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Sustainable energy management in the AI era: a comprehensive analysis of ML and DL approaches

Computing 2025年第6期107卷 1-64页

作者： Javed, Haseeb Eid, Fatma El-Sappagh, Shaker Abuhmed, Tamer Department of Computer Science and Engineering College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Technology Management Stony Brook University NY11794 United States Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Banha13518 Egypt

This study comprehensively analyzes the application of innovative deep learning (DL) and machine learning (ML) techniques in smart energy management systems (EMSs), with an emphasis on load forecasting, demand response, and the development of smart energy sectors. The application of various ML and DL models were examined in over 200 studies from 2014 to 2024 in an electrical network's EMS to highlight the key benefits and advances made by each technology for the sustainable management systems in energy sector. The findings emphasize DL and ML models’ enhanced precision and predictive capabilities in load forecasting, their efficacy in enabling efficient demand response mechanisms, and their significance in supporting the development of smart energy sectors. Furthermore, recommendations are made based on the survey results to assist in incorporating these techniques into EMS frameworks, such as investment in data infrastructure, model training and validation, and collaboration between researchers, industry experts, and policymakers. The study also discusses the limitations identified in the literature, such as limited real-world implementations, challenges regarding quality and data availability, and the need for enhanced ML and DL model interpretability. Addressing these limitations can assist in increasing the application and efficacy of ML and DL techniques in EMSs, enabling a more efficient and sustainable energy landscape. Finally, this study facilitates researchers' exploration of ML and DL in energy management, highlighting relevant limitations, strengths, and alternative approaches associated with sustainable energy management. It also indicates potential future research directions for further investigation. © The Author(s) 2025.

关键词： Power management (telecommunication)

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IoT Integration Protocol for Enhanced Hospital Care

arXiv

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arXiv 2025年

作者： Zontou, Ellie Kyprioti, Antonia Dept. of Computer Engineering and Informatics University of Patras Patras26504 Greece Dept. of Medicine University of Thessaly Larisa41334 Greece

This paper introduces the "IoT Integration Protocol for Enhanced Hospital Care", a comprehensive framework designed to leverage Internet of Things (IoT) technology to enhance patient care, improve operational efficiency, and ensure data security in hospital settings. With the growing emphasis on utilizing advanced technologies in healthcare, this protocol aims to harness the potential of IoT devices to optimize patient monitoring, enable remote care, and support clinical decision-making. By integrating IoT seamlessly into nursing workflows and patient care plans, hospitals can achieve higher levels of patient-centric care and real-time data insights, leading to better treatment outcomes and resource allocation. This paper outlines the protocol’s objectives, key components, and expected benefits, while emphasizing the importance of ethical considerations and ongoing evaluation to ensure successful implementation. © 2025, CC BY-NC-ND.

关键词： Resource allocation

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DIPLI: Deep Image Prior Lucky Imaging for Blind Astronomical Image Restoration

arXiv

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arXiv 2025年

作者： Singh, Suraj Batsheva, Anastasia Rogov, Oleg Y. Bouridane, Ahmed Skolkovo Institute of Science and Technology Russia College of Computing and Informatics Computer Engineering Department University of Sharjah United Arab Emirates AIRI Russia

Contemporary image restoration and super-resolution techniques effectively harness deep neural networks, markedly outperforming traditional methods. However, astrophotography presents unique challenges for deep learning due to limited training data. This work explores hybrid strategies, such as the Deep Image Prior (DIP) model, which facilitates blind training but is susceptible to overfitting, artifact generation, and instability when handling noisy images. We propose enhancements to the DIP model’s baseline performance through several advanced techniques. First, we refine the model to process multiple frames concurrently, employing the Back Projection method and the TVNet model. Next, we adopt a Markov approach incorporating Monte Carlo estimation, Langevin dynamics, and a variational input technique to achieve unbiased estimates with minimal variance and counteract overfitting effectively. Collectively, these modifications reduce the likelihood of noise learning and mitigate loss function fluctuations during training, enhancing result stability. We validated our algorithm across multiple image sets of astronomical and celestial objects, achieving performance that not only mitigates limitations of Lucky Imaging, a classical computer vision technique that remains a standard in astronomical image reconstruction but surpasses the original DIP model, state of the art transformer- and diffusion-based models, underscoring the significance of our improvements. © 2025, CC BY-NC-SA.

