Computers

24 pages, 2642 KiB

Open AccessArticle

Identification of Scientific Texts Generated by Large Language Models Using Machine Learning

by David Soto-Osorio, Grigori Sidorov, Liliana Chanona-Hernández and Blanca Cecilia López-Ramírez

Computers 2024, 13(12), 346; https://doi.org/10.3390/computers13120346 - 19 Dec 2024

Large language models (LLMs) are tools that help us in a variety of activities, from creating well-structured texts to quickly consulting information. But as these new technologies are so easily accessible, many people use them for their own benefit without properly citing the [...] Read more.

Large language models (LLMs) are tools that help us in a variety of activities, from creating well-structured texts to quickly consulting information. But as these new technologies are so easily accessible, many people use them for their own benefit without properly citing the original author, or in other cases the student sector can be heavily compromised because students may opt for a quick answer over understanding and comprehending a specific topic in depth, considerably reducing their basic writing, editing and reading comprehension skills. Therefore, we propose to create a model to identify texts produced by LLM. To do so, we will use natural language processing (NLP) and machine-learning algorithms to recognize texts that mask LLM misuse using different types of adversarial attack, like paraphrasing or translation from one language to another. The main contributions of this work are to identify the texts generated by the large language models, and for this purpose several experiments were developed looking for the best results implementing the f1, accuracy, recall and precision metrics, together with PCA and t-SNE diagrams to see the classification of each one of the texts. Full article

(This article belongs to the Special Issue When Natural Language Processing Meets Machine Learning—Opportunities, Challenges and Solutions)

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35 pages, 534 KiB

Open AccessArticle

Blockchain-Enabled Pension System Innovations: A Hungarian Case Study on Business Process Management Integration

by Dániel Kovács, Bálint Molnár and Viktor Weininger

Computers 2024, 13(12), 345; https://doi.org/10.3390/computers13120345 - 18 Dec 2024

Abstract

This paper explores the integration of Business Process Management (BPM) with blockchain technology to enhance pension systems, using Hungary as a case study. Specifically, it addresses scientific challenges related to data access management, regulatory compliance, and system scalability within blockchain-based pension frameworks. This [...] Read more.

This paper explores the integration of Business Process Management (BPM) with blockchain technology to enhance pension systems, using Hungary as a case study. Specifically, it addresses scientific challenges related to data access management, regulatory compliance, and system scalability within blockchain-based pension frameworks. This study investigates how BPM can improve the transparency, efficiency, and security of blockchain applications in pension administration by optimizing workflows and automating compliance with regulations such as GDPR. By analyzing operational flow diagrams and implementing architectural models, this paper presents an innovative approach to pension management, demonstrating significant improvements in service quality and operational efficiency. Findings from this research provide empirical evidence of the benefits of BPM-enhanced blockchain systems, offering insights applicable to pension systems beyond the Hungarian context, including examples from other countries. Full article

(This article belongs to the Special Issue Harnessing the Blockchain Technology in Unveiling Futuristic Applications)

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18 pages, 1302 KiB

Open AccessArticle

Applying Classification Techniques in Machine Learning to Predict Job Satisfaction of University Professors: A Sociodemographic and Occupational Perspective

by Carlos Alberto Espinosa-Pinos, Paúl Bladimir Acosta-Pérez and Camila Alessandra Valarezo-Calero

Computers 2024, 13(12), 344; https://doi.org/10.3390/computers13120344 - 17 Dec 2024

Abstract

This article investigates the factors that affect the job satisfaction of university teachers for which 400 teachers from 4 institutions (public and private) in Ecuador were stratified, resulting in a total of 1600 data points collected through online forms. The research was of [...] Read more.

