Engineering Proceedings

22 pages, 1231 KB

Open AccessProceeding Paper

Emerging Trends in Paper-Based Electrochemical Biosensors for Healthcare Applications

by Aparoop Das, Partha Protim Borthakur, Dibyajyoti Das, Jon Jyoti Sahariah, Parimita Kalita and Kalyani Pathak

Eng. Proc. 2025, 106(1), 8; https://doi.org/10.3390/engproc2025106008 - 11 Sep 2025

Abstract

Paper-based electrochemical biosensors have emerged as a revolutionary technology in healthcare diagnostics due to their affordability, portability, ease of use, and environmental sustainability. These biosensors utilize paper as the primary material, capitalizing on its unique properties such as high porosity, flexibility, and capillary [...] Read more.

Paper-based electrochemical biosensors have emerged as a revolutionary technology in healthcare diagnostics due to their affordability, portability, ease of use, and environmental sustainability. These biosensors utilize paper as the primary material, capitalizing on its unique properties such as high porosity, flexibility, and capillary action, which make it an ideal candidate for low-cost, functional, and reliable diagnostic devices. The simplicity and cost-effectiveness of paper-based biosensors make them especially suitable for point-of-care (POC) applications, particularly in resource-limited settings where traditional diagnostic tools may be inaccessible. Their lightweight nature and ease of operation allow non-specialized users to perform diagnostic tests without the need for complex laboratory equipment, making them suitable for emergency, field, and remote applications. Technological advancements in paper-based biosensors have significantly enhanced their capabilities. Integration with microfluidic systems has improved fluid handling and reagent storage, resulting in enhanced sensor performance, including greater sensitivity and specificity for target biomarkers. The use of nanomaterials in electrode fabrication, such as reduced graphene oxide and gold nanoparticles, has further elevated their sensitivity, allowing for the precise detection of low-concentration biomarkers. Moreover, the development of multiplexed sensor arrays has enabled the simultaneous detection of multiple biomarkers from a single sample, facilitating comprehensive and rapid diagnostics in clinical settings. These biosensors have found applications in diagnosing a wide range of diseases, including infectious diseases, cancer, and metabolic disorders. They are also effective in genetic analysis and metabolic monitoring, such as tracking glucose, lactate, and uric acid levels, which are crucial for managing chronic conditions like diabetes and kidney diseases. In this review, the latest advancements in paper-based electrochemical biosensors are explored, with a focus on their applications, technological innovations, challenges, and future directions. Full article

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5 pages, 1050 KB

Open AccessProceeding Paper

In Vitro Cytotoxicity of Single Walled Carbon Nanotube Bioconjugates on Cancer Cells

by Zvikomborero T. Gwanzura, Willem J. Perold and Anna-Mart Engelbrecht

Eng. Proc. 2025, 109(1), 6; https://doi.org/10.3390/engproc2025109006 - 11 Sep 2025

Abstract

Carbon nanotubes have shown great promise in drug delivery systems as they can easily penetrate the cell membrane. Herein, carbon nanotubes functionalized with polyethylene glycol and folic acid were used to improve target specificity in breast and colon cancer cells. The functionalized carbon [...] Read more.

Carbon nanotubes have shown great promise in drug delivery systems as they can easily penetrate the cell membrane. Herein, carbon nanotubes functionalized with polyethylene glycol and folic acid were used to improve target specificity in breast and colon cancer cells. The functionalized carbon nanotubes were bioconjugated with bioactive compounds from plant extracts. In vitro cytotoxicity studies were conducted to demonstrate cellular uptake and apoptosis due to bioconjugate cellular internalization. The bioconjugates were able to preserve normal cells and induce cell death in cancer cells. The efficacy of the carbon nanotube bioconjugates in this study shows great potential in cancer therapy applications. Full article

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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

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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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

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)

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

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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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

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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12 pages, 1074 KB

Open AccessProceeding Paper

Multiplexed Quantification of Soil Nutrients Using an AI-Enhanced and Low-Cost Impedimetric Sensor

by Antonio Ruiz-Gonzalez

Eng. Proc. 2025, 106(1), 7; https://doi.org/10.3390/engproc2025106007 - 10 Sep 2025

Abstract

Soil nutrient monitoring is essential to achieving UN development goals and meeting the projected 70% increase in agricultural production from 2009 values by 2050. This study presents a novel, low-cost impedimetric device for the direct and simultaneous measurement of soil ion bioavailability (Na [...] Read more.

