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Machine learning applications in energy systems: current trends, challenges, and research directions

Energy Informatics 2025年第1期8卷 1-39页

作者： Aslam, Saad Aung, Pyi Phyo Rafsanjani, Ahmad Sahban Majeed, Anwar P. P. Abdul Faculty of Engineering and Technology School of Computing and Artificial Intelligence Sunway University Bandar Sunway Selangor Subang Jaya47500 Malaysia

The paradigm shift towards Smart Grids, Smart Buildings, Smart Monitoring, and Operation has driven researchers to propose innovative solutions for designing and maintaining energy systems. Although the integration of Renewable Energy Sources (RES) supports sustainability goals, it also introduces vulnerabilities to unpredictable challenges such as grid stability, energy storage requirements, and infrastructure modernization. Machine Learning (ML) has emerged as a transformative tool to address these challenges, offering opportunities to enhance energy efficiency, and system design in alignment with Sustainable Development Goals (SDGs). The emphasis on these goals necessitates the study of new system designs that prioritize energy efficiency. Building on its proven success, researchers are increasingly adopting ML-driven approaches to accelerate advances in energy systems. This work presents a detailed review of current ML-driven research trends in energy systems, outlines the associated challenges, and provides potential research directions and recommendations. Unlike the existing literature, which focuses primarily on ML applications in the RES domain, this study offers a holistic perspective on ML-driven approaches across various aspects of energy systems, including energy policy and sustainability. It aims to serve as a comprehensive resource, bridging the gap between research advancements and practical implementations in energy systems through ML-driven innovation. © The Author(s) 2025.

关键词： Modernization

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Triple-Band Circular Ring Monopole Antenna for RF Power Harvesting Applications

Advanced Electromagnetics

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Advanced Electromagnetics 2025年第1期14卷 39-51页

作者： Mbida, Joseph Mari Mebara Eyebe, Guy Ayissi Singh, Ghanshyam Mbinack, Clément Yende, Mohamed Njianga Department of Physics Faculty of Sciences University of Yaounde I Yaounde Cameroon Electrical and Computing Engineering Department Université du Québec à Trois-Rivieres Canada Department of Electronics and Communication Engineering Feroze Gandhi Institute of Engineering and Technology Raebareli India

This paper presents a simple and efficient approach for an antenna design and construction based on an I-shaped slot-loaded triple wideband microstrip monopole antenna with novel radio frequency (RF) power/energy harvesting capability. The proposed antenna takes advantage of circular ring shape properties, loaded with an I-shaped slot with degraded ground structure (DGS) to improve the input performances of the conventional planar circular microstrip patch antenna (C-patch). Input impedance and magnetic field radiation pattern are calculated using the electromagnetic theory of transmission line in traveling waves and resonant cavity TE11 field mode, respectively. The proposed antenna is configured on thin lossy substrate material RO4003C of volume 64.800×57.000×1.524 (in mm3), relative dielectric permittivity Εr = 3.55, and numerically investigated using CST MWS. The prototype of the proposed antenna has been fabricated and tested. The experimental results show that the fabricated antenna exhibits an impressive 142% (2.98-11.11 GHz), 21.3% (11.54-14.42 GHz), and 19.6% (15.78-19.44 GHz) fractional bandwidth (FBW) for the resonant frequency of 5.73, 13.55, and 18.69 GHz respectively and peak gain of 4.2 dBi at the frequency of 5.8 GHz. Experimentally, the proposed antenna can be used in multiple applications like 2.4 GHz (2.401-2.495 GHz) Wi-Fi band/5 GHz (5.170-5.875 GHz) WLAN band/(3.1-10.6 GHz) US UWB at the view of the achieved lower band, FR3 6G operation regarding the obtained middle/intermediate band, and the future wireless and millimeter-wave communication systems according to the obtained upper band. © 2025, Advanced Electromagnetics. All rights reserved.