This article investigates the factors that affect the job satisfaction of university teachers for which 400 teachers from 4 institutions (public and private) in Ecuador were stratified, resulting in a total of 1600 data points collected through online forms. The research was of a cross-sectional design and quantitative and used machine learning techniques of classification and prediction to analyze variables such as ethnic identity, field of knowledge, gender, number of children, job burnout, perceived stress, and occupational risk. The results indicate that the best classification model is neural networks with a precision of 0.7304; the most significant variables for predicting the job satisfaction of university teachers are: the number of children they have, scores related to perceived stress, professional risk, and burnout, province of the university at which the university teacher surveyed works, and city where the teacher works. This is in contrast to marital status, which does not contribute to its prediction. These findings highlight the need for inclusive policies and effective strategies to improve teacher well-being in the university academic environment. Full article

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29 pages, 4651 KiB

Open AccessArticle

Hybrid Vision Transformer and Convolutional Neural Network for Multi-Class and Multi-Label Classification of Tuberculosis Anomalies on Chest X-Ray

by Rizka Yulvina, Stefanus Andika Putra, Mia Rizkinia, Arierta Pujitresnani, Eric Daniel Tenda, Reyhan Eddy Yunus, Dean Handimulya Djumaryo, Prasandhya Astagiri Yusuf and Vanya Valindria

Computers 2024, 13(12), 343; https://doi.org/10.3390/computers13120343 - 17 Dec 2024

Abstract

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading cause of global mortality. While TB detection can be performed through chest X-ray (CXR) analysis, numerous studies have leveraged AI to automate and enhance the diagnostic process. However, existing approaches often focus on partial [...] Read more.

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading cause of global mortality. While TB detection can be performed through chest X-ray (CXR) analysis, numerous studies have leveraged AI to automate and enhance the diagnostic process. However, existing approaches often focus on partial or incomplete lesion detection, lacking comprehensive multi-class and multi-label solutions for the full range of TB-related anomalies. To address this, we present a hybrid AI model combining vision transformer (ViT) and convolutional neural network (CNN) architectures for efficient multi-class and multi-label classification of 14 TB-related anomalies. Using 133 CXR images from Dr. Cipto Mangunkusumo National Central General Hospital and 214 images from the NIH datasets, we tackled data imbalance with augmentation, class weighting, and focal loss. The model achieved an accuracy of 0.911, a loss of 0.285, and an AUC of 0.510. Given the complexity of handling not only multi-class but also multi-label data with imbalanced and limited samples, the AUC score reflects the challenging nature of the task rather than any shortcoming of the model itself. By classifying the most distinct TB-related labels in a single AI study, this research highlights the potential of AI to enhance both the accuracy and efficiency of detecting TB-related anomalies, offering valuable advancements in combating this global health burden. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)

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24 pages, 7294 KiB

Open AccessArticle

Augmented Reality for Event Promotion

by Tiago Lameirão, Miguel Melo and Filipe Pinto

Computers 2024, 13(12), 342; https://doi.org/10.3390/computers13120342 - 16 Dec 2024

Abstract

This article presents the development of an augmented reality (AR) application aimed at promoting events in urban environments. The main goal of the project was to create an immersive experience that enhances user interaction with their surroundings, leveraging AR technology. The application was [...] Read more.

This article presents the development of an augmented reality (AR) application aimed at promoting events in urban environments. The main goal of the project was to create an immersive experience that enhances user interaction with their surroundings, leveraging AR technology. The application was built using Django Rest Framework (DRF) for backend services and Unity for the AR functionalities and frontend. Key features include user registration and authentication, event viewing, interaction with virtual characters, and feedback on attended events, providing an engaging platform to promote urban events. The development process involved several stages, from requirements analysis and system architecture design to implementation and testing. A series of tests were performed, confirming that the application meets its objectives. These tests highlighted the system’s ability to enhance user interaction with urban environments and demonstrated its potential for commercialization. The results suggest that the AR application contributes to innovation in smart cities, offering a new avenue for promoting events and engaging local communities. Future work will focus on refining the user experience and expanding the app’s functionality to support more complex event scenarios. Full article

(This article belongs to the Special Issue Extended or Mixed Reality (AR + VR): Technology and Applications)

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18 pages, 6340 KiB

Open AccessArticle

Identifying Bias in Deep Neural Networks Using Image Transforms

by Sai Teja Erukude, Akhil Joshi and Lior Shamir

Computers 2024, 13(12), 341; https://doi.org/10.3390/computers13120341 - 15 Dec 2024

Abstract

CNNs have become one of the most commonly used computational tools in the past two decades. One of the primary downsides of CNNs is that they work as a “black box”, where the user cannot necessarily know how the image data are analyzed, [...] Read more.

CNNs have become one of the most commonly used computational tools in the past two decades. One of the primary downsides of CNNs is that they work as a “black box”, where the user cannot necessarily know how the image data are analyzed, and therefore needs to rely on empirical evaluation to test the efficacy of a trained CNN. This can lead to hidden biases that affect the performance evaluation of neural networks, but are difficult to identify. Here we discuss examples of such hidden biases in common and widely used benchmark datasets, and propose techniques for identifying dataset biases that can affect the standard performance evaluation metrics. One effective approach to identify dataset bias is to perform image classification by using merely blank background parts of the original images. However, in some situations, a blank background in the images is not available, making it more difficult to separate foreground or contextual information from the bias. To overcome this, we propose a method to identify dataset bias without the need to crop background information from the images. The method is based on applying several image transforms to the original images, including Fourier transform, wavelet transforms, median filter, and their combinations. These transforms are applied to recover background bias information that CNNs use to classify images. These transformations affect the contextual visual information in a different manner than it affects the systemic background bias. Therefore, the method can distinguish between contextual information and the bias, and can reveal the presence of background bias even without the need to separate sub-image parts from the blank background of the original images. The code used in the experiments is publicly available. Full article

(This article belongs to the Special Issue Feature Papers in Computers 2024)

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16 pages, 240 KiB

Open AccessArticle

A Comparative Study of Sentiment Analysis on Customer Reviews Using Machine Learning and Deep Learning

by Logan Ashbaugh and Yan Zhang

Computers 2024, 13(12), 340; https://doi.org/10.3390/computers13120340 - 15 Dec 2024

Abstract

Sentiment analysis is a key technique in natural language processing that enables computers to understand human emotions expressed in text. It is widely used in applications such as customer feedback analysis, social media monitoring, and product reviews. However, sentiment analysis of customer reviews [...] Read more.

Sentiment analysis is a key technique in natural language processing that enables computers to understand human emotions expressed in text. It is widely used in applications such as customer feedback analysis, social media monitoring, and product reviews. However, sentiment analysis of customer reviews presents unique challenges, including the need for large datasets and the difficulty in accurately capturing subtle emotional nuances in text. In this paper, we present a comparative study of sentiment analysis on customer reviews using both deep learning and traditional machine learning techniques. The deep learning models include Convolutional Neural Network (CNN) and Recursive Neural Network (RNN), while the machine learning methods consist of Logistic Regression, Random Forest, and Naive Bayes. Our dataset is composed of Amazon product reviews, where we utilize the star rating as a proxy for the sentiment expressed in each review. Through comprehensive experiments, we assess the performance of each model in terms of accuracy and effectiveness in detecting sentiment. This study provides valuable insights into the strengths and limitations of both deep learning and traditional machine learning approaches for sentiment analysis. Full article

(This article belongs to the Special Issue When Natural Language Processing Meets Machine Learning—Opportunities, Challenges and Solutions)

23 pages, 8899 KiB

Open AccessArticle

Loading Frequency Classification in Shape Memory Alloys: A Machine Learning Approach

by Dmytro Tymoshchuk, Oleh Yasniy, Pavlo Maruschak, Volodymyr Iasnii and Iryna Didych

Computers 2024, 13(12), 339; https://doi.org/10.3390/computers13120339 - 14 Dec 2024

Abstract

This paper investigates the use of machine learning methods to predict the loading frequency of shape memory alloys (SMAs) based on experimental data. SMAs, in particular nickel-titanium (NiTi) alloys, have unique properties that restore the original shape after significant deformation. The frequency of [...] Read more.

This paper investigates the use of machine learning methods to predict the loading frequency of shape memory alloys (SMAs) based on experimental data. SMAs, in particular nickel-titanium (NiTi) alloys, have unique properties that restore the original shape after significant deformation. The frequency of loading significantly affects the functional characteristics of SMAs. Experimental data were obtained from cyclic tensile tests of a 1.5 mm diameter Ni_55.8Ti_44.2 wire at different loading frequencies (0.1, 0.5, 1.0, and 5.0 Hz). Various machine learning methods were used to predict the loading frequency f (Hz) based on input parameters such as stress σ (MPa), number of cycles N, strain ε (%), and loading–unloading stage: boosted trees, random forest, support vector machines, k-nearest neighbors, and artificial neural networks of the MLP type. Experimental data of 100–140 load–unload cycles for four load frequencies were used for training. The dataset contained 13,365 elements. The results showed that the MLP neural network model demonstrated the highest accuracy in load frequency classification. The boosted trees and random forest models also performed well, although slightly below MLP. The SVM method also performed quite well. The KNN method showed the worst results among all models. Additional testing of the MLP model on cycles that were not included in the training data (200th, 300th, and 1035th cycles) showed that the model retains high efficiency in predicting load frequency, although the accuracy gradually decreases on later cycles due to the accumulation of structural changes in the material. Full article

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18 pages, 5030 KiB

Open AccessArticle

Design and Development of a Low-Cost Educational Platform for Investigating Human-Centric Lighting (HCL) Settings

by George K. Adam and Aris Tsangrassoulis

Computers 2024, 13(12), 338; https://doi.org/10.3390/computers13120338 - 14 Dec 2024

Abstract

The design of reliable and accurate indoor lighting control systems for LEDs’ (light-emitting diodes) color temperature and brightness, in an effort to affect human circadian rhythms, has been emerging in the last few years. However, this is quite challenging since parameters, such as [...] Read more.

The design of reliable and accurate indoor lighting control systems for LEDs’ (light-emitting diodes) color temperature and brightness, in an effort to affect human circadian rhythms, has been emerging in the last few years. However, this is quite challenging since parameters, such as the melanopic equivalent daylight illuminance (mEDI), have to be evaluated in real time, using illuminance values and the spectrum of incident light. In this work, to address these issues, a prototype platform has been built based on the low-cost and low-power Arduino UNO R4 Wi-Fi BLE (Bluetooth Low Energy) board, which facilitates experiments with a new control approach for LEDs’ correlated color temperature (CCT). Together with the aforementioned platform, the methodology for mEDI calculation using an 11-channel multi-spectral sensor is presented. With proper calibration of the sensor, the visible spectrum can be reconstructed with a resolution of 1 nm, making the estimation of mEDI more accurate. Full article

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13 pages, 615 KiB

Open AccessArticle

Wearable Sensor-Based Behavioral User Authentication Using a Hybrid Deep Learning Approach with Squeeze-and-Excitation Mechanism

by Sakorn Mekruksavanich and Anuchit Jitpattanakul

Computers 2024, 13(12), 337; https://doi.org/10.3390/computers13120337 - 14 Dec 2024

Abstract

Behavior-based user authentication has arisen as a viable method for strengthening cybersecurity in an age of pervasive wearable and mobile technologies. This research introduces an innovative approach for ongoing user authentication via behavioral biometrics obtained from wearable sensors. We present a hybrid deep [...] Read more.

Behavior-based user authentication has arisen as a viable method for strengthening cybersecurity in an age of pervasive wearable and mobile technologies. This research introduces an innovative approach for ongoing user authentication via behavioral biometrics obtained from wearable sensors. We present a hybrid deep learning network called SE-DeepConvNet, which integrates a squeeze-and-excitation (SE) method to proficiently simulate and authenticate user behavior characteristics. Our methodology utilizes data collected by wearable sensors, such as accelerometers, gyroscopes, and magnetometers, to obtain a thorough behavioral appearance. The suggested network design integrates convolutional neural networks for spatial feature extraction, while the SE blocks improve feature identification by flexibly recalibrating channel-wise feature responses. Experiments performed on two datasets, HMOG and USC-HAD, indicate the efficacy of our technique across different tasks. In the HMOG dataset, SE-DeepConvNet attains a minimal equal error rate (EER) of 0.38% and a maximum accuracy of 99.78% for the Read_Walk activity. Our model presents outstanding authentication (0% EER, 100% accuracy) for various walking activities in the USC-HAD dataset, encompassing intricate situations such as ascending and descending stairs. These findings markedly exceed existing deep learning techniques, demonstrating the promise of our technology for secure and inconspicuous continuous authentication in wearable devices. The suggested approach demonstrates the potential for use in individual device security, access management, and ongoing uniqueness verification in sensitive settings. Full article

(This article belongs to the Special Issue Computational Science and Its Applications 2024 (ICCSA 2024))

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39 pages, 4608 KiB

Open AccessReview

The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection

by Momina Liaqat Ali and Zhou Zhang

Computers 2024, 13(12), 336; https://doi.org/10.3390/computers13120336 - 14 Dec 2024

Abstract

This paper provides a comprehensive review of the YOLO (You Only Look Once) framework up to its latest version, YOLO 11. As a state-of-the-art model for object detection, YOLO has revolutionized the field by achieving an optimal balance between speed and accuracy. The [...] Read more.

This paper provides a comprehensive review of the YOLO (You Only Look Once) framework up to its latest version, YOLO 11. As a state-of-the-art model for object detection, YOLO has revolutionized the field by achieving an optimal balance between speed and accuracy. The review traces the evolution of YOLO variants, highlighting key architectural improvements, performance benchmarks, and applications in domains such as healthcare, autonomous vehicles, and robotics. It also evaluates the framework’s strengths and limitations in practical scenarios, addressing challenges like small object detection, environmental variability, and computational constraints. By synthesizing findings from recent research, this work identifies critical gaps in the literature and outlines future directions to enhance YOLO’s adaptability, robustness, and integration into emerging technologies. This review provides researchers and practitioners with valuable insights to drive innovation in object detection and related applications. Full article

(This article belongs to the Special Issue Advanced Image Processing and Computer Vision)

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18 pages, 2633 KiB

Open AccessArticle

Software Reliability Prediction Based on Recurrent Neural Network and Ensemble Method

by Wafa Alshehri, Salma Kammoun Jarraya and Arwa Allinjawi

Computers 2024, 13(12), 335; https://doi.org/10.3390/computers13120335 - 13 Dec 2024

Abstract

Software reliability is a crucial factor in determining software quality quantitatively. It is also used to estimate the software testing duration. In software reliability testing, traditional parametric software reliability growth models (SRGMs) are effectively used. Nevertheless, a single parametric model cannot provide accurate [...] Read more.

Software reliability is a crucial factor in determining software quality quantitatively. It is also used to estimate the software testing duration. In software reliability testing, traditional parametric software reliability growth models (SRGMs) are effectively used. Nevertheless, a single parametric model cannot provide accurate predictions in all cases. Moreover, non-parametric models have proven to be efficient for predicting software reliability as alternatives to parametric models. In this paper, we adopted a deep learning method for software reliability testing in computer vision systems. Also, we focused on critical computer vision applications that need high reliability. We propose a new deep learning-based model that is combined and based on the ensemble method to improve the performance of software reliability testing. The experimental results of the new model architecture present fairly accurate predictive capability compared to other existing single Neural Network (NN) based models. Full article

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16 pages, 785 KiB

Open AccessArticle

Information and Computing Ecosystem’s Architecture for Monitoring and Forecasting Natural Disasters

by Valeria Gribova and Dmitry Kharitonov

Computers 2024, 13(12), 334; https://doi.org/10.3390/computers13120334 - 13 Dec 2024

Abstract

Monitoring natural phenomena using a variety of methods to predict disasters is a trend that is growing over time. However, there is a great disunity among methods and means of data analysis, formats and interfaces of storing and providing data, and software and [...] Read more.

Monitoring natural phenomena using a variety of methods to predict disasters is a trend that is growing over time. However, there is a great disunity among methods and means of data analysis, formats and interfaces of storing and providing data, and software and information systems for data processing. As part of a large project to create a planetary observatory that combines data from spatially distributed geosphere monitoring systems, the efforts of leading institutes of the Russian Academy of Sciences are also aimed at creating an information and computing ecosystem to unite researchers processing and analyzing the data obtained. This article provides a brief overview of the current state of publications on information ecosystems in various applied fields, and it also proposes a concept for an ecosystem on a multiagent basis with unique technical features. The concept of the ecosystem includes the following: the ability to function in a heterogeneous environment on federal principles, the parallelization of data processing between agents using Petri nets as a mechanism ensuring the correct execution of data processing scenarios, the concept of georeferenced alarm events requiring ecosystem reactions and possible notification of responsible persons, and multilevel information protection allowing data owners to control access at each stage of information processing. Full article

(This article belongs to the Section Cloud Continuum and Enabled Applications)

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34 pages, 1717 KiB

Open AccessReview

Unveiling the Dynamic Landscape of Digital Forensics: The Endless Pursuit

by Muhammad Sharjeel Zareen, Baber Aslam, Shahzaib Tahir, Imran Rasheed and Fawad Khan

Computers 2024, 13(12), 333; https://doi.org/10.3390/computers13120333 - 11 Dec 2024

Abstract

The invention of transistors in the 1940s marked the beginning of a technological revolution that has impacted every aspect of our lives. However, along with the positive advancements, the malicious use of computing technologies has become a serious concern. The international community has [...] Read more.

The invention of transistors in the 1940s marked the beginning of a technological revolution that has impacted every aspect of our lives. However, along with the positive advancements, the malicious use of computing technologies has become a serious concern. The international community has been actively collaborating to develop digital forensics techniques to combat the unlawful use of these technologies. However, the evolution of digital forensics has often lagged behind the rapid developments in computing technologies. In addition to their harmful use, computing devices are increasingly involved in crime scenes and accidents, necessitating digital forensics to reconstruct events. This paper provides a comprehensive review of the development of computing technologies from the 1940s to the present, highlighting the trends in their malicious use and the corresponding advancements in digital forensics. The paper also discusses various institutes, laboratories, organizations, and training setups established at national and international levels for digital forensics purposes. Furthermore, it explores the initial legislations related to computer-related crimes and the standards associated with digital forensics. These reviews and discussions conclude at identifying the shortfalls in digital forensics and proposes an all-inclusive digital forensics process model meeting these shortfalls while complying to international standards and meeting regulatory and legal requirements of digital forensics. Full article

(This article belongs to the Special Issue Cyber Security and Privacy in IoT Era)

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13 pages, 2613 KiB

Open AccessArticle

Player Performance Analysis in Table Tennis Through Human Action Recognition

by Kangnan Dong and Wei Qi Yan

Computers 2024, 13(12), 332; https://doi.org/10.3390/computers13120332 - 11 Dec 2024

Abstract

This paper aims to enhance the effectiveness of table tennis coaching and player performance analysis through human action recognition by using deep learning. In the field of video analysis, human action recognition has emerged as a highly researched area. Beyond post-session analysis, it [...] Read more.

This paper aims to enhance the effectiveness of table tennis coaching and player performance analysis through human action recognition by using deep learning. In the field of video analysis, human action recognition has emerged as a highly researched area. Beyond post-session analysis, it has the potential for real-time applications, such as providing instant feedback or comparing ideal motions with actual player movements. However, the complexity of human actions presents significant challenges. To address these issues, in this paper, we combine the latest computer vision and deep learning algorithms to accurately identify and classify a few table tennis strokes in human action recognition. Through an in-depth review of existing methods, we develop a high-precision offline method for player action recognition. Our experimental results show that the proposed method achieves an average accuracy of 99.85% in recognizing six distinct table tennis actions based on our own dataset. Full article

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24 pages, 3859 KiB

Open AccessArticle

A Coordination Approach to Support Crowdsourced Software-Design Process

by Ohoud Alhagbani and Sultan Alyahya

Computers 2024, 13(12), 331; https://doi.org/10.3390/computers13120331 - 7 Dec 2024

Abstract

Crowdsourcing software design (CSD) is the completion of specific software-design tasks on behalf of a client by a large, unspecified group of external individuals who have the specialized knowledge required by an open call. Although current CSD platforms have provided features to improve [...] Read more.

Crowdsourcing software design (CSD) is the completion of specific software-design tasks on behalf of a client by a large, unspecified group of external individuals who have the specialized knowledge required by an open call. Although current CSD platforms have provided features to improve coordination in the CSD process (such as email notifications, chat, and announcements), these features are insufficient to solve the coordination limitations. A lack of appropriate coordination support in CSD activities may cause delays and missed opportunities for participants, and thus the best quality of design contest results may not be guaranteed. This research aims to support the effective management of the CSD process through identifying the key activity dependencies among participants in CSD platforms and designing a set of process models to provide coordination support through managing this activity. In order to do this, a five-stage approach was used: First, the current CSD process was investigated by reviewing 13 CSD platforms. Second, the review resulted in the identification of 17 possible suggestions to improve CSD. These suggestions were evaluated in stage 3 through distributing a survey to 41 participants who had experience in using platforms in the field of CSD. In stage 4, we designed ten process models that could meet the requirements of suggestions, while in stage 5, we evaluated these process models through interviews with domain experts. The result shows that coordination support in the activities of the CSD can make valuable contributions to the development of CSD platforms. Full article

(This article belongs to the Special Issue Best Practices, Challenges and Opportunities in Software Engineering)

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4 pages, 156 KiB

Open AccessEditorial

Editorial “Blockchain Technology—A Breakthrough Innovation for Modern Industries”

by Nino Adamashvili, Caterina Tricase, Otar Zumburidze, Radu State and Roberto Tonelli

Computers 2024, 13(12), 330; https://doi.org/10.3390/computers13120330 - 7 Dec 2024

Abstract

In June 2022, the Italian national project PRIN (Research Projects of National Relevance), W [...] Full article

(This article belongs to the Special Issue Blockchain Technology—a Breakthrough Innovation for Modern Industries)

28 pages, 1185 KiB

Open AccessReview

Integrating Blockchains with the IoT: A Review of Architectures and Marine Use Cases

by Andreas Polyvios Delladetsimas, Stamatis Papangelou, Elias Iosif and George Giaglis

Computers 2024, 13(12), 329; https://doi.org/10.3390/computers13120329 - 6 Dec 2024

Abstract

This review examines the integration of blockchain technology with the IoT in the Marine Internet of Things (MIoT) and Internet of Underwater Things (IoUT), with applications in areas such as oceanographic monitoring and naval defense. These environments present distinct challenges, including a limited [...] Read more.

This review examines the integration of blockchain technology with the IoT in the Marine Internet of Things (MIoT) and Internet of Underwater Things (IoUT), with applications in areas such as oceanographic monitoring and naval defense. These environments present distinct challenges, including a limited communication bandwidth, energy constraints, and secure data handling needs. Enhancing BIoT systems requires a strategic selection of computing paradigms, such as edge and fog computing, and lightweight nodes to reduce latency and improve data processing in resource-limited settings. While a blockchain can improve data integrity and security, it can also introduce complexities, including interoperability issues, high energy consumption, standardization challenges, and costly transitions from legacy systems. The solutions reviewed here include lightweight consensus mechanisms to reduce computational demands. They also utilize established platforms, such as Ethereum and Hyperledger, or custom blockchains designed to meet marine-specific requirements. Additional approaches incorporate technologies such as fog and edge layers, software-defined networking (SDN), the InterPlanetary File System (IPFS) for decentralized storage, and AI-enhanced security measures, all adapted to each application’s needs. Future research will need to prioritize scalability, energy efficiency, and interoperability for effective BIoT deployment. Full article

(This article belongs to the Special Issue When Blockchain Meets IoT: Challenges and Potentials)

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19 pages, 2545 KiB

Open AccessArticle

Distinguishing Human Journalists from Artificial Storytellers Through Stylistic Fingerprints

by Van Hieu Tran, Yakub Sebastian, Asif Karim and Sami Azam

Computers 2024, 13(12), 328; https://doi.org/10.3390/computers13120328 - 5 Dec 2024

Abstract

Background: Artificial intelligence poses a critical challenge to the authenticity of journalistic documents. Objectives: This research proposes a method to automatically identify AI-generated news articles based on various stylistic features. Methods/Approach: We used machine learning algorithms and trained five classifiers [...] Read more.

Background: Artificial intelligence poses a critical challenge to the authenticity of journalistic documents. Objectives: This research proposes a method to automatically identify AI-generated news articles based on various stylistic features. Methods/Approach: We used machine learning algorithms and trained five classifiers to distinguish journalistic news articles from their AI-generated counterparts based on various lexical, syntactic, and readability features. BERTopic was used to extract salient keywords from these articles, which were then used to prompt Google’s Gemini to generate new artificial articles on the same topic. Results: The Random Forest classifier performed the best on the task (accuracy = 98.3%, precision = 0.984, recall = 0.983, and F1-score = 0.983). Random Forest feature importance, Analysis of Variance (ANOVA), Mutual Information, and Recursive Feature Elimination revealed the top five important features: sentence length range, paragraph length coefficient of variation, verb ratio, sentence complex tags, and paragraph length range. Conclusions: This research introduces an innovative approach to prompt engineering using the BERTopic modelling technique and identifies key stylistic features to distinguish AI-generated content from human-generated content. Therefore, it contributes to the ongoing efforts to combat disinformation, enhancing the credibility of content in various industries, such as academic research, education, and journalism. Full article

(This article belongs to the Special Issue Emerging Trends in Machine Learning and Artificial Intelligence)

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