关键词： Markov processes

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Design of Secure Multi-User Coded-SSK with Index Selecting Capability

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IEEE Transactions on Communications 2025年

作者： Hassan, Sumeyra Panayirci, Erdal Helleseth, Tor Vincent Poor, H. Kadir Has University Department of Electrical and Electronics Engineering Istanbul34083 Turkey University of Bergen Department of Informatics Bergen Norway Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

We propose a new coded space shift keying (CSSK) signaling technique for multi-user (MU), multiple-input multiple-output (MIMO) communication systems incorporating physical layer security (PLS). Besides its error correction ability, the designed linear code is capable of choosing transmit antenna indices automatically and selecting the best set of antenna combinations that minimizes the bit error rate (BER). Results obtained for the single-user (SU) schemes are then extended to a general single-cell downlink MU CSSK setting. A precoder design is proposed with a maximum ratio combining (MRC) technique to eliminate the multi-user interference (MUI) entirely by taking advantage of channel state information (CSI) at the transmitter. It is shown that the same precoding provides a very effective jamming signal for the PLS against passive eavesdroppers, degrading their signal-to-interference-plus-noise ratio (SINR) severely. A closed-form expression for the achievable secrecy rates is derived and it is maximized by the proposed power allocation algorithm. Finally, it is shown analytically and by computer simulations that substantially better BER performance is achieved by each user over interference-free transmission compared to an SU transmission with a maximum likelihood (ML) detector. © 1972-2012 IEEE.

关键词： Signal to noise ratio

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Evaluation of Multiple Apache Spark Applications using Kubernetes as a Cluster manager on Google Cloud

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Procedia computer Science 2025年 252卷 576-582页

作者： M. Jayanthi K. Ram Mohan Rao Vuppala Sukanya Assistant Professor Dept of Computer Science and Informatics Mahatma Gandhi University Nalgonda Professor Dept of Information Technology Vasavi College of Engineering Hyderabad Assistant Professor Dept of CSE OU Hyderabad

Big data processing frameworks demands for scalable and efficient cluster management. Apache Spark has emerged as prominent big data processing framework providing high-speed data processing and analytics capabilities for multiple applications. This paper explores the integration of Kubernetes as a cluster manager for Apache Spark applications leveraging its containerization capabilities to improve resource utilization and simplify deployment. In this paper the challenges of deploying spark applications on traditional cluster managers and showcase the advantages of adopting Kubernetes are analysed. The experimental evaluation demonstrates the benefits of Kubernetes as a cluster manager for Apache Spark framework. To execute the multiple Apache Spark applications on Kubernetes a homogenous cluster on Google Cloud is created by History bucket and service account. Finally multiple applications are executed on Google Kubernetes Engine. Output can be shown as the number of executor pods created with the performance metrics can be viewed. In conclusion, this paper compares the performance metrics such as job execution time and resource utilization with the different cluster.

关键词： Homogenous Apache Spark Kubernetes Google Kubernetes Engine Containerization

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A multi-stage review framework for AI-driven predictive maintenance and fault diagnosis in photovoltaic systems

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Applied Energy 2025年 393卷

作者： Hamza, Ali Ali, Zunaib Dudley, Sandra Saleem, Komal Uneeb, Muhammad Christofides, Nicholas London United Kingdom London United Kingdom Islamabad Pakistan Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus

The photovoltaic (PV) sector encounters challenges such as high initial costs, reliance on weather, susceptibility to faults, irregularities in the grid, and degradation of components. Predictive maintenance (PdM) aims to proactively identify issues, thereby enhancing reliability and efficiency but may lack specific fault details without additional diagnostic efforts. This research presents an advanced PdM and fault diagnosis framework that integrates fault pattern analysis, severity assessments, and critical fault predictions. It aims to improve the functionality of PV systems, minimize downtime, and enhance reliability by identifying and analyzing specific fault patterns. Consequently, our article provides a critical review of current Artificial Intelligence (AI) methodologies for PdM and fault diