspired by YOLACT (You Only Look At CoefficienTs) for precise lane line segmentation. A multi-scale feature fusion mechanism with self-attention will be introduced to improve robustness. Experiments will demonstrate that the improved YOLOv5 outperforms other models in accuracy, recall, and mAP@0.5. Future work will focus on optimizing the model structure and enhancing the fusion mechanism for better performance. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 2057 KB

Open AccessProceeding Paper

Research Trends and Gaps in Road Infrastructure Impacted by Seawater: A Combined Systematic Literature and Bibliometric Review

by Paikun, Isfa Hani, Asep Ramdan and Zidan Muhamad Ramdhani

Eng. Proc. 2025, 107(1), 50; https://doi.org/10.3390/engproc2025107050 - 2 Sep 2025

Viewed by 235

Abstract

Seawater impact poses increasing challenges to coastal road infrastructure, creating urgent needs for a comprehensive understanding of current research trends and knowledge gaps to enhance infrastructure resilience and sustainability. This study employs a combined systematic literature review (SLR) and bibliometric analysis using PRISMA [...] Read more.

Seawater impact poses increasing challenges to coastal road infrastructure, creating urgent needs for a comprehensive understanding of current research trends and knowledge gaps to enhance infrastructure resilience and sustainability. This study employs a combined systematic literature review (SLR) and bibliometric analysis using PRISMA methodology to examine seawater-impacted road infrastructure research from 1952 to 2025. An initial dataset of 185 articles from 150 sources was filtered to 47 articles for detailed analysis, covering research by 481 authors with a 0.95% annual growth rate. Bibliometric analysis revealed significant geographic disparities, with only 13.51% of international collaborations. The United States, China, and Japan emerged as leading contributors, while Norway demonstrated the highest impact with 39.00 citations per article. Eight critical themes were identified in pavement management and infrastructure resilience, showing a shift toward technology-based solutions, including real-time monitoring technologies, sustainable materials, and adaptive management strategies. Despite growing emphasis on technological solutions, significant research gaps persist in understanding road structure–ecosystem interactions and developing comprehensive long-term monitoring methods. The study indicates an urgent need for increased international collaboration and interdisciplinary approaches combining civil engineering with environmental science to effectively address coastal road infrastructure challenges and enhance global sustainability. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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13 pages, 2093 KB

Open AccessProceeding Paper

Multi-Objective Optimization of Micromachining Parameters for Titanium Alloy Ti-3Al-2.5V Using Grey Relational Analysis

by Sivakumar Nallappan Sellappan, Manivel Chinnappandi, Pradeep Kumar Jeyaraj, Senthil Kumar Shanmugam P. Seethalakshmi, Zaid Sulaiman and Abd Rahman Abdul RahimSulaiman

Eng. Proc. 2025, 107(1), 51; https://doi.org/10.3390/engproc2025107051 - 3 Sep 2025

Viewed by 339

Abstract

This research investigates the multi-objective optimization of micro-milling processes for the titanium alloy Ti-3Al-2.5V (grade 9) through the application of grey relational analysis. The incorporation of nanometer-sized particles in hybrid machining lubricants plays a crucial role in improving heat transfer during machining. The [...] Read more.

This research investigates the multi-objective optimization of micro-milling processes for the titanium alloy Ti-3Al-2.5V (grade 9) through the application of grey relational analysis. The incorporation of nanometer-sized particles in hybrid machining lubricants plays a crucial role in improving heat transfer during machining. The approach aims to increase the efficiency and effectiveness of micro-milling by addressing various performance metrics simultaneously, leading to better machining results for this titanium alloy. Additionally, the integration of nanoparticles into the machining lubricant significantly improves the lubrication properties, reducing friction during the machining process. The study analyzed four machining parameters: machining speed, rate of feed, axial depth of cut, and the weight percentage concentration of hybrid machining lubricants Multi-wall Carbon Nano Tube and Alumina Oxide (MWCNT and Al₂O₃). The machining nanolubricant was formulated by adding 1% and 2% volume concentrations of MWCNT and Al₂O₃ nanoparticles to the industrial machining fluid. In this machining context, the friction between the machining tool and the Ti-3Al-2.5V work piece is a vital factor influencing the output quality. The results demonstrate that the chosen machining parameters and machining lubricants have a direct impact on the coefficient of friction and surface roughness. The study concludes that utilizing machining nanolubrication for machining Ti-3Al-2.5V (grade 9) significantly enhances the quality compared with traditional machining lubricants. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 2484 KB

Open AccessProceeding Paper

A Comparative Evaluation of CNN and Transfer Learning Models for Multi-Class Skin Disease Classification

by Ivana Lucia Kharisma, Rifki Arief Munandar, D Ihsan Maulana, Mochamad Naufal Rizky and Kamdan

Eng. Proc. 2025, 107(1), 52; https://doi.org/10.3390/engproc2025107052 - 29 Aug 2025

Viewed by 184

Abstract

The automated classification of skin diseases has the potential to significantly enhance patient outcomes through early and targeted treatment. In this study, we compared a custom convolutional neural network (CNN) with three transfer learning architectures—ResNet50, DenseNet201, and Inception—for classifying nine different dermatological conditions. [...] Read more.

The automated classification of skin diseases has the potential to significantly enhance patient outcomes through early and targeted treatment. In this study, we compared a custom convolutional neural network (CNN) with three transfer learning architectures—ResNet50, DenseNet201, and Inception—for classifying nine different dermatological conditions. The models were trained on the publicly available FYP Skin Disease Dataset, which consists of 4500 images of both prevalent and serious skin diseases including melanoma, basal cell carcinoma, and squamous cell carcinoma. We conducted extensive performance analysis using accuracy, loss curves, confusion matrices, and multi-class ROC analysis. Our findings indicate that the custom CNN achieved 100% validation accuracy, followed by DenseNet201 at 99% and Inception at 98%, with ResNet50 lagging at 44%. These results highlight the potential of both custom architectures and transfer learning models in dermatological image analysis, which have significant implications for researchers and clinicians seeking viable automated solutions for skin disease diagnosis. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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13 pages, 1757 KB

Open AccessProceeding Paper

Research Trends and Gaps Relevant to the Safety and Balance of Structures Affected by Earthquakes and Floods: A Combined Literature Review and Systematic Bibliometrix Analysis

by Paikun, Andika Putra Pribad, Villiawanti Lestari and Maulana Yusuf

Eng. Proc. 2025, 107(1), 53; https://doi.org/10.3390/engproc2025107053 - 3 Sep 2025

Viewed by 783

Abstract

This study examines research trends and identifies key gaps relevant to the field of structural safety and resilience; additionally, a systematic literature review (SLR) guided by the PRISMA methodology was conducted, analyzing 4188 documents ranging from 1975 to 2025. The research revealed key [...] Read more.

This study examines research trends and identifies key gaps relevant to the field of structural safety and resilience; additionally, a systematic literature review (SLR) guided by the PRISMA methodology was conducted, analyzing 4188 documents ranging from 1975 to 2025. The research revealed key trends, including a focus on various aspects of the structural stability and resilience of buildings affected by earthquakes through analysis of various innovative methods and materials. The present study encompasses work describing the use of steel–wood composite columns to improve building stability, assessment of the impact of wood accumulation on bridges during floods, and the effect of debris flow on the stability of check dams. In addition, this study also evaluates the seismic performance of school buildings in Mexico, a method of diagnosing cracks in concrete dams, and the application of recycled materials from old tires for seismic disaster mitigation. Acoustic emission monitoring methods in medieval towers and the design of seismic isolation systems with variable damping are also discussed. Bibliometric analysis highlighted increased collaboration and a thematic shift towards green and data-driven approaches. However, significant gaps were identified. The findings explain that the use of innovative materials and methods can improve the stability and resistance of building structures with respect to dynamic loads, such as those associated with earthquakes and floods. The findings provide guidance for the design and maintenance of safer and more sustainable infrastructure in the future. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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9 pages, 235 KB

Open AccessProceeding Paper

Use of Powtoon as a Technology-Based Creative Learning Medium: A Systematic Literature Review

by Aneu Nurjanah, Dewi Susilawati, Jihan Munawafi Yusup and Agus Hendriyanto

Eng. Proc. 2025, 107(1), 54; https://doi.org/10.3390/engproc2025107054 - 3 Sep 2025

Viewed by 701

Abstract

The integration of digital technology into education has significantly shifted traditional teaching methods toward more interactive and student-centered learning. This literature review investigates the use of Powtoon, a web-based animation platform, as a creative learning medium in elementary thematic education. The study aims [...] Read more.

The integration of digital technology into education has significantly shifted traditional teaching methods toward more interactive and student-centered learning. This literature review investigates the use of Powtoon, a web-based animation platform, as a creative learning medium in elementary thematic education. The study aims to explore how Powtoon enhances student motivation, engagement, and academic outcomes through interactive visuals and storytelling. A review of previous studies reveals that Powtoon is effective across various subjects, including science, mathematics, language, and social studies, improving student focus, knowledge retention, and learning enjoyment. The research method involves analyzing empirical studies that report the educational impact of Powtoon in classroom settings. Results show that Powtoon promotes active learning, supports the development of 21st-century skills, and bridges the gap between available technology and its implementation in elementary schools, where traditional teaching still prevails. The novelty of this review lies in its focus on Powtoon’s role in cross-disciplinary thematic instruction, offering new insights beyond subject-specific usage. The study concludes that Powtoon holds strong potential as a pedagogical tool and recommends its broader adoption to foster creative, engaging, and technology-integrated learning environments in elementary education. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

13 pages, 1297 KB

Open AccessProceeding Paper

Future Planning Based on Student Movement Linked with Their Wi-Fi Signals

by Qi Hao, N. Z. Jhanjhi, Sayan Kumar Ray, Farzeen Ashfaq and Marina Artiyasa

Eng. Proc. 2025, 107(1), 55; https://doi.org/10.3390/engproc2025107055 - 28 Aug 2025

Viewed by 126

Abstract

There is large scale data collected from the various Wi-Fi networks on modern university campuses which contribute to observing student behavioral patterns. This paper explores the use of Wi-Fi connection information and internet browsing habits to forecast student dining preferences, improving data-driven models [...] Read more.

There is large scale data collected from the various Wi-Fi networks on modern university campuses which contribute to observing student behavioral patterns. This paper explores the use of Wi-Fi connection information and internet browsing habits to forecast student dining preferences, improving data-driven models for campus eating service optimizations. This study combines spatial–temporal features with browsing behavior analysis and employs advanced machine learning techniques to develop a multi-modal learning framework. Moreover, when Chinese consumers go out to eat, the analysis of anonymized Wi-Fi data also reveals considerable relationships among digital footprints and dining choices using a predictive model that can reach an accuracy level between 84 and 88%. The discoveries assist in the advancement of educational data mining and are beneficial for the real-world optimization of campus services, all under strong privacy protection using an end-to-end comprehensive data protection framework. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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10 pages, 1905 KB

Open AccessProceeding Paper

Backpropagation Neural Network Algorithm for Optimizing Network Bandwidth Allocation Based on User Access Patterns

by Akmal Nuur Fauzan, Mohammad Sahal Assahari, Abdul Rahman Jainun and Somantri

Eng. Proc. 2025, 107(1), 56; https://doi.org/10.3390/engproc2025107056 - 3 Sep 2025

Viewed by 234

Abstract

In the era of rapid digital transformation, efficient network bandwidth allocation is vital for ensuring high-quality service and seamless network operations, particularly in environments with dynamic traffic patterns. This study proposes a novel approach to optimize bandwidth allocation using a Backpropagation Neural Network [...] Read more.

In the era of rapid digital transformation, efficient network bandwidth allocation is vital for ensuring high-quality service and seamless network operations, particularly in environments with dynamic traffic patterns. This study proposes a novel approach to optimize bandwidth allocation using a Backpropagation Neural Network (BPNN) algorithm. The research utilizes a dataset sourced from Kaggle that has been modified to focus on prioritization during resource allocation and employs a preprocessing pipeline for consistency across network parameters. The BPNN model is trained with normalized data and evaluated using metrics such as Mean Squared Error (MSE) and R² score. The results, with an MSE of 0.1637 and an R² score of 0.9920, demonstrate high accuracy and minimal error in predicting bandwidth allocation. Furthermore, training and validation loss trends confirm the model’s convergence and effectiveness in real-time applications. The study underscores the integration of machine learning with network optimization principles, offering a robust framework for dynamic bandwidth management and resource-efficient network operations. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 2857 KB

Open AccessProceeding Paper

Multi-Sensor Early Warning System with Fuzzy Logic Method Based on the Internet of Things

by Fadhlurrahman Afif, Deden Witarsyah, Dedy Syamsuar and Hanif Fakhrurroja

Eng. Proc. 2025, 107(1), 57; https://doi.org/10.3390/engproc2025107057 - 3 Sep 2025

Viewed by 361

Abstract

A landslide disaster is one of the many natural disasters that often occur in Indonesia. This disaster is one of the difficult to avoid disasters, so it often causes fatalities and large material losses. Currently, mitigation systems for landslide disasters are still less [...] Read more.

A landslide disaster is one of the many natural disasters that often occur in Indonesia. This disaster is one of the difficult to avoid disasters, so it often causes fatalities and large material losses. Currently, mitigation systems for landslide disasters are still less effective in their use. Early warning systems that can give information about the landslide through smartphones could be the best solution for this digital era, because society generally has smartphones and is connected through the internet. The early warning system also demanded the ability to decide the landslide status. Fuzzy logic is one of many types of artificial intelligence used as a decision support system, which is similar to human logic. Therefore, it is necessary to build an early warning system against landslides based on the Internet of Things (IoT) that can determine the status of landslides that occur based on the soil slope using the MPU6050 accelerometer sensor and moisture data using the soil moisture sensor. This system can later monitor the slope and moisture data of the soil and can transmit landslide status on smartphone applications connected to the internet. The result of this research is an IoT-based landslide early warning system that can transmit landslide and soil moisture data and transmit landslide status in the form of push notifications on smartphones using the Blynk application. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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7 pages, 1224 KB

Open AccessProceeding Paper

A Retrospective Analysis of Soft Computing Techniques for Rule Mining

by Mrinalini Rana, Ujjwal Makkar, Isha Batra and Falentino Sembiring

Eng. Proc. 2025, 107(1), 58; https://doi.org/10.3390/engproc2025107058 - 3 Sep 2025

Viewed by 339

Abstract

This retrospective study of soft computing methods for rule mining examines the use of data mining as a method for finding important connections or trends in big datasets in order to solve challenging business problems. The effectiveness of data mining is crucial for [...] Read more.

This retrospective study of soft computing methods for rule mining examines the use of data mining as a method for finding important connections or trends in big datasets in order to solve challenging business problems. The effectiveness of data mining is crucial for organizations that analyze both historical and real-time data from diverse sources. However, the rapid growth in data volumes has presented challenges for traditional rule mining methods, creating demand for more advanced frameworks. This study incorporates soft computing algorithms and mathematical optimization techniques into rule mining, leading to more accurate and relevant results while reducing the time required for analysis. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 1419 KB

Open AccessProceeding Paper

A Real-Time Intelligent Surveillance System for Suspicious Behavior and Facial Emotion Analysis Using YOLOv8 and DeepFace

by Uswa Ihsan, Noor Zaman Jhanjhi, Humaira Ashraf, Farzeen Ashfaq and Fikri Arif Wicaksana

Eng. Proc. 2025, 107(1), 59; https://doi.org/10.3390/engproc2025107059 - 4 Sep 2025

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Abstract

This study describes the creation of an intelligent surveillance system based on deep learning that aims to improve real-time security monitoring by automatically identifying suspicious activity. By using cutting-edge computer vision techniques, the suggested system overcomes the drawbacks of conventional surveillance that depends [...] Read more.

This study describes the creation of an intelligent surveillance system based on deep learning that aims to improve real-time security monitoring by automatically identifying suspicious activity. By using cutting-edge computer vision techniques, the suggested system overcomes the drawbacks of conventional surveillance that depends on human observation to spot irregularities in public spaces. The system successfully completes motion detection, trajectory analysis, and emotion recognition by using the YOLOv8 model for object detection and DeepFace for facial emotion analysis. Roboflow is used for dataset annotation, model training with optimized parameters, and visualization of object trajectories and detection confidence. The findings show that abnormal behaviors can be accurately identified, with noteworthy observations made about the emotional expressions and movement patterns of those deemed to be threats. Even though the system performs well in real time, issues like misclassification, model explainability, and a lack of diversity in the dataset still exist. Future research will concentrate on integrating multimodal data fusion, deeper models, and temporal sequence analysis to further enhance detection robustness and system intelligence. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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7 pages, 385 KB

Open AccessProceeding Paper

COVID-19 Prediction Using Machine Learning

by Ali Raza, Attique Ur Rehman and Imam Sanjaya

Eng. Proc. 2025, 107(1), 60; https://doi.org/10.3390/engproc2025107060 - 4 Sep 2025

Viewed by 3509

Abstract

The COVID-19 virus caused unprecedented global disruption. There have been millions of cases and deaths reported worldwide. Accurate prediction of COVID-19 trends is crucial for effective decision-making, resource allocation, and policy formulation. ML has been shown to be an excellent method for projecting [...] Read more.

The COVID-19 virus caused unprecedented global disruption. There have been millions of cases and deaths reported worldwide. Accurate prediction of COVID-19 trends is crucial for effective decision-making, resource allocation, and policy formulation. ML has been shown to be an excellent method for projecting the virus’s growth and impact as it can analyze vast datasets, discover trends, and develop predictive models. This study examines the use of various machine learning techniques for the prediction of COVID-19 such as time series analysis, regression models, and classification techniques. This paper further addresses the problems and constraints of applying the ML model to this context and suggests possible enhancements for future forecasting endeavors. The overall intention of this work is to enlighten people as to how this ML-based method contributes to pandemic forecasting in terms of improvements in pandemic preparation and response schemes. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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11 pages, 832 KB

Open AccessProceeding Paper

Heart Failure Prediction Through a Comparative Study of Machine Learning and Deep Learning Models

by Mohid Qadeer, Rizwan Ayaz and Muhammad Ikhsan Thohir

Eng. Proc. 2025, 107(1), 61; https://doi.org/10.3390/engproc2025107061 - 4 Sep 2025

Viewed by 4328

Abstract

The heart is essential to human life, so it is important to protect it and understand any kind of damage it can have. All the diseases related to hearts leads to heart failure. To help address this, a tool for predicting survival is [...] Read more.

The heart is essential to human life, so it is important to protect it and understand any kind of damage it can have. All the diseases related to hearts leads to heart failure. To help address this, a tool for predicting survival is needed. This study explores the use of several classification models for forecasting heart failure outcomes using the Heart Failure Clinical Records dataset. The outcome contrasts a deep learning (DL) model known as the Convolutional Neural Network (CNN) with many machine learning models, including Random Forest (RF), K-Nearest Neighbors (KNN), Decision Tree (DT), and Naïve Bayes (NB). Various data processing techniques, like standard scaling and Synthetic Minority Oversampling Technique (SMOTE), are used to improve prediction accuracy. The CNN model performs best by achieving 99%. In comparison, the best-performing ML model, Naïve Bayes, reaches 92.57%. This shows that deep learning provides better predictions of heart failure, making it a useful tool for early detection and better patient care. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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13 pages, 1463 KB

Open AccessProceeding Paper

Improving the Mechanical Performance of TPU95A Filament in FDM 3D Printing via Parameter Optimization Using the Taguchi Method

by Abdelrahman Albardawil, Aden Robby Muhamad Aditya, Muchammad Yusup Mubarok, Lazuardi Akmal Islami and Dani Mardiyana

Eng. Proc. 2025, 107(1), 62; https://doi.org/10.3390/engproc2025107062 - 4 Sep 2025

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Abstract

This study explores the mechanical characteristics of 3D-printed specimens fabricated using TPU-95A filament, with a focus on the influence of key printing variables—temperature, speed, and layer height—on tensile strength, toughness, and surface hardness. Through systematic testing, the tensile evaluation revealed a peak tensile [...] Read more.

This study explores the mechanical characteristics of 3D-printed specimens fabricated using TPU-95A filament, with a focus on the influence of key printing variables—temperature, speed, and layer height—on tensile strength, toughness, and surface hardness. Through systematic testing, the tensile evaluation revealed a peak tensile strength of 329.02 kgf/cm² and toughness of 1.56 under conditions of elevated temperatures and optimized layer configurations. Similarly, the hardness assessment indicated a maximum average value of 74.9 Shore A, emphasizing the substantial effect of process parameters on material integrity and resilience. A detailed variance analysis confirmed the pivotal roles of temperature and layer height in enhancing mechanical properties. Using a statistical optimization approach, optimal printing conditions were identified, demonstrating that higher temperatures, moderate speeds, and reduced layer heights significantly improve the balance between strength, flexibility, and durability. These findings contribute to the development of tailored fabrication strategies, offering practical insights for applications where precision and mechanical reliability are critical. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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9 pages, 977 KB

Open AccessProceeding Paper

Boosting Software Fault Prediction Accuracy with Ensemble Learning

by Ashu Mehta, Isha Batra and Anggun Fergina

Eng. Proc. 2025, 107(1), 63; https://doi.org/10.3390/engproc2025107063 - 27 Aug 2025

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Abstract

Software defects are natural quality characteristics of software that are difficult to eliminate completely, even with concerted efforts. In addition to Bayes Net, in this study, C4.5 Decision Tree, Multilayer Perceptron (MLP), and Random Forests (RFs) are used. Moreover, an ensemble strategy with [...] Read more.

Software defects are natural quality characteristics of software that are difficult to eliminate completely, even with concerted efforts. In addition to Bayes Net, in this study, C4.5 Decision Tree, Multilayer Perceptron (MLP), and Random Forests (RFs) are used. Moreover, an ensemble strategy with GNB, BNB, RF, and MLP is proposed to enhance the prediction accuracy. Results from empirical evaluations indicate that the F1 score, accuracy, precision, and recall of this strategy are higher than those of any individual approach, providing strong evidence for the ensemble model as an effective method for improving defect prediction performance. The ensemble approach could be a promising pathway to bolster the software quality process, mainly in machine learning-based fault prediction. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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11 pages, 299 KB

Open AccessProceeding Paper

Early Mental Health Detection and Emotional States in Teenagers Through Chatbot Systems Using Natural Language Processing (NLP)

by Kamdan Kamdan, Najla Ghaida Fauziyah, Muhammad A. Fadlullah, Dilla A. Hanif and Ivana Lucia Kharisma

Eng. Proc. 2025, 107(1), 64; https://doi.org/10.3390/engproc2025107064 - 2 Sep 2025

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Abstract

The World Health Organization estimates that between 10% and 20% of young people worldwide suffer from mental health problems during adolescence, which is a crucial time for many youngsters. Adolescent mental health cases have increased by 31% as a result of the COVID-19 [...] Read more.

The World Health Organization estimates that between 10% and 20% of young people worldwide suffer from mental health problems during adolescence, which is a crucial time for many youngsters. Adolescent mental health cases have increased by 31% as a result of the COVID-19 epidemic, which has made the issue much worse. Due to stigma, restricted access, and challenges in evaluating emotional states, traditional mental health care frequently faces challenges. The goal of this research is to create a chatbot system that uses machine learning and natural language processing (NLP) to identify emotional distress and mental health issues in teenagers at an early age. The study offers a safe environment for teenagers to express their emotions while examining chatbot interactions to find trends suggestive of mental health problems. To improve the efficacy of the chatbot, the methodology integrates quantitative and qualitative techniques, utilizing data from open datasets and mental health services. Sentiment analysis and emotion identification are important strategies that enable the chatbot to react sympathetically. By offering easily available support and highlighting the role of technology in addressing adolescent mental health issues, this project ultimately seeks to enhance youth mental health outcomes. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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9 pages, 664 KB

Open AccessProceeding Paper

Concrete Innovation Using Tree Branch Waste as Coarse Aggregate and Stone Ash as Fine Aggregate

by Irsad Fauzan Sunarlan, Okky Lutfi Fauzi, Usep Saepudin and Utamy Sukmayu Saputri

Eng. Proc. 2025, 107(1), 65; https://doi.org/10.3390/engproc2025107065 - 4 Sep 2025

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Abstract

Concrete is a widely used construction material. This research investigates the effect of adding tree branch waste and stone dust as substitutes for coarse and fine aggregates on concrete’s physical and mechanical properties. The results show that these additives significantly impact weight and [...] Read more.

Concrete is a widely used construction material. This research investigates the effect of adding tree branch waste and stone dust as substitutes for coarse and fine aggregates on concrete’s physical and mechanical properties. The results show that these additives significantly impact weight and compressive strength. The weight comparison for 10% additive concrete was 7.28 kg at 7 and 14 days, while for 20% additive concrete, it was 7.02 kg at 7 days and 7.06 kg at 14 days. Normal concrete weighed 7.50 kg at 7 days and 7.66 kg at 14 days. The planned compressive strength (K250 or F’c: 20 MPa) for 28 days was met, with samples containing 10% and 20% additives exceeding the planned strength. However, increased use of these materials led to a reduction in compressive strength. Therefore, the addition of tree branches and stone dust should be limited to 10%, as the highest compressive strength was obtained at this percentage. This research suggests that using tree branch waste and stone dust as partial substitutes for aggregates can reduce concrete’s weight while maintaining its strength. Limiting the addition to 10% is recommended for optimal results. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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17 pages, 1662 KB

Open AccessProceeding Paper

Performance Analysis of IndoBERT for Detection of Online Gambling Promotion in YouTube Comments

by Kamdan Kamdan, Malik Pajar Anugrah, Moh Jeli Almutaali, Restu Ramdani and Ivana Lucia Kharisma

Eng. Proc. 2025, 107(1), 66; https://doi.org/10.3390/engproc2025107066 - 2 Sep 2025

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Abstract

The proliferation of online gambling promotions on social media platforms, particularly YouTube, poses a significant challenge in digital security and regulation. This study evaluates the performance of IndoBERT in detecting online gambling-related spam in YouTube comments. The research utilizes YouTube Data API to [...] Read more.

The proliferation of online gambling promotions on social media platforms, particularly YouTube, poses a significant challenge in digital security and regulation. This study evaluates the performance of IndoBERT in detecting online gambling-related spam in YouTube comments. The research utilizes YouTube Data API to collect comments, preprocess the text through cleaning and tokenization, and fine-tune IndoBERT for classification. The model’s performance is assessed using accuracy, precision, recall, and F1-score metrics. IndoBERT achieves outstanding results with an accuracy of 98.26%, proving its effectiveness in detecting online gambling promotion. The confusion matrix analysis highlights a low error rate, with minimal false positives and false negatives. IndoBERT is a promising tool for combating online gambling spam, offering high reliability for automated content moderation. Future improvements should focus on handling implicit promotional language, enhancing dataset diversity, and integrating rule-based filtering. This study contributes to NLP advancements in Indonesian text classification, supporting efforts to maintain a safer digital environment. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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11 pages, 919 KB

Open AccessProceeding Paper

Implementation of Predictive Analytics in Healthcare Using Hybrid Deep Learning Models

by Poonam Kargotra, Irfan Ramzan Parray, Arun Malik and Ivana Lucia Kharisma

Eng. Proc. 2025, 107(1), 67; https://doi.org/10.3390/engproc2025107067 - 8 Sep 2025

Viewed by 1306

Abstract

Predictive analytics has emerged as a powerful tool for improving decision-making in healthcare, particularly in disease prediction and patient management. However, conventional architectures may find it difficult to handle various features of healthcare data, such as high dimensionality and ineffective measures to handle [...] Read more.

Predictive analytics has emerged as a powerful tool for improving decision-making in healthcare, particularly in disease prediction and patient management. However, conventional architectures may find it difficult to handle various features of healthcare data, such as high dimensionality and ineffective measures to handle unstructured data. This work examines the shortcomings of the traditional ML strategy by fusing deep learning approaches with the existing models in an improved predictive performance. Specifically, we propose three hybrid models: (1) Random Forest and Neural Networks (RF + NN), (2) XGBoost and Neural Networks (XGBoost + NN), and (3) Autoencoder and Random Forest (Autoencoder + RF). The goal is to compare these models’ ability to predict healthcare outcomes using standard performance metrics, which include the measures of accuracy, precision, recall, and F1-score. An important research gap revealed from the literature review is that most models tend to have higher precision at the cost of recall and vice versa. Our proposed hybrid models combine the strengths of feature selection from traditional algorithms (RF, XGBoost) with the advanced pattern recognition capabilities of Neural Networks (NNs) and autoencoders, aiming for a more balanced predictive performance. The RF + NN model produces the highest accuracy at 96.81%, with precise accuracy at 90.48% and accurate precision at 70.08%. Nevertheless, the accuracy of a slightly lower XGBoost + NN model of 96.75% showed better actual capability of identifying true positives than false positives, with 73.54% recall. From our results it is evident that the best model in terms of precision was the Autoencoder + RF model, with a precision of 91.36%; it was however the worst in recall, with only 66.22%. Accordingly, these findings imply that for the same level of predictive accuracy, the hybrid models are better in handling imbalanced problems and provide directions for better healthcare predictive systems in the future. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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11 pages, 1664 KB

Open AccessProceeding Paper

Dynamic Feature Engineering for Adaptive Fraud Detection

by Ajay Sharma, Shamneesh Sharma, Arun Malik, Rajeev Sobti and Anang Suryana

Eng. Proc. 2025, 107(1), 68; https://doi.org/10.3390/engproc2025107068 - 8 Sep 2025

Viewed by 3057

Abstract

In today’s digital economy, electronic payments are essential to supporting financial transactions. However, the danger of fraud also rises with company complexity and volume. This study uses machine learning and advanced analytics to investigate fraud detection in electronic payments. Using business tools like [...] Read more.

In today’s digital economy, electronic payments are essential to supporting financial transactions. However, the danger of fraud also rises with company complexity and volume. This study uses machine learning and advanced analytics to investigate fraud detection in electronic payments. Using business tools like accounts, account types, and balance sheets, we spot patterns and trends connected to illicit activities. To detect and identify fraud, our study uses pre-existing data, machine learning algorithms, and infrastructure. The author has assessed the performance of several models, such as logistic regression, random forests, and k-nearest neighbor models, using criteria like accuracy, precision, and recall. To determine the most important characteristics for fraud detection, the author also conducts a significance analysis and examines the model’s interpretability. According to the current study’s findings, financial institutions and payment systems will be able to identify fraud more efficiently and gain an improved knowledge of the traits of commercial fraud. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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10 pages, 253 KB

Open AccessProceeding Paper

Combating the Problems of the Necessity of Continuous Learning in Medical Datasets of Type 2 Diabetes

by Jenny Price, Tatsuya Yamazaki, Kazuya Fujihara and Hirohito Sone

Eng. Proc. 2025, 107(1), 69; https://doi.org/10.3390/engproc2025107069 - 8 Sep 2025

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Abstract

There are two major problems that researchers must contend with when dealing with machine learning in the medical field. The first being the ever-changing nature of what is considered good practice, and the lack of available data to train. The change in opinion [...] Read more.

There are two major problems that researchers must contend with when dealing with machine learning in the medical field. The first being the ever-changing nature of what is considered good practice, and the lack of available data to train. The change in opinion on what is considered good practice requires an ongoing effort to update the machine learning models. This requires a concept called continual learning, which requires that researchers must combat against the problem of a model forgetting previously learned information and the balancing of bigger classes and newer smaller classes. Usually, when new information is introduced, a model must be retrained, which threatens the previously gained knowledge. The training is then difficult because of the lack of data. However, when dealing with medications they can become irrelevant to use. When such things happen when dealing with the standard machine learning models, the entire model needs to be retrained in order to remove the specific medication. This causes even more difficulties, because patient data are heavily protected and there is the chance that the dataset will not be available for training again in the future. While most papers focus on medical imaging and diagnosis, medicine does not end with diagnosis. We have an elderly population that is growing and not enough doctors are are available. To have everyone be able to see specialists, or even a doctor, is becoming even harder. To combat these issues, we need to have models in use that we can update continuously to help bridge the gap of care. We propose a method that can be trained continuously in order to easily remove outdated medications. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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8 pages, 608 KB

Open AccessProceeding Paper

Investigating Cervical Cancer Detection Frameworks Based on Machine Learning: The Critical Tradeoff Between Accuracy and Data Security

by Sofia Singla, Navdeep Singh Sodhi, Isha Batra and Somantri

Eng. Proc. 2025, 107(1), 70; https://doi.org/10.3390/engproc2025107070 - 9 Sep 2025

Viewed by 484

Abstract

Cervical cancer has emerged as the most prevalent and deadly illness affecting women across the globe. Researchers are trying their best to detect this life-threatening illness accurately. In view of this only, machine learning approaches, multiple medical procedures, statistical models, etc., have been [...] Read more.

Cervical cancer has emerged as the most prevalent and deadly illness affecting women across the globe. Researchers are trying their best to detect this life-threatening illness accurately. In view of this only, machine learning approaches, multiple medical procedures, statistical models, etc., have been utilized to provide optimized and efficient treatment to all patients to protect their lives. In this study, we have compared previously proposed frameworks for the early detection of cervical cancer and analysis of patients’ data security. We demonstrated the respective benefits and limitations, investigated the datasets and the type of data employed, and analyzed the accuracy of the healthcare procedures utilized for patients in terms of improving management. The limitations of reviewed studies show that more reliable proposals need to be presented by researchers in future. Based on this only, it is concluded that the accurate and early detection of cervical cancer shows a tradeoff with patients’ data security while communicating across healthcare institutions. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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11 pages, 1612 KB

Open AccessProceeding Paper

Implementation of You Only Look Once (YOLO) Technology and Reinforcement Learning for Web-Based Project Monitoring

by Anggun Fergina, Muhamad Fadhli Nurdiansyah Rangkuti, Axa Rajandrya, Muhamad Rizki Akbar, Lusiana Sani Parwati, Zaenal Alamsyah and Amanna Dzikrillah Lazuardini

Eng. Proc. 2025, 107(1), 71; https://doi.org/10.3390/engproc2025107071 - 12 Sep 2025

Abstract

Project monitoring is an important element in project management that aims to ensure project implementation in accordance with the plan, schedule, budget, and objectives that have been set. The ineffectiveness of project monitoring can cause various problems, such as delays, cost overruns, inappropriate [...] Read more.

Project monitoring is an important element in project management that aims to ensure project implementation in accordance with the plan, schedule, budget, and objectives that have been set. The ineffectiveness of project monitoring can cause various problems, such as delays, cost overruns, inappropriate quality of results, and poor communication between stakeholders. To address these issues, technological advances such as YOLO (You Only Look Once) and Reinforcement Learning (RL) offer innovative solutions through real-time visual detection and data-driven automated decision making. This research aims to develop a web-based project monitoring system that integrates YOLO to detect activities in the field, such as workers and heavy equipment, and RL to provide optimal recommendations for resource management. The implementation of the system is expected to increase efficiency, reduce risk, and support more accurate decision making. Based on previous research, the adoption of AI technology in project monitoring is proven to reduce operational costs and increase productivity. This web-based system is designed to provide flexibility and accessibility, allowing users to monitor projects in real-time through an interactive interface. The expected outcome of this research is the creation of an effective technological solution to improving the efficiency of construction project management, as supported by the findings of previous research that shows the great potential of AI in the construction sector. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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11 pages, 241 KB

Open AccessProceeding Paper

A Literature Review: Bias Detection and Mitigation in Criminal Justice

by Pravallika Kondapalli, Parminder Singh, Arun Malik and C. S. A. Teddy Lesmana

Eng. Proc. 2025, 107(1), 72; https://doi.org/10.3390/engproc2025107072 - 9 Sep 2025

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Abstract

The use of algorithmic models or systems in criminal justice is increasing day by day, yet the bias in these algorithms can perpetuate historical inequities, especially in predictive tools like COMPAS. This literature survey examines 30 studies addressing algorithmic bias in criminal justice. [...] Read more.

The use of algorithmic models or systems in criminal justice is increasing day by day, yet the bias in these algorithms can perpetuate historical inequities, especially in predictive tools like COMPAS. This literature survey examines 30 studies addressing algorithmic bias in criminal justice. Key topics include bias types, bias detection metrics or variables such as demographic parity and equalized odds, and bias mitigation techniques like re-weighting and adversarial debiasing. Challenges in achieving fair and unbiased predictions are highlighted, including ethical considerations and trade-offs or a balance between fairness and accuracy. Insights from COMPAS and similar systems underscore the need for ongoing research, proposing potential directions for policy and practice. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

14 pages, 4751 KB

Open AccessProceeding Paper

Latent Structural Discovery in Clinical Texts via Transformer-Based Embeddings and Token Graphs

by Farzeen Ashfaq, NZ Jhanjhi, Navid Ali Khan, Chen Jia, Uswa Ihsan and Anggy Pradiftha Junfithrana

Eng. Proc. 2025, 107(1), 73; https://doi.org/10.3390/engproc2025107073 - 9 Sep 2025

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Abstract

Electrocardiogram reports are an important component of cardiovascular diagnostics, routinely generated in hospitals and clinical settings to monitor cardiac activity and guide medical decision-making. ECG reports often consist of structured waveform data accompanied by free-text interpretations written by clinicians. Although the waveform data [...] Read more.

Electrocardiogram reports are an important component of cardiovascular diagnostics, routinely generated in hospitals and clinical settings to monitor cardiac activity and guide medical decision-making. ECG reports often consist of structured waveform data accompanied by free-text interpretations written by clinicians. Although the waveform data can be analyzed using signal processing techniques, the unstructured text component contains rich, contextual insights into diagnoses, conditions, and patient-specific observations that are not easily captured by conventional methods. Extracting meaningful patterns from clinical narratives poses significant challenges. In this work, we present an unsupervised framework for exploring and analyzing ECG diagnostic reports using transformer-based language modeling and clustering techniques. We use the domain-specific language model BioBERT to encode text-based ECG reports into dense vector representations that capture the semantics of medical language. These embeddings are subsequently standardized and subjected to a series of clustering algorithms, including KMeans, hierarchical clustering, DBSCAN, and K-Medoids, to uncover latent groupings within the data. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 1892 KB

Open AccessProceeding Paper

Smart Cloud Architectures: The Combination of Machine Learning and Cloud Computing

by Aqsa Asghar, Attique Ur Rehman, Rizwan Ayaz and Anang Suryana

Eng. Proc. 2025, 107(1), 74; https://doi.org/10.3390/engproc2025107074 - 9 Sep 2025

Viewed by 101

Abstract

Machine learning (ML) in cloud architectures is used to manage powerful servers that run distributed systems over the internet. ML predicts the workload and traffic from cloud consumers and allocates resources according to the demand. ML in cloud architectures is there to improve [...] Read more.

Machine learning (ML) in cloud architectures is used to manage powerful servers that run distributed systems over the internet. ML predicts the workload and traffic from cloud consumers and allocates resources according to the demand. ML in cloud architectures is there to improve performance and increase availability to manage cloud computing resources. The combination of ML and cloud architectures balances the workload and ensures reliability. This research discusses cloud architectures that use ML to run different algorithms to predict the improvement in the cloud architectures by using a cloud computing resource dataset. The dataset is used with different classifiers with the same ML framework that is discussed in this paper; the ML framework has a sequence to provide the steps of the model training and testing and uses different techniques and methods for the better performance of the cloud architectures. The researchers used various ML techniques to create a model for predicting the workload. To enhance the model’s performance and flexibility, we used a regression-based dataset that was recently updated, which was used with different ML approaches to predict better performance in the cloud architectures. By using the Generalized Linear Model, we achieved the highest performance. The R² value refers to the goodness of the model and its performance. Using cloud datasets and machine learning with cloud architectures enhances performance using the different techniques in this paper, resulting in a more generalizable model with overfitting risk. This study focuses on refining the execution of cloud architectures with the help of ML. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 1344 KB

Open AccessProceeding Paper

Decision Support System for Assessing Teacher Performance Using the Simple Additive Weighting (SAW) Method at SMK XYZ

by Anggun Fergina, Asep Sukandar, Rahma Nisa Salsabila and Ayuni Indah Wulandari

Eng. Proc. 2025, 107(1), 75; https://doi.org/10.3390/engproc2025107075 - 9 Sep 2025

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Abstract

SMK XYZ is a private school under Yayasan Pembina Pendidikan Doa Bangsa (YPPDB) which was established in 2011. The school has several expertise programs, including Software Engineering, Institutional Accounting and Finance, and Motorcycle Business Engineering. Assessing the success of a school is an [...] Read more.

SMK XYZ is a private school under Yayasan Pembina Pendidikan Doa Bangsa (YPPDB) which was established in 2011. The school has several expertise programs, including Software Engineering, Institutional Accounting and Finance, and Motorcycle Business Engineering. Assessing the success of a school is an important thing that greatly affects the development of students in the learning process to achieve their goals. Assessment of teachers’ work should be performed using appropriate and efficient methods. To improve teacher performance, the development of an agenda monitoring and assessment system based on the Simple Additive Weighting (SAW) method can be an effective alternative. This system is designed to assist school management in monitoring teacher activities objectively and measurably, as well as providing clear assessments based on certain criteria such as attendance, tardiness, student evaluation results, and innovation in learning. The SAW method is used to calculate the final score of teacher performance by summing up the weighted values of each normalized criterion. In this case study, the system helps decision makers to recognize the strengths and weaknesses of each teacher, so that related recommendations for competency development can be given. The implementation of this system demonstrates increased responsibility in appraisal and motivates teachers to improve their performance according to set standards. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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9 pages, 546 KB

Open AccessProceeding Paper

Static Malware Detection and Classification Using Machine Learning: A Random Forest Approach

by Kamdan, Yoga Pratama, Rifki Sariful Munzi, Aqshal Bilnandzari Mustafa and Ivana Lucia Kharisma

Eng. Proc. 2025, 107(1), 76; https://doi.org/10.3390/engproc2025107076 - 9 Sep 2025

Viewed by 143

Abstract

Malware remains one of the most critical threats in the digital ecosystem, targeting both mobile and desktop platforms. Traditional signature-based detection techniques face limitations in identifying polymorphic and zero-day variants. This study proposes a static analysis-based approach using machine learning classifiers, focusing on [...] Read more.

Malware remains one of the most critical threats in the digital ecosystem, targeting both mobile and desktop platforms. Traditional signature-based detection techniques face limitations in identifying polymorphic and zero-day variants. This study proposes a static analysis-based approach using machine learning classifiers, focusing on Random Forest, Decision Tree, and Support Vector Machine (SVM). The dataset was collected from MalwareBazaar, and static features such as PE headers, entropy, and API calls were extracted. Experimental results show that SVM achieved the highest accuracy at 53.2%, while Decision Tree obtained the best F1-score at 61.1%, indicating stronger recall capabilities. Random Forest provided balanced results across all metrics with a shorter training time of 0.23 s, highlighting its efficiency for practical use. These findings demonstrate that while no single classifier dominates across all metrics, Random Forest offers a trade-off between performance and efficiency, making it suitable for large-scale static malware detection systems. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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8 pages, 1532 KB

Open AccessProceeding Paper

Deep Learning Approach for Breast Cancer Detection Using UNet and CNN in Ultrasound Imaging

by Ravikumar Ch, Usikela Naresh, Arun Malik and M. Putra Sani Hattamurrahman

Eng. Proc. 2025, 107(1), 77; https://doi.org/10.3390/engproc2025107077 - 9 Sep 2025

Viewed by 13

Abstract

Breast cancer continues to be a serious concern for global health, especially when proper treatment is time-sensitive. This research contributes a novel method to improve breast cancer detection in ultrasound images by employing a deep learning technique that integrates UNet and Convolution Neural [...] Read more.

Breast cancer continues to be a serious concern for global health, especially when proper treatment is time-sensitive. This research contributes a novel method to improve breast cancer detection in ultrasound images by employing a deep learning technique that integrates UNet and Convolution Neural Networks(CNN) architectures. For tumor segmentation within breast ultrasound images, UNet has been used, alongside a CNN that classifies the resulting tumor as benign or malignant and performs feature extraction. When evaluated on the ‘Dataset_BUSI_with_GT’, the model was found to be reliable across varying conditions, achieving high sensitivity (97.44%) and accuracy (95.24%), scores better than those ofexisting approaches. The developed system is composed of an imaging module, image upload, preprocessing, inference, result display, and feedback, providing non-interrupted service and enhancing user-centered functionalities. Continuous improvement capabilities allow the system to redefine new image changes, sustaining reliability in examinations and clinical settings. Compared to other methodologies, the proposed model demonstrates superior accuracy alongside less computational resources, translating to reduced diagnostic human error while optimizing the workflow in primary healthcare. Future work could includethe application of multimodal imaging, deploy real-time imaging, and increase its interpretability to strengthen its use in medical diagnosis. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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14 pages, 975 KB

Open AccessProceeding Paper

Recent Advancements in Machine Learning Models for Malware Detection: A Systematic Literature Review

by Nurul Islam Hasanah, Gina Purnama Insany, Ivana Lucia Kharisma and Natasya Dewi Rahayu

Eng. Proc. 2025, 107(1), 78; https://doi.org/10.3390/engproc2025107078 - 10 Sep 2025

Viewed by 147

Abstract

Malware detection has become a critical area of research due to the increasing sophistication of cyberattacks targeting various platforms, including IoT devices, Android systems, and desktop environments. This study employed the systematic literature review (SLR) method, following PRISMA guidelines, to analyze recent advancements [...] Read more.

Malware detection has become a critical area of research due to the increasing sophistication of cyberattacks targeting various platforms, including IoT devices, Android systems, and desktop environments. This study employed the systematic literature review (SLR) method, following PRISMA guidelines, to analyze recent advancements in malware detection using machine learning (ML) models. A total of six studies were selected based on strict inclusion and exclusion criteria, focusing on algorithms, datasets, performance metrics, and targeted platforms. The review reveals that ensemble methods like Gradient Boosting and XGBoost achieve high detection accuracy, with several models exceeding 90% on benchmark datasets such as VirusShare and MSCAD. Additionally, IoT platforms emerged as the most commonly targeted environment in malware detection research, emphasizing their vulnerability. Despite these advancements, the review identifies gaps in dataset diversity and platform-specific optimizations. This study provides insights into the current trends, challenges, and future directions for machine learning-based malware detection. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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14 pages, 869 KB

Open AccessProceeding Paper

A Novel Adaptive Cluster-Based Federated Learning Framework for Anomaly Detection in VANETs

by Ravikumar Ch, P Sudheer, Isha Batra and Falentino Sembiring

Eng. Proc. 2025, 107(1), 79; https://doi.org/10.3390/engproc2025107079 - 10 Sep 2025

Viewed by 27

Abstract

Vehicular Ad Hoc Networks (VANETs) encounter significant hurdles in anomaly detection owing to their dynamic characteristics, scalability demands, and privacy issues. This research presents a new Adaptive Cluster-Based Federated Learning (ACFL) architecture to tackle these challenges. In contrast to conventional machine learning models, [...] Read more.

Vehicular Ad Hoc Networks (VANETs) encounter significant hurdles in anomaly detection owing to their dynamic characteristics, scalability demands, and privacy issues. This research presents a new Adaptive Cluster-Based Federated Learning (ACFL) architecture to tackle these challenges. In contrast to conventional machine learning models, the ACFL framework dynamically organizes cars through the Context-Aware Cluster Manager (CACM), which adjusts clusters according to real-time variables like mobility, node density, and communication patterns. Each cluster utilizes Modified Temporal Neural Networks (MTNNs) for localized anomaly detection, employing time-series analysis to improve precision. Federated learning is enabled via the Hierarchical Aggregation Layer (HAL), which effectively consolidates updates across clusters, ensuring scalability and data confidentiality. The proposed framework was assessed in comparison to established machine learning models, including Support Vector Machines (SVM), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbor (KNN), and the K-Nearest Neighbors with Kernelized Feature Selection and Clustering(KNN-KFSC) approach, utilizing the VeReMi dataset. Findings demonstrate that ACFL surpasses existing models in identifying abnormalities, including Global Positioning System(GPS)spoofing and Denial of Service (DoS) assaults, exhibiting enhanced accuracy, adaptability, and scalability. This work emphasizes the capability of ACFL to tackle urgent security issues in VANET, facilitating the development of secure next-generation intelligent transportation systems. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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7 pages, 191 KB

Open AccessProceeding Paper

Identifying the Most Effective and Worthwhile PayLater Application for Gen Z in the Digital Era Using the TOPSIS Method

by Dede Sukmawan, Riri Ramadhani, Tasya Sabila Aulia and Irvan Maulana Armadian

Eng. Proc. 2025, 107(1), 80; https://doi.org/10.3390/engproc2025107080 - 10 Sep 2025

Viewed by 17

Abstract

The development of digital technology has changed various aspects of life, including in the financial sector. One of the innovations that has received a significant amount of attention is the PayLater service. Generation Z, as a generation born in the digital era, has [...] Read more.

The development of digital technology has changed various aspects of life, including in the financial sector. One of the innovations that has received a significant amount of attention is the PayLater service. Generation Z, as a generation born in the digital era, has a unique consumption pattern. Members of Generation Z tend to look for financial solutions that are fast, practical, and accessible through technology. This study aims to provide guidance for Generation Z (age 20–28 years) in choosing the PayLater application that best suits their needs and financial situation. Using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, this study evaluates the effectiveness of several popular PayLater applications. Data were collected through an online questionnaire aimed at potential users who already had a monthly income. The criteria used in the assessment include the average transaction value, difficulty in paying installments, data security and privacy, ease of application access, and interest rates. The results of the analysis show that Shopee PayLater has the highest preference score, making it the best choice for Generation Z. This research is expected to contribute to improving financial literacy and helping Generation Z to make better decisions regarding financial services in the digital era. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

11 pages, 1005 KB

Open AccessProceeding Paper

Multimodal Fusion for Enhanced Human–Computer Interaction

by Ajay Sharma, Isha Batra, Shamneesh Sharma and Anggy Pradiftha Junfithrana

Eng. Proc. 2025, 107(1), 81; https://doi.org/10.3390/engproc2025107081 - 10 Sep 2025

Viewed by 17

Abstract

Our paper introduces a novel idea of a virtual mouse character driven by gesture detection, eye-tracking, and voice monitoring. This system uses cutting-edge computer vision and machine learning technology to let users command and control the mouse pointer using eye motions, voice commands, [...] Read more.

Our paper introduces a novel idea of a virtual mouse character driven by gesture detection, eye-tracking, and voice monitoring. This system uses cutting-edge computer vision and machine learning technology to let users command and control the mouse pointer using eye motions, voice commands, or hand gestures. This system’s main goal is to provide users who want a more natural, hands-free approach to interacting with their computers as well as those with impairments that limit their bodily motions, such as those with paralysis—with an easy and engaging interface. The system improves accessibility and usability by combining many input modalities, therefore providing a flexible answer for numerous users. While the speech recognition function permits hands-free operation via voice instructions, the eye-tracking component detects and responds to the user’s gaze, therefore providing exact cursor control. Gesture recognition enhances these features even further by letting users use their hands simply to execute mouse operations. This technology not only enhances personal user experience for people with impairments but also marks a major development in human–computer interaction. It shows how computer vision and machine learning may be used to provide more inclusive and flexible user interfaces, therefore improving the accessibility and efficiency of computer usage for everyone. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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16 pages, 832 KB

Open AccessProceeding Paper

Leveraging MongoDB in Real-Time Emotion Recognition from Video for Scalable and Efficient Data Handling

by Haikal Muhammad Kurniawan, Muhammad Maulidan, Muhammad Faisal Zulmaulidin and Alun Sujjada

Eng. Proc. 2025, 107(1), 84; https://doi.org/10.3390/engproc2025107084 - 11 Sep 2025

Abstract

Real-time emotion recognition from video poses significant challenges in handling large-scale and continuously growing datasets. Traditional relational databases often fail to meet the scalability and efficiency requirements of such applications. MongoDB, a NoSQL database, offers significant advantages in scalability, speed, and data management, [...] Read more.

Real-time emotion recognition from video poses significant challenges in handling large-scale and continuously growing datasets. Traditional relational databases often fail to meet the scalability and efficiency requirements of such applications. MongoDB, a NoSQL database, offers significant advantages in scalability, speed, and data management, making it an ideal choice for video-based emotion recognition systems. This paper explores the use of MongoDB to optimize the management of video data in real-time emotion recognition, leveraging its features like sharding, indexing, and replication. We demonstrate how MongoDB’s advanced features enhance the performance and reliability of emotion recognition systems by reducing latency and processing time. Through experimental results, we show that MongoDB outperforms traditional relational databases and other NoSQL solutions in handling large datasets efficiently. Future work will explore integrating MongoDB with cloud platforms to improve scalability and incorporate advanced deep learning algorithms for better emotion recognition accuracy. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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5 pages, 202 KB

Open AccessProceeding Paper

Cattle Disease Prediction Using Machine Learning Algorithms

by Muneeb Ahmed, Sabeen Javaid and Sudin Saepudin

Eng. Proc. 2025, 107(1), 85; https://doi.org/10.3390/engproc2025107085 - 1 Sep 2025

Abstract

The main purpose of this research paper is to assess the prevalence, diagnosis, and management of common cattle diseases in different countries, including Jordan, Pakistan, Uganda, Korea, Bangladesh, and Europe. Our dataset includes 163 cases and 123 detailed symptoms; this research identifies patterns [...] Read more.

The main purpose of this research paper is to assess the prevalence, diagnosis, and management of common cattle diseases in different countries, including Jordan, Pakistan, Uganda, Korea, Bangladesh, and Europe. Our dataset includes 163 cases and 123 detailed symptoms; this research identifies patterns of symptoms with great accuracy. The accuracy of the dataset is 98%. The main diseases in cattle include digestive disorders, osteodystrophy, tick-borne diseases, and lumpy skin disease. Two types of tools were used: innovative diagnostic tools, like fuzzy logic models, and a diagnostic decision support tool (DDST). This tool performs disease detection and management. The findings demonstrate the importance of accurate diagnosis in vaccination programs and biosecurity measures in order to adequately measure economic losses and improve livestock health. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

877 KB

Open AccessProceeding Paper

N-Gram and Full-Text Search Algorithm Testing for Pattern Recognition in a Chatbot Engine

by I Made Sukarsa, Deden Witarsyah, I Putu Agung Bayupati, Putu Wira Buana, Ni Wayan Wisswani, I Ketut Adi Purnawan, I Putu Adi Putra Setiawan, I Putu Ngurah Krisna Dana, I Wayan Darmika Esa Krissayoga and Eko Prasetyo

Eng. Proc. 2025, 107(1), 86; https://doi.org/10.3390/engproc2025107086 (registering DOI) - 12 Sep 2025

Abstract

The development of chatbots to access database services and information systems has triggered a lot of research on frameworks for service development, including the development of ISONER (Information System On Internet Messenger). This framework consists of multiple phases including pattern recognition, query processing, [...] Read more.

The development of chatbots to access database services and information systems has triggered a lot of research on frameworks for service development, including the development of ISONER (Information System On Internet Messenger). This framework consists of multiple phases including pattern recognition, query processing, and response generation. In its implementation, the framework develops pattern recognition services that are currently based on Natural Language Processing (NLP). Improved pattern recognition algorithms enhance the system’s ability to accurately interpret user intent. The pattern recognition used in this research utilizes built-in plugins from MySQL, namely N-gram and Full-Text Search, which can be run directly on the MySQL engine to reduce latency and do not require another programming language. The FTS and fourgram algorithms gave the best results when applied on 100 test data points, with a threshold of 0.91, accuracy of 91%, precision of 99%, and recall of 92%; the average computation time was 19 s for 100 test data points and 2 min 49 s for 1000 data points tested simultaneously. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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