关键词： Directional patterns (antenna)

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ERBM: A Machine Learning-Driven Rule-Based Model for Intrusion Detection in IoT Environments

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Computers, Materials and Continua 2025年第3期83.0卷 5155-5179页

作者： Mehmmod, Arshad Batool, Komal Sajid, Ahthsham Alam, Muhammad Mansoor Khan, Inam Ullah Department of Information Security and Data Science Riphah Institute of Systems Engineering Riphah International University Islamabad 46000 Pakistan Faculty of Computing Riphah International University Islamabad 46000 Pakistan Faculty of Computing and Informatics Multimedia University Cyberjaya 63100 Malaysia

Traditional rule-based Intrusion Detection Systems (IDS) are commonly employed owing to their simple design and ability to detect known threats. Nevertheless, as dynamic network traffic and a new degree of threats exist in IoT environments, these systems do not perform well and have elevated false positive rates—consequently decreasing detection accuracy. In this study, we try to overcome these restrictions by employing fuzzy logic and machine learning to develop an Enhanced Rule-Based Model (ERBM) to classify the packets better and identify intrusions. The ERBM developed for this approach improves data preprocessing and feature selections by utilizing fuzzy logic, where three membership functions are created to classify all the network traffic features as low, medium, or high to remain situationally aware of the environment. Such fuzzy logic sets produce adaptive detection rules by reducing data uncertainty. Also, for further classification, machine learning classifiers such as Decision Tree (DT), Random Forest (RF), and Neural Networks (NN) learn complex ways of attacks and make the detection process more precise. A thorough performance evaluation using different metrics, including accuracy, precision, recall, F1 Score, detection rate, and false-positive rate, verifies the supremacy of ERBM over classical IDS. Under extensive experiments, the ERBM enables a remarkable detection rate of 99% with considerably fewer false positives than the conventional models. Integrating the ability for uncertain reasoning with fuzzy logic and an adaptable component via machine learning solutions, the ERBM system provides a unique, scalable, data-driven approach to IoT intrusion detection. This research presents a major enhancement initiative in the context of rule-based IDS, introducing improvements in accuracy to evolving IoT threats. Copyright © 2025 The Authors.

关键词： fuzzy prediction intrusions IoT Rule based

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Generative AI for Energy Harvesting Internet of Things Network: Fundamental, Applications, and Opportunities

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IEEE Internet of Things Magazine 2025年第3期8卷 72-80页

作者： Xie, Wenwen Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Dobre, Octavia A. Jilin University China The College of Computing and Data Science Nanyang Technological University Singapore The University of Hong Kong China Memorial University The Faculty of Engineering and Applied Science Canada

Internet of Things (IoT) devices are typically powered by small-sized batteries with limited energy storage capacity, requiring regular replacement or recharging. To reduce costs and maintain connectivity in IoT networks, energy harvesting technologies are regarded as a promising solution. Notably, due to its robust analytical and generative capabilities, generative artificial intelligence (GenAl) has demonstrated significant potential in optimizing energy harvesting networks. Therefore, we discuss key applications of GenAl in improving energy harvesting IoT networks in this article. Specifically, we first review the key technologies of GenAI and the architecture of energy harvesting IoT networks. Then, we show how GenAI can address different problems to improve the performance of the energy harvesting IoT networks. Subsequently, we present a case study of unmanned aerial vehicle (UAV)-enabled data col-lection and energy transfer. The case study shows distinctively the necessity of energy harvesting technology and verify the effectiveness of GenAI-based methods. Finally, we discuss some import-ant open directions. © 2018 IEEE.

关键词： Costs Generative AI Reviews Energy exchange Data collection Autonomous aerial vehicles Batteries Energy harvesting Internet of Things Battery management systems Energy storage

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End-to-end optimized ensemble model for Alzheimer’s disease detection 19

End-to-end optimized ensemble model for Alzheimer’s disease...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Hassan, Abdelrahman Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an increasing prevalence among the elderly, making early and accurate diagnosis critical for effective intervention and management. This paper introduces an end-to-end machine learning pipeline optimized for detecting AD using multimodal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The study proposes an ensemble model that integrates key modalities—demographics, cognitive scores, neuropsychological assessments, and MRI data—via early fusion to enhance AD detection. Various machine learning models, including Logistic Regression, Gradient Boosting, AdaBoost, XGBoost, Support Vector Machine, and Neural Networks, are compared, and feature selection is optimized through Recursive Feature Elimination, ANOVA F-value, and mutual information methods, with mutual information showing the most efficacy. With further hyperparameter tuning, the highest accuracy is achieved at 95.85%, with Gradient Boosting as the top-performing model. Additional experiments assess and evaluate individual modality contributions, underscoring the predictive value of cognitive scores and neuropsychological features. To enhance clinical interpretability, we incorporate model explainability using LIME and SHAP, which identify key features influencing model predictions across different AD stages. Explainable AI (XAI) is essential in healthcare for providing transparency and supporting clinician confidence by highlighting which biomarkers, such as MMSE, CDRSB, and PTAU, contribute most to diagnostic outcomes. This study demonstrates classical machine learning's potential in AD diagnostics, offering a pathway to cost-effective and clinically viable models for neurodegenerative disease management. © 2025 IEEE.

关键词： Adversarial machine learning

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A Comprehensive Integrated Security Model with Advanced Access Control for Enhancing WSN computing in IoT Frameworks

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Tehnicki Vjesnik 2025年第3期32卷 1040-1046页

作者： Navaneethan, S. Arun, A. Department of Networking & Communications School of Computing SRM Institute of Science and Technology SRM Nagar Kattan Kulathur Tamilnadu 603203 India Department of Networking & Communications School of Computing Faculty of Engineering & Technology SRM Institute of Science and Technology Kattan Kulathur Tamilnadu 603203 India

Wireless Sensor Network (WSN) computing has emerged as a pivotal intermediary process addressing latency challenges inherent in Cloud-oriented Internet of Things (IoT) services. However, this advancement brings forth significant security and privacy concerns exacerbated by the proliferation of network nodes and the vast volumes of real-time data generated within IoT frameworks. To mitigate these issues, this research proposes an Integrated Security Model with Advanced Access Control (ISM-AAC) Mechanism. The ISM-AAC integrates Functional Encryption (FE) and Role-Based Access Control (RBAC) to fortify communication security. The model employs a comprehensive approach encompassing Access Control Processes, Integrated Encryption Processes, and Re-Encryption Processes to enhance data confidentiality and integrity. The detailed framework includes phases such as system initialization, user management, and access permission administration, ensuring robust security throughout data transactions. Evaluation of the model's efficacy is conducted based on various security metrics pertinent to WSN computing environments. This study contributes to the advancement of secure computing in IoT by proposing a sophisticated ISM-AAC framework that not only addresses current security challenges but also anticipates future threats in the landscape of WSN computing. The findings underscore the model's effectiveness in safeguarding sensitive IoT data and lay the groundwork for future enhancements in IoT security protocols. © 2025, Strojarski Facultet. All rights reserved.

关键词： Access control models

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Leveraging Large Language Models for Smart Pharmacy Systems: Enhancing Drug Safety and Operational Efficiency 19

Leveraging Large Language Models for Smart Pharmacy Systems:...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Osheba, Abdelrahman Abou-El-Ela, Ahmed Adel, Osama Maaod, Youssef Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications. © 2025 IEEE.

关键词： Electronic health record

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PricingTheCloud: A Pricing Estimator for an Informed Cloud-Migration Process 20th

PricingTheCloud: A Pricing Estimator for an Informed Cloud...

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20th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2024

作者： Djouela Kamgang, Ines Raissa Elhabbash, Abdessalam Elkhatib, Yehia Department of Computer Engineering Faculty of Engineering and Technology University of Buea Buea Cameroon School of Computing and Communications Lancaster University Lancaster United Kingdom School of Computing Science University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9783031812255

One of the main challenges facing businesses migrating to the cloud is getting an estimate of their costs in advance. The estimators available to date allow companies to compare the different virtual machine offerings from each operator, but only venture very slightly into estimating the overall cost, which includes operational and network costs. Existing estimators include operational costs in their estimates, but almost no one considers the network cost, which is a complex but far from negligible component. In this paper, we seek to address this issue by proposing a new estimator called PricingTheCloud. It is an estimator that enables companies to have an accurate estimate of their costs in advance. Unlike other estimators, PricingTheCloud considers network costs in the cost estimation. Its evaluation shows an average accuracy of 86.73% for compute costs and 65.44% for network costs in different AWS-to-AWS scenarios as compared to AWS invoices and shows the effectiveness of the proposed estimator compared to three other cloud costs estimators namely, Cloudorado, Holori, and Vantage. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computational cost

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SQUMUTH squirrel search based algorithm for high order mutant generation in mutation testing

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Discover computing 2025年第1期28卷 1-17页

作者： Mohanty, Subhasish Mishra, Jyotirmaya Mohapatra, Sudhir Kumar Bejo, Seifu Detso Deferisha, Aliazar Deneke Computer Science and Engineering GIET University Odisha Gunupur India Faculty of Engineering and Technology Sri Sri University Cuttack India Department of Software Engineering CCI Wolkite University Wolkite Ethiopia Faculty of Computing and Software Engineering Arba Minch University Arba Minch Ethiopia

In today's software testing community, quality assessment remains critical, with mutation testing standing as a cornerstone technique for evaluating the effectiveness of test cases. This method involves introducing faulty code, or mutants, into the program to assess the quality of the test suite and other testing methods. However, mutation testing faces challenges such as the generation of numerous mutants, the presence of equivalent mutants that are difficult to detect through testing, and the lack of realistic mutant creation. Literature reviews indicate significant efforts to address these issues through formal solutions and heuristic methods. Recent optimization-based methods are now recognized as cost-effective result in an optimized solution. Hence, to overcome these limitations, this study introduces SQUMUTH, a novel approach for high-order mutant generation based on the Squirrel Search Algorithm (SSA). Inspired by the foraging behavior of squirrels, SSA offers a promising solution for enhancing the efficiency and effectiveness of mutation testing. Experimental evaluations on eight well-known Java benchmark programs demonstrate that SQUMUTH outperforms existing methods. Comparative analyses of mutation scores and the rates of realistic mutants consistently show its better performance across all subject programs compared to other state-of-the-art methods such as Social Group Optimization, Binary Genetic Algorithm, and random testing. The experimental results underscore its effectiveness in generating more realistic mutants. The experimental results indicated that the proposed approach has the potential to advance software testing by improving the cost-effectiveness of mutation analysis and the quality of software systems. © The Author(s) 2025.

关键词： First-order mutants Higher-order mutants Mutation score Mutation testing Software testing Squirrel search algorithm

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Preference Definition in a Multi-agent System Approach for User-Centric Internet of Things Smart Environments 18th

Preference Definition in a Multi-agent System Approach for...

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18th International KES Conference on Agents and Multi-Agent Systems: Technologies and Applications, KES-AMSTA 2024

作者： Mandaric, Katarina Jezic, Gordan Faculty of Electrical Engineering and Computing Internet of Things Laboratory University of Zagreb Unska 3 HR Zagreb10000 Croatia

ISBN: (纸本)9789819764686

The term Ambient Intelligence (AmI) has been a topic of interest for many years, as it has the potential to transform services and environments—from living and working spaces to hospitals. Context awareness is a prerequisite for the development of truly intelligent services, i.e. it is necessary to detect and understand the state of the space as well as events/activities taking place. Additionally, for the complete adaptation of the space to the user, it is necessary to take the user’s preferences into account. Defining user preferences is a very complex area with numerous approaches, starting with the development of simple automated services in a smart home, where the user is offered control of a simple set of rules to define their wants and needs. The paper conducts an analysis of preference definitions in different environments and proposes a preference definition for the use case of smart lighting and smart heating and cooling system in a Multi-Agent System approach. This proposed definition forms the basis for effective negotiation between users with different preferences, where conflicts are inevitable. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart homes

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