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Optimizing novel thermal energy storage systems: Enhancing melting efficiency with tubes, stands, and advanced machine learning techniques

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Journal of Energy Storage 2025年 124卷

作者： almadhor, Ahmad Basem, ali Singh, Pradeep Kumar Othman, Nashwan Adnan Samad, Sarminah alhomayani, Fahad M. Abduvalieva, Dilsora ali, H. Elhosiny Akram, Muhammad Afzal, Abdul Rahman Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Saudi Arabia Faculty of Engineering Warith Al-Anbiyaa University Karbala56001 Iraq Department of Mechanical Engineering Institute of Engineering & Technology GLA University U.P. Mathura281406 India Department of Computer Engineering College of Engineering Knowledge University Erbil44001 Iraq Department of Computer Engineering Al-Kitab University Altun Kupri Iraq Department of Management College of Business Administration Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia College of Computers and Information Technology Taif University Saudi Arabia Applied College Taif University Saudi Arabia Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor avenue 27 Tashkent100070 Uzbekistan Physics Department Faculty of Science King Khalid University P.O. Box 9004 Abha Saudi Arabia Faculty of Design Informatics and Business University of Abertay Scotland Dundee United Kingdom Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah21361 Saudi Arabia

The transition to renewable energy is crucial to meet growing global energy demands while minimizing environmental impact. Solar energy, a leading renewable source, faces limitations due to its intermittent nature, making energy storage systems essential for continuous supply. Thermal energy storage systems (TESSs) provide a compelling solution, especially by utilizing latent heat storage with phase change materials (PCMs), which efficiently store large amounts of energy. However, PCM-based systems suffer from low thermal conductivity and weak heat transfer performance. Numerous techniques to amend the thermal conductivity of PCMs, whether applied individually or together, show promise for improving the efficiency of TESSs. However, challenges like increased viscosity, decreased latent heat capacity, high costs, and complex manufacturing often limit their practical use. Thus, it is crucial to investigate more straightforward and more cost-effective solutions, like incorporating extended surfaces into the PCM container. In this study, a novel approach is taken to ameliorate the heat transfer surface area in TESSs by incorporating tubes and stands inside the PCM container rather than relying on conventional fins. A new compact TESS design was introduced, featuring fixed tubes supported by aluminum-made stands. Two artificial neural networks (ANN) models were trained with input parameters, including the length of the vertical stands, horizontal stands, and tilted stands, to anticipate the half-melting time and complete melting time of the PCM. In the end, two optimal designs of O-TESS1 and O-TESS2 were proposed by a single-objective optimization, focusing on minimizing the half and complete melting times of the PCM, respectively. A multi-objective optimization was also performed to balance the mentioned objectives. Using the generated data, a Pareto front and TOPSIS-selected designs were developed to visualize the trade-offs between these competing objectives. It took

关键词： Solar heating

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Leveraging explainable artificial intelligence with ensemble of deep learning model for dementia prediction to enhance clinical decision support systems

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Scientific Reports 2025年第1期15卷 1-17页

作者： Medani, Mohamed Elhessewi, Ghada Moh. Samir alqahtani, Mohammed Asklany, Somia A. alamro, Sulaiman albalawneh, Da’ad alshammeri, Menwa Assiri, Mohammed Department of Information Systems Applied College at Mahayil King Khalid University Abha Saudi Arabia Department of Health Sciences College of Health and Rehabilitation Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha 61922 Saudi Arabia Department of Computer Science and Information Technology Faculty of Sciences and Arts Northern Border University Turaif Arar 91431 Saudi Arabia Department of Computer Science College of Computer Qassim University Buraydah 51452 Saudi Arabia Department of Computer Science University College in Umluj University of Tabuk Tabuk Saudi Arabia Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University P.O. BOX 16273 Al-Kharj 3963 Saudi Arabia

The prevalence of dementia is growing worldwide due to the fast ageing of the population. Dementia is an intricate illness that is frequently produced by a mixture of genetic and environmental risk factors. There is no treatment for dementia yet;therefore, the early detection and identification of persons at greater risk of emerging dementia becomes crucial, as this might deliver an opportunity to adopt lifestyle variations to decrease the risk of dementia. Many dementia risk prediction techniques to recognize individuals at high risk have progressed in the past few years. Accepting a structure uniting explainability in artificial intelligence (XAI) with intricate systems will enable us to classify analysts of dementia incidence and then verify their occurrence in the survey as recognized or suspected risk factors. Deep learning (DL) and machine learning (ML) are current techniques for detecting and classifying dementia and making decisions without human participation. This study introduces a Leveraging Explainability Artificial Intelligence and Optimization algorithm for Accurate Dementia Prediction and Classification Model (LXAIOA-ADPCM) technique in medical diagnosis. The main intention of the LXAIOA-ADPCM technique is to progress a novel algorithm for dementia prediction using advanced techniques. Initially, data normalization is performed by utilizing min–max normalization to convert input data into a beneficial format. Furthermore, the feature selection process is performed by implementing the naked mole‐rat algorithm (NMRA) model. For the classification process, the proposed LXAIOA-ADPCM model implements ensemble classifiers such as the bidirectional long short-term memory (BiLSTM), sparse autoencoder (SAE), and temporal convolutional network (TCN) techniques. Finally, the hyperparameter selection of ensemble models is accomplished by utilizing the gazelle optimization algorithm (GOA) technique. Finally, the Grad‐CAM is employed as an XAI technique to enhance

关键词： Data normalization Dementia prediction Explainability artificial intelligence Feature selection Gazelle optimization algorithm

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Multi-criteria GIS-based approach for optimal site selection of solar and wind energy

Unconventional Resources

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Unconventional Resources 2025年 7卷

作者： Nassar, Ahmed K. al-Dulaimi, Omer Fakhruldeen, Hassan Falah Sapaev, I.B. Jabbar, Feryal Ibrahim Dawood, Imad Ibrahim Khalaf, Doaa H. algburi, Sameer Policy Planning and Development Program Department of International Affairs College of Arts and Sciences Qatar University Doha Qatar Electrical Technical College Al-Farahidi University Baghdad Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-Sadiq University Baghdad 10011 Iraq Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University Tashkent Uzbekistan Medical Physics Department College of Sciences Al-Mustaqbal University Babil 51001 Iraq Mazaya University College Iraq Design Department Al-Turath University College Baghdad Iraq College of Engineering Al-Kitab University Kirkuk 36015 Iraq

This research introduces a comprehensive multi-criteria geographic information system-based approach designed to determine optimal locations for solar and wind energy development by integrating geographic information system, multi-criteria decision analysis, and the analytical hierarchy process. The methodology enables spatial analysis that considers a wide range of environmental, economic, and technical factors, producing suitability maps that prioritize locations for renewable energy infrastructure. Applied to the geographic and climatic context of Iraq, the approach addresses the countries’ growing energy demands and infrastructure constraints by identifying regions with high renewable energy potential. the analysis resulted in 38.33 % of the total study area being suitable for utility-scale solar photovoltaic installations, 12.44 % for large-scale wind farms, and 34.47 % for small-scale solar photovoltaic projects. site selection criteria included solar irradiance, wind speed, terrain slope, ambient temperature, land use, elevation, and proximity to infrastructure such as roads and power lines. the study demonstrates the critical role of spatial decision-making tools in promoting sustainable energy strategies, especially within developing countries experiencing energy deficits and environmental pressures. © 2025 The Authors

关键词： Multi-criteria geographic information system-based approach Renewable energy Site selection Solar and wind energy Sustainability planning

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MRLATO: An Adaptive Task Offloading Mechanism Based on Meta Reinforcement Learning in Edge Computing Environment

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Peiying Liu, Jiamin Guizani, Maher Wang, Jian Kumar, Neeraj Tan, Lizhuang Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao266580 China Ministry of Education Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China University of Texas Arlington Computer Science and Engineering Department TX United States College of Science Qingdao266580 China Thapar University Department of Computer Science and Engineering Patiala147004 India Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Traditional cloud computing models struggle to meet the requirements of latency-sensitive applications when processing large amounts of data. As a solution, Multi-access Edge Computing (MEC) extends computing resources to the edge of the network to reduce processing delays and improve user experience. However, in dynamically changing edge computing environments, effective decision making on whether to offload tasks to edge servers remains a core challenge. For this purpose, we propose MRLATO, an adaptive task offloading mechanism based on Meta Reinforcement Learning (MRL), which exploits a large amount of a priori knowledge of different tasks to achieve fast adaptation. The task offloading process is modelled as multiple Markov Decision Processes (MDPs) and solved using a Sequence to Sequence (Seq2Seq) neural network integrating multi-head attention and recursive task sequencing. It is shown by the experimental results that the proposed method has the lowest latency in all experimental settings and the convergence efficiency is improved by 14.06% compared to the traditional Deep Reinforcement Learning (DRL) algorithm. This research fully demonstrates the significant benefits of the deep integration of MRL with the edge computing domain, providing new optimisation ideas for task offloading decisions. © 1967-2012 IEEE.

关键词： Computation offloading

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Diagnosis of Autism Spectrum Disorder by Imperialistic Competitive algorithm and Logistic Regression Classifier

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computers, Materials & Continua 2023年第11期77卷 1515-1534页

作者： Shabana R.Ziyad Liyakathunisa Eman aljohani I.A.Saeed Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl Kharj16274Saudi Arabia Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah41411Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl Kharj16274Saudi Arabia

Autism spectrum disorder(ASD),classified as a developmental disability,is now more common in children than ever.A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in *** can seek professional help for a better prognosis of the child’s therapy when ASD is diagnosed under five *** research study aims to develop an automated tool for diagnosing autism in *** computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition,feature selection,and classification *** most deterministic features are selected from the self-acquired dataset by novel feature selection methods before *** Imperialistic competitive algorithm(ICA)based on empires conquering colonies performs feature selection in this *** performance of Logistic Regression(LR),Decision tree,K-Nearest Neighbor(KNN),and Random Forest(RF)classifiers are experimentally studied in this research *** experimental results prove that the Logistic regression classifier exhibits the highest accuracy for the self-acquired *** ASD detection is evaluated experimentally with the Least Absolute Shrinkage and Selection Operator(LASSO)feature selection method and different *** Exploratory Data Analysis(EDA)phase has uncovered crucial facts about the data,like the correlation of the features in the dataset with the class variable.

关键词： Autism spectrum disorder feature selection imperialist competitive algorithm LASSO logistic regression random forest

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AI Integration in the IT Professional Workplace: A Scoping Review and Interview Study with Implications for Education and Professional Competencies 2024

AI Integration in the IT Professional Workplace: A Scoping R...

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29th Working Group Reports on Innovation and Technology in computer Science Education, ITiCSE WGR 2024

作者： Clear, Tony Cajander, Åsa Clear, alison Mcdermott, Roger Daniels, Mats Divitini, Monica Forshaw, Matthew Humble, Niklas Kasinidou, Maria Kleanthous, Styliani Kultur, Can Parvini, Ghazaleh Polash, Mohammad Zhu, Tingting Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand Department of Information Technology Uppsala University Uppsala Sweden School of Business Eastern Institute of Technology Auckland New Zealand School of Computing Robert Gordon University Aberdeen United Kingdom Dpt. of Computer Science Norwegian University of Science and Technology Trondheim Norway The Alan Turing Institute Newcastle Upon Tyne United Kingdom Open University of Cyprus Nicosia Cyprus The Open University of Cyprus Nicosia Cyprus School of Computer Science Carnegie Mellon University PittsburghPA United States Manning College of Information and Computer Sciences University of Massachusetts Amherst Amherst United States School of Computer Science University of Sydney Sydney Australia Department of Geography Geomatics and Environment University of Toronto Mississauga Toronto Canada School of Computer Science Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

As Artificial Intelligence (AI) continues transforming workplaces globally, particularly within the Information Technology (IT) industry, understanding its impact on IT professionals and computing curricula is crucial. This research builds on joint work from two countries, addressing concerns about AI's increasing influence in IT sector workplaces and its implications for tertiary education. The study focuses on AI technologies such as generative AI (GenAI) and large language models (LLMs). It examines how they are perceived and adopted and their effects on workplace dynamics, task allocation, and human-system interaction. IT professionals, noted as early adopters of AI, offer valuable insights into the interplay between AI and work engagement, highlighting the significant competencies required for digital workplaces. This study employs a dual-method approach, combining a systematic and multi-vocal literature review and qualitative research methods. These included a thematic analysis of a set of 47 interviews conducted between March and May of 2024 with IT professionals in two countries (New Zealand and Sweden). The research aimed to understand the implications for computing students, education curricula, and the assessment of emerging professional competencies. The literature review found insufficient evidence addressing comprehensive AI practice methodologies, highlighting the need to both develop and regulate professional competencies for effective AI integration. Key interview findings revealed diverse levels of GenAI adoption, ranging from individual experimentation to institutional integration. Participants generally expressed positive attitudes toward the technology and were actively pursuing self-learning despite some concerns. The themes emerging from the interviews included AI's role in augmenting human tasks, privacy and security concerns, productivity enhancements, legal and ethical challenges, and the evolving need for new competencies in the workplace.

关键词： artificial intelligence

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Machine Learning Techniques for Intrusion Detection System Malware Android Devices

Machine Learning Techniques for Intrusion Detection System M...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： M. Jeevanantham N. Shanmugalakshmi S. Sudhakar M. C. Babu K. Ramanan R. Saravanakumar Master of Computer Application Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Application Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering NPR College of Engineering and Technology Dindigul Tamil Nadu India Department of Artificial Intelligence and Data Sciences Chennai Institute of Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Malware infestations have increased with the rise of mobile devices, notably Android smartphones. Our work fixes this issue by improving Android security using Decision Trees (DT), Support Vector Machines (SVMs), Random Forest (RFs), and K-nearest neighbours (KNNs). We defend Android smartphones from malware using a proven way. We will test these machine learning methods against diverse illnesses. Android smartphones have raised security concerns, necessitating innovative techniques to recognize new threats. Machine learning algorithms that recognize and anticipate patterns may replace Android security. DT, KNN, RF, and SVM algorithms helped us construct a strong malware detection system. We evaluate numerous Android malware detection machine-learning algorithms based on speed, accuracy, and effectiveness. These findings should improve Android security machine-learning solution selection and implementation. Decision trees are transparent and open, random forests use ensemble learning, support vector machines excel in probabilistic classification, and KNN detects Android infections from similarity patterns better. A comparison study can provide the finest security solution. The initiative will provide the basis for mobile device security research and enhance Android's security using machine learning. Android smartphones will be safer from new attacks when security gaps are closed.

关键词： Support vector machines Radio frequency Machine learning algorithms Machine learning Nearest neighbor methods Malware Security Decision trees Smart phones Random forests

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Deep Learning and Entity Embedding-Based Intrusion Detection Model for Wireless Sensor Networks

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computers, Materials & Continua 2021年第10期69卷 1343-1360页

作者： Bandar almaslukh Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Wireless sensor networks(WSNs)are considered promising for applications such as military surveillance and *** security of these networks must be ensured in order to have reliable *** such networks requires more attention,as they typically implement no dedicated security *** addition,the sensors have limited computing resources and power and storage,which makes WSNs vulnerable to various attacks,especially denial of service(DoS).The main types of DoS attacks against WSNs are blackhole,grayhole,flooding,and *** are two primary techniques to build an intrusion detection system(IDS):signature-based and *** study uses the data-driven approach since the signature-based method fails to detect a zero-day *** publications have proposed data-driven approaches to protect WSNs against such *** approaches are based on either the traditional machine learning(ML)method or a deep learning *** fundamental limitations of these methods include the use of raw features to build an intrusion detection model,which can result in low detection *** study implements entity embedding to transform the raw features to a more robust representation that can enable more precise detection and demonstrates how the proposed method can outperform state-of-the-art solutions in terms of recognition accuracy.

关键词： Wireless sensor networks intrusion detection deep learning entity embedding artificial neural networks

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Ransomware detection and family classification using fine-tuned BERT and RoBERTa models

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Egyptian Informatics Journal 2025年 30卷

作者： Hussain, Amjad Saadia, Ayesha alserhani, Faeiz M. Department of Cyber Security Air University Islamabad Capital Territory Islamabad44230 Pakistan Department of Computer Science Air University Islamabad Capital Territory Islamabad44230 Pakistan Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Al Jouf Sakakah72388 Saudi Arabia

Integrating Internet of Things (IoT) technologies in healthcare has revolutionized patient care, enabling real-time monitoring, predictive analytics, and personalized treatments. However, it presents significant challenges that must be addressed to ensure secure and reliable implementation. IoT devices in healthcare, such as remote patient monitors, are often constrained by limited computational power, making them vulnerable to sophisticated cyberattacks, including ransomware. In 2017 the WannaCry ransomware attack disrupted many National Health Service facilities in the United Kingdom and emphasized the critical need for robust cybersecurity measures. The lack of standardization across IoT devices creates interoperability issues and complicates data transfer between medical devices and healthcare systems. This research explores these challenges and proposes a novel approach using hyperparameter-optimized transfer learning-based models, Bidirectional Encoder Representations from Transformers (BERT), and a Robustly Optimized BERT Approach (RoBERTa), to not only detect but also classify ransomware targeting IoT devices by analyzing dynamically executed API call sequences in a sandbox environment. A total of 3300 samples from 10 ransomware families including 300 benign cases are analyzed dynamically in a sandbox environment. The newly created dataset is then preprocessed and fed to the BERT and RoBERTa models for training. The BERT achieved 95.60% accuracy with a minimal loss of 0.1650 while the RoBERTa achieved 94.39% accuracy with 0.1948 loss in classifying ransomware families. These results indicate that the proposed approach is game-changing in the classification of previously unidentified behavioral patterns inside ransomware and enhances the ability to tackle newly developing threats. By leveraging the dynamic analysis with API call sequences in a correct format, and training hyperparameter-optimized transformer learning-based models, the methodology efficiently

关键词： computer viruses

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