Soil nutrient monitoring is essential to achieving UN development goals and meeting the projected 70% increase in agricultural production from 2009 values by 2050. This study presents a novel, low-cost impedimetric device for the direct and simultaneous measurement of soil ion bioavailability (Na⁺, K⁺), temperature, and humidity. Designed for Arduino integration, the device offers scalable, cost-effective deployment. Different AI algorithms were trained to interpret signals (Support Vector Machine, Random Forest, XBoost), enabling real-time monitoring. Best performance was achieved for XBoost. Calibration was first performed using solutions of known NaCl and KCl concentrations to establish impedance patterns, and benchmarking against fitted Cole model outputs demonstrated high predictive accuracy (R² = 0.99 for both Na⁺ and K⁺). The system operated across a 1–100 kHz impedance range with environmental resolution of ±0.5 °C, ±3% RH, and ±1 hPa, acquiring data every 10 min during in vivo trials. This affordable, AI-enhanced platform has the potential to empower smallholder farmers by reducing reliance on costly laboratory analyses, enabling precise fertiliser application, and integrating seamlessly into smart farming platforms for sustainable yield improvement. Full article

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Open AccessProceeding Paper

Rapid Route to Lab-on-Chip (LOC) Prototype Fabrication with Limited Resources

by Manfred Scriba, Masibulele Kakaza, Eldas Maesela and Vusani Mandiwana

Eng. Proc. 2025, 109(1), 4; https://doi.org/10.3390/engproc2025109004 - 10 Sep 2025

Abstract

Several approaches to producing lab-on-chip (LOC) devices have been developed in the last 20 years, including laser cutting of acrylic sheets and laminating them with adhesive films. While this route allows for rapid manufacture of devices, it cannot be scaled up beyond a [...] Read more.

Several approaches to producing lab-on-chip (LOC) devices have been developed in the last 20 years, including laser cutting of acrylic sheets and laminating them with adhesive films. While this route allows for rapid manufacture of devices, it cannot be scaled up beyond a couple of prototypes. For mass production of 3D LOC devices, injection molding is required, but mold manufacturing can be very costly. In this work we briefly report laser cutting parameters and lamination approaches, as well as 3D-printed injection mold inserts that allow one to produce LOC prototypes in facilities that have limited resources. This allows these facilities to transition from a couple of demonstrators to more than 100 devices in a short time and with limited costs. Full article

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20 pages, 2230 KB

Open AccessProceeding Paper

Synthesis and Analysis of Active Filters Using the Multi-Loop Negative Feedback Method

by Adriana Borodzhieva and Snezhinka Zaharieva

Eng. Proc. 2025, 104(1), 91; https://doi.org/10.3390/engproc2025104091 - 9 Sep 2025

Abstract

This paper offers a comprehensive methodology for the synthesis and analysis of active filters, including low-pass, high-pass, and band-pass configurations, utilizing operational amplifiers and multi-loop negative feedback systems. The approach involves deriving explicit analytical expressions for the design and optimization of eight distinct [...] Read more.

This paper offers a comprehensive methodology for the synthesis and analysis of active filters, including low-pass, high-pass, and band-pass configurations, utilizing operational amplifiers and multi-loop negative feedback systems. The approach involves deriving explicit analytical expressions for the design and optimization of eight distinct filter circuit solutions: one low-pass, one high-pass, and six band-pass filters with varying specifications. These derivations include the calculation of normalized and denormalized component values (resistors and capacitors), enabling precise tuning and practical implementation of the filters. Furthermore, the methodology encompasses the determination of key filter parameters such as passband gain, pole quality factor (Q-factor), and cut-off/center frequency, after selecting standard resistor and capacitor values suitable for the target application. The analytical framework facilitates a systematic approach to filter design, ensuring that the resulting circuits meet specific frequency response criteria while maintaining optimal stability and performance. The proposed methodology can be effectively applied in the development of various active filtering systems for signal processing, communication, and instrumentation, offering engineers a reliable foundation for designing high-performance, tailored filter solutions. Full article

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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

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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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

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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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

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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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

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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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

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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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

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)

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

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, 1012 KB

Open AccessProceeding Paper

Design and Implementation of Wireless Detection Network for Bridge Inspection

by Zhensong Ni, Shuri Cai, Cairong Ni, Baojia Lin and Liyao Li

Eng. Proc. 2025, 108(1), 40; https://doi.org/10.3390/engproc2025108040 - 9 Sep 2025

Abstract

The construction of a wireless detection network for bridge inspection is important in intelligent infrastructure management. Advanced wireless communication technology and a sensor network enable the real-time remote and accurate monitoring of bridge structure health. We designed a protocol and implemented it in [...] Read more.

The construction of a wireless detection network for bridge inspection is important in intelligent infrastructure management. Advanced wireless communication technology and a sensor network enable the real-time remote and accurate monitoring of bridge structure health. We designed a protocol and implemented it in a wireless detection network to overcome the limitations of traditional bridge health monitoring methods. The network improves the efficiency and accuracy of monitoring and ensures safe bridge maintenance. We analyzed the requirements of bridge monitoring, including the strict requirements for high-precision data acquisition, low delay transmission, energy efficiency and network reliability. Full article

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18 pages, 1181 KB

Open AccessProceeding Paper

Advancements in Optical Biosensor Technology for Food Safety and Quality Assurance

by Pabina Rani Boro, Partha Protim Borthakur and Elora Baruah

Eng. Proc. 2025, 106(1), 6; https://doi.org/10.3390/engproc2025106006 - 9 Sep 2025

Abstract

Optical biosensors have emerged as a transformative technology for food safety monitoring. These devices combine biorecognition molecules with advanced optical transducers, enabling the detection of a wide array of food contaminants, including pathogens, toxins, pesticides, and antibiotic residues. This review comprehensively explores the [...] Read more.

Optical biosensors have emerged as a transformative technology for food safety monitoring. These devices combine biorecognition molecules with advanced optical transducers, enabling the detection of a wide array of food contaminants, including pathogens, toxins, pesticides, and antibiotic residues. This review comprehensively explores the principles, advancements, applications, and future trends of optical biosensors in ensuring food safety. The key advantages of optical biosensors, such as high sensitivity to trace contaminants, fast response times, and portability, make them an attractive alternative to traditional analytical methods. Types of optical biosensors discussed include surface plasmon resonance (SPR), interferometric, fluorescence and chemiluminescence, and colorimetric biosensors. SPR biosensors stand out for their real-time, label-free analysis of foodborne pathogens and contaminants, while fluorescence and chemiluminescence biosensors offer exceptional sensitivity for detecting low levels of toxins. Interferometric and colorimetric biosensors, characterized by their portability and visual signal output, are well-suited for field-based applications. Biosensors have proven invaluable in monitoring heavy metals, pesticide residues, and antibiotic contaminants, ensuring compliance with stringent food safety standards. The integration of nanotechnology has further enhanced the performance of optical biosensors, with nanomaterials such as quantum dots and nanoparticles enabling ultra-sensitive detection and signal amplification. Optical biosensors represent a vital advancement in the field of food safety, addressing critical public health concerns through their rapid and reliable detection capabilities. Continued interdisciplinary efforts in nanotechnology, material science, and device engineering are poised to further expand their applications, making them indispensable tools for safeguarding global food supply chains. Full article

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7 pages, 394 KB

Open AccessProceeding Paper

An Approach to Prediction Using Networked Multimedia

by Vladislav Hinkov and Georgi Krastev

Eng. Proc. 2025, 104(1), 90; https://doi.org/10.3390/engproc2025104090 - 8 Sep 2025

Abstract

One of the tasks of statistical analysis is related to the development of forecasts with different horizons. The results of modeling the development trend can also be used for prognostic purposes. At the same time, the assumption is made that during the forecast [...] Read more.

One of the tasks of statistical analysis is related to the development of forecasts with different horizons. The results of modeling the development trend can also be used for prognostic purposes. At the same time, the assumption is made that during the forecast period, the phenomenon under study will exhibit the same patterns of development that it exhibited during the base period. Network multimedia is a unifying link in the parallel development of multimedia and communication technologies. The integrated interaction of technological solutions in the field of multimedia and computer networks is a condition for achieving a greater final application effect in the presentation of information. Experimental studies of modern network multimedia in operational conditions are important for revealing bottlenecks in their functioning. On this basis, recommendations can be made to improve performance indicators, such as performance, reliability, mode of service, etc. This publication is devoted to the experimental study of the trend and the possibility of predicting network multimedia with time series. The implemented algorithm for automated trend determination examines pre-set different trends–linear, quadratic, cubic, hyperbolic, fractional-rational, logarithmic, exponential, exponential, combined–and chooses the most effective of them. Full article

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Engineering Proceedings

Vol. 107

ETLTC 2025

Vol. 102

ITS APF 2025

Vol. 100

TechSys 2025

Vol. 99

EEM 2025

Vol. 98

SSIM 2024

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