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Application of Deep Learning for Heart Attack Prediction with Explainable Artificial Intelligence
Assessing the Impact of Prolonged Sitting and Poor Posture on Lower Back Pain: A Photogrammetric and Machine Learning Approach
On Using GeoGebra and ChatGPT for Geometric Discovery

Journal Description

Computers

Computers is an international, scientific, peer-reviewed, open access journal of computer science, including computer and network architecture and computer–human interaction as its main foci, published monthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), dblp, Inspec, and other databases.
Journal Rank: JCR - Q2 (Computer Science, Interdisciplinary Applications) / CiteScore - Q2 (Computer Networks and Communications)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.2 days after submission; acceptance to publication is undertaken in 3.9 days (median values for papers published in this journal in the first half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 2.6 (2023); 5-Year Impact Factor: 2.4 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2073-431X

Latest Articles

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

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

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

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

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

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

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

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

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

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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26 pages, 677 KiB

Open AccessReview

Exploring Data Analysis Methods in Generative Models: From Fine-Tuning to RAG Implementation

by Bogdan Mihai Guțu and Nirvana Popescu

Computers 2024, 13(12), 327; https://doi.org/10.3390/computers13120327 - 5 Dec 2024

The exponential growth in data from technological advancements has created opportunities across fields like healthcare, finance, and social media, but sensitive data raise security and privacy challenges. Generative models offer solutions by modeling complex data and generating synthetic data, making them useful for [...] Read more.

The exponential growth in data from technological advancements has created opportunities across fields like healthcare, finance, and social media, but sensitive data raise security and privacy challenges. Generative models offer solutions by modeling complex data and generating synthetic data, making them useful for the analysis of large private datasets. This article is a review of data analysis techniques based on generative models, with a focus on large language models (LLMs). It covers the strengths, limitations, and applications of methods like the fine-tuning of LLMs and retrieval-augmented generation (RAG). This study consolidates, analyzes, and interprets the findings from the literature to provide a coherent overview of the current research landscape on this topic, aiming to guide effective, privacy-conscious data analysis and exploring future improvements, especially for low-resource languages. Full article

(This article belongs to the Special Issue Generative Artificial Intelligence and Machine Learning in Industrial Processes and Manufacturing)

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17 pages, 1195 KiB

Open AccessReview

Exploring the Design for Wearability of Wearable Devices: A Scoping Review

by Yeo Weon Seo, Valentina La Marca, Animesh Tandon, Jung-Chih Chiao and Colin K. Drummond

Computers 2024, 13(12), 326; https://doi.org/10.3390/computers13120326 - 5 Dec 2024

Wearable smart devices have become ubiquitous in modern society, extensively researched for their health monitoring capabilities and convenience features. However, the “wearability” of these devices remains a relatively understudied area, particularly in terms of design informed by clinical trials. Wearable devices possess significant [...] Read more.

Wearable smart devices have become ubiquitous in modern society, extensively researched for their health monitoring capabilities and convenience features. However, the “wearability” of these devices remains a relatively understudied area, particularly in terms of design informed by clinical trials. Wearable devices possess significant potential to enhance daily life, yet their success depends on understanding and validating the design factors that influence comfort, usability, and seamless integration into everyday routines. This review aimed to evaluate the “wearability” of smart devices through a mixed-methods scoping literature review. By analyzing studies on comfort, usability, and daily integration, it sought to identify design improvements and research gaps to enhance user experience and system design. From an initial pool of 130 publications (1998–2024), 19 studies met the inclusion criteria. The review identified three significant outcomes: (1) a lack of standardized assessment methods, (2) the predominance of qualitative over quantitative assessments, and (3) limited utility of findings for informing design. Although qualitative studies provide valuable insights, the absence of quantitative research hampers the development of validated, generalizable design criteria. This underscores the urgent need for future studies to adopt robust quantitative methodologies to better assess wearability and inform evidence-based design strategies. Full article

(This article belongs to the Special Issue Wearable Computing and Activity Recognition)

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25 pages, 1610 KiB

Open AccessArticle

A Novel End-to-End Provenance System for Predictive Maintenance: A Case Study for Industrial Machinery Predictive Maintenance

by Emrullah Gultekin and Mehmet S. Aktas

Computers 2024, 13(12), 325; https://doi.org/10.3390/computers13120325 - 4 Dec 2024

In this study, we address the critical gap in predictive maintenance systems regarding the absence of a robust provenance system and specification. To tackle this issue, we propose a provenance system based on the PROV-O schema, designed to enhance explainability, accountability, and transparency [...] Read more.

In this study, we address the critical gap in predictive maintenance systems regarding the absence of a robust provenance system and specification. To tackle this issue, we propose a provenance system based on the PROV-O schema, designed to enhance explainability, accountability, and transparency in predictive maintenance processes. Our framework facilitates the collection, processing, recording, and visualization of provenance data, integrating them seamlessly into these systems. We developed a prototype to evaluate the effectiveness of our approach and conducted comprehensive user studies to assess the system’s usability. Participants found the extended PROV-O structure valuable, with improved task completion times. Furthermore, performance tests demonstrated that our system manages high workloads efficiently, with minimal overhead. The contributions of this study include the design of a provenance system tailored for predictive maintenance and a specification that ensures scalability and efficiency. Full article

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

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14 pages, 270 KiB

Open AccessArticle

The Effects of Adaptive Gamification in Science Learning: A Comparison Between Traditional Inquiry-Based Learning and Gender Differences

by Alkinoos-Ioannis Zourmpakis, Michail Kalogiannakis and Stamatios Papadakis

Computers 2024, 13(12), 324; https://doi.org/10.3390/computers13120324 - 4 Dec 2024

Gamification has become a topic of interest for researchers and educators, particularly in science education, in the last few years. Students of all educational levels have consistently faced challenges when grasping scientific concepts. However, the effectiveness of gamification, especially in terms of academic [...] Read more.

Gamification has become a topic of interest for researchers and educators, particularly in science education, in the last few years. Students of all educational levels have consistently faced challenges when grasping scientific concepts. However, the effectiveness of gamification, especially in terms of academic performance, has shown mixed results. This has led researchers to explore a new alternative approach, adaptive gamification. Our study compared the effects of adaptive gamification with traditional inquiry-based learning. Two classes of 9-year-old students participated, with the experimental group using adaptive gamification and the control group following a more conventional teaching approach using inquiry-based lessons and experiments. Both groups were tested before and after the lessons, and their results were analyzed using SPSS. The findings revealed that while both groups showed a significant difference after the lessons, the experimental group had significantly higher scores than the control group. Particularly significant results were observed regarding learning improvements based on students’ gender, with female and male students in the experimental group demonstrating significant improvement. In contrast, in the control group, only the male students displayed significant learning improvement. This research contributes significantly to the relatively new field of adaptive gamification in science education and the improvement of students’ science learning, particularly in the context of gender differences. Full article

(This article belongs to the Special Issue Gamification and Serious Games Applications in Immersive Learning Environments)

15 pages, 1050 KiB

Open AccessArticle

Siamese Network-Based Lightweight Framework for Tomato Leaf Disease Recognition

by Selvarajah Thuseethan, Palanisamy Vigneshwaran, Joseph Charles and Chathrie Wimalasooriya

Computers 2024, 13(12), 323; https://doi.org/10.3390/computers13120323 - 4 Dec 2024

In this paper, a novel Siamese network-based lightweight framework is proposed for automatic tomato leaf disease recognition. This framework achieves the highest accuracy of 96.97% on the tomato subset obtained from the PlantVillage dataset and 95.48% on the Taiwan tomato leaf disease dataset. [...] Read more.

In this paper, a novel Siamese network-based lightweight framework is proposed for automatic tomato leaf disease recognition. This framework achieves the highest accuracy of 96.97% on the tomato subset obtained from the PlantVillage dataset and 95.48% on the Taiwan tomato leaf disease dataset. Experimental results further confirm that the proposed framework is effective with imbalanced and small data. The backbone network integrated with this framework is lightweight with approximately 2.9629 million trainable parameters, which is second to SqueezeNet and significantly lower than other lightweight deep networks. Automatic tomato disease recognition from leaf images is vital to avoid crop losses by applying control measures on time. Even though recent deep learning-based tomato disease recognition methods with classical training procedures showed promising recognition results, they demand large labeled data and involve expensive training. The traditional deep learning models proposed for tomato disease recognition also consume high memory and storage because of a high number of parameters. While lightweight networks overcome some of these issues to a certain extent, they continue to show low performance and struggle to handle imbalanced data. Full article

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

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15 pages, 976 KiB

Open AccessArticle

Computer-Supported Strategic Decision Making for Ecosystems Creation

by Patricia Rodriguez-Garcia, Patricia Carracedo, David Lopez-Lopez, Angel A. Juan and Jon A. Martin

Computers 2024, 13(12), 322; https://doi.org/10.3390/computers13120322 - 4 Dec 2024

In the corporate strategy arena, the concept of ecosystems has emerged as a transformative approach to promote competitive advantage, growth, and innovation. Corporate ecosystems enable companies to benefit from interconnections among diverse partners, products, and services to deliver enhanced value to customers. However, [...] Read more.

In the corporate strategy arena, the concept of ecosystems has emerged as a transformative approach to promote competitive advantage, growth, and innovation. Corporate ecosystems enable companies to benefit from interconnections among diverse partners, products, and services to deliver enhanced value to customers. However, the process of ecosystem creation represents a significant challenge for CEOs, as they must analyze a wide number of alternative sectors, partners, business cases, and other critical elements. Particularly, as it is a strategic decision, it lies beyond the traditional approach of risk-return by incorporating other factors, e.g.: the feasibility, desirability and sustainability of each alternative. This paper investigates how computer-supported optimization algorithms can help to solve the complex problem faced by CEOs when making these factors to create a successful and sustainable ecosystem. The paper shows how a CEO can make informed strategic decisions by identifying the best projects to include in the ecosystem portfolio, balancing financial risk and return with technical feasibility, customer appeal, and technical considerations. Full article

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25 pages, 2551 KiB

Open AccessArticle

Optimizing Scheduled Virtual Machine Requests Placement in Cloud Environments: A Tabu Search Approach

by Mohamed Koubàa, Abdullah S. Karar and Faouzi Bahloul

Computers 2024, 13(12), 321; https://doi.org/10.3390/computers13120321 - 2 Dec 2024

This paper introduces a novel model for virtual machine (VM) requests with predefined start and end times, referred to as scheduled virtual machine demands (SVMs). In cloud computing environments, SVMs represent anticipated resource requirements derived from historical data, usage trends, and predictive analytics, [...] Read more.

This paper introduces a novel model for virtual machine (VM) requests with predefined start and end times, referred to as scheduled virtual machine demands (SVMs). In cloud computing environments, SVMs represent anticipated resource requirements derived from historical data, usage trends, and predictive analytics, allowing cloud providers to optimize resource allocation for maximum efficiency. Unlike traditional VMs, SVMs are not active concurrently. This allows providers to reuse physical resources such as CPU, RAM, and storage for time-disjoint requests, opening new avenues for optimizing resource distribution in data centers. To leverage this opportunity, we propose an advanced VM placement algorithm designed to maximize the number of hosted SVMs in cloud data centers. We formulate the SVM placement problem (SVMPP) as a combinatorial optimization challenge and introduce a tailored Tabu Search (TS) meta-heuristic to provide an effective solution. Our algorithm demonstrates significant improvements over existing placement methods, achieving up to a 15% increase in resource efficiency compared to baseline approaches. This advancement highlights the TS algorithm’s potential to deliver substantial scalability and optimization benefits, particularly for high-demand scenarios, albeit with a necessary consideration for computational cost. Full article

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

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17 pages, 3922 KiB

Open AccessArticle

Hybrid Population-Based Hill Climbing Algorithm for Generating Highly Nonlinear S-boxes

by Oleksandr Kuznetsov, Nikolay Poluyanenko, Kateryna Kuznetsova, Emanuele Frontoni and Marco Arnesano

Computers 2024, 13(12), 320; https://doi.org/10.3390/computers13120320 - 2 Dec 2024

This paper introduces the hybrid population-based hill-climbing (HPHC) algorithm, a novel approach for generating cryptographically strong S-boxes that combines the efficiency of hill climbing with the exploration capabilities of population-based methods. The algorithm achieves consistent generation of 8-bit S-boxes with a nonlinearity of [...] Read more.

This paper introduces the hybrid population-based hill-climbing (HPHC) algorithm, a novel approach for generating cryptographically strong S-boxes that combines the efficiency of hill climbing with the exploration capabilities of population-based methods. The algorithm achieves consistent generation of 8-bit S-boxes with a nonlinearity of 104, a critical threshold for cryptographic applications. Our approach demonstrates remarkable efficiency, requiring only 49,277 evaluations on average to generate such S-boxes, representing a 600-fold improvement over traditional simulated annealing methods and a 15-fold improvement over recent genetic algorithm variants. We present comprehensive experimental results from extensive parameter space exploration, revealing that minimal populations (often single-individual) combined with moderate mutation rates achieve optimal performance. This paper provides detailed analysis of algorithm behavior, parameter sensitivity, and performance characteristics, supported by rigorous statistical evaluation. We demonstrate that population size should approximate available thread count for optimal parallel execution despite smaller populations being theoretically more efficient. The HPHC algorithm maintains high reliability across diverse parameter settings while requiring minimal computational resources, making it particularly suitable for practical cryptographic applications. Full article

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21 pages, 1831 KiB

Open AccessArticle

Accurate Range-Free Localization Using Cuckoo Search Optimization in IoT and Wireless Sensor Networks

by Abdelali Hadir and Naima Kaabouch

Computers 2024, 13(12), 319; https://doi.org/10.3390/computers13120319 - 2 Dec 2024

Precise positioning of sensors is critical for the performance of various applications in the Internet of Things and wireless sensor networks. The efficiency of these networks heavily depends on the precision of sensor node locations. Among various localization approaches, DV-Hop is highly recommended [...] Read more.

Precise positioning of sensors is critical for the performance of various applications in the Internet of Things and wireless sensor networks. The efficiency of these networks heavily depends on the precision of sensor node locations. Among various localization approaches, DV-Hop is highly recommended for its simplicity and robustness. However, despite its popularity, DV-Hop suffers from significant accuracy issues, primarily due to its reliance on average hop size for distance estimation. This limitation often results in substantial localization errors, compromising the overall network effectiveness. To address this gap, we developed an enhanced DV-Hop approach that integrates the cuckoo search algorithm (CS). Our solution improves the accuracy of node localization by introducing a normalized average hop size calculation and leveraging the optimization capabilities of CS. This hybrid approach refines the distance estimation process, significantly reducing the errors inherent in traditional DV-Hop. Findings from simulations reveal that the developed approach surpasses the accuracy of both the original DV-Hop and multiple other current localization methods, providing a more precise and reliable localization method for IoT and WSN applications. Full article

(This article belongs to the Special Issue Wireless Sensor Network, IoT and Cloud Computing Technologies for Smart Cities)

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31 pages, 3335 KiB

Open AccessArticle

Unified Ecosystem for Data Sharing and AI-Driven Predictive Maintenance in Aviation

by Igor Kabashkin and Vitaly Susanin

Computers 2024, 13(12), 318; https://doi.org/10.3390/computers13120318 - 28 Nov 2024

The aviation industry faces considerable challenges in maintenance management due to the complexities of data standardization, data sharing, and predictive maintenance capabilities. This paper introduces a unified ecosystem for data sharing and AI-driven predictive maintenance designed to address these challenges by integrating real-time [...] Read more.

The aviation industry faces considerable challenges in maintenance management due to the complexities of data standardization, data sharing, and predictive maintenance capabilities. This paper introduces a unified ecosystem for data sharing and AI-driven predictive maintenance designed to address these challenges by integrating real-time and historical data from diverse sources, including aircraft sensors, maintenance logs, and operational records. The proposed ecosystem enables predictive analytics and anomaly detection, enhancing decision-making processes for airlines, maintenance, repair, and overhaul providers, and regulatory bodies. Key elements of the ecosystem include a modular design with feedback loops, scalable AI models for predictive maintenance, and robust data-sharing frameworks. This paper outlines the architecture of a unified aviation maintenance ecosystem built around multiple data sources, including aircraft sensors, maintenance logs, flight data, weather data, and manufacturer specifications. By integrating various components and stakeholders, the system achieves its full potential through several key use cases: monitoring aircraft component health, predicting component failures, receiving maintenance alerts, performing preventive maintenance, and generating compliance reports. Each use case is described in detail and supported by illustrative dataflow diagrams. The findings underscore the transformative impact of such an ecosystem on aviation maintenance practices, marking a significant step toward safer, more efficient, and sustainable aviation operations. Full article

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

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20 pages, 1529 KiB

Open AccessArticle

Dynamic Cohort Formation with Hierarchical Blockchain Using GDP for Enhanced FL

by Sunila Fatima Ahmad, Zahra Abbas, Madiha Haider Syed, Adeel Anjum and Semeen Rehman

Computers 2024, 13(12), 317; https://doi.org/10.3390/computers13120317 - 28 Nov 2024

Federated Learning (FL) can be defined as an effective solution for using the benefits of machine learning (ML) in distributed systems, in which the data of the clients remain protected. However overlaid challenges affecting today’s FL systems are centered on time optimization, scalability, [...] Read more.

Federated Learning (FL) can be defined as an effective solution for using the benefits of machine learning (ML) in distributed systems, in which the data of the clients remain protected. However overlaid challenges affecting today’s FL systems are centered on time optimization, scalability, and security. To these challenges, this paper responds with a new solution comprising the dynamic cohort creation via smart contracts and a hierarchical blockchain approach. Firstly, the research utilizes smart contracts for the dynamic tracking of cohorts in real time and addresses a two-tier blockchain structure for safety and efficiency in storage. In addition, Gaussian Differential Privacy (GDP) is used as a privacy-preserving mechanism that adds controlled noise to the data or model updates to protect individual data points from being inferred by adversaries. The method we are proposing includes four practical steps that include efficient update validation and aggregation; this will enhance training time, and increase model accuracy as well as data confidentiality. The standard dataset is used to show enhanced performance and scalability which validates this method. Based on the above investigations, it could be concluded that the proposed approach improves FL efficiency and creates a new direction in the development of secure, accurate, and scalable ML. The present study indicates that the implementation of blockchain with FL fortified by GDP will establish a novel innovation between intelligent and safe Artificial Intelligence (AI) architecture for safeguarding the privacy of ML system. Full article

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

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15 pages, 657 KiB

Open AccessArticle

Blending Ensemble Learning Model for 12-Lead Electrocardiogram-Based Arrhythmia Classification

by Hai-Long Nguyen, Van Su Pham and Hai-Chau Le

Computers 2024, 13(12), 316; https://doi.org/10.3390/computers13120316 - 27 Nov 2024

The increasing prevalence of heart diseases has driven the development of automated arrhythmia classification systems using machine learning and electrocardiograms (ECGs). This paper presents a novel ensemble learning method for classifying multiple arrhythmia types using 12-lead ECG signals through a blending technique. The [...] Read more.

The increasing prevalence of heart diseases has driven the development of automated arrhythmia classification systems using machine learning and electrocardiograms (ECGs). This paper presents a novel ensemble learning method for classifying multiple arrhythmia types using 12-lead ECG signals through a blending technique. The framework employs a predetermined meta-model from foundation models, while the remaining models serve as potential base estimators, ranked by accuracy. Using sequential forward selection and meta-feature augmentation, the system determines an optimal base estimator set and creates a meta-dataset for the meta-model, which is optimized through grid search with k-fold cross-validation. Experiments conducted with seven diverse machine learning algorithms (Adaptive Boosting, Extreme Gradient Boosting, Decision Trees, k-Nearest Neighbors, Logistic Regression, Random Forest, and Support Vector Machine) demonstrate that the proposed blending solution, utilizing an LR meta-model with three optimal base models, achieves a superior classification accuracy of 96.48%, offering an effective tool for clinical decision support. Full article

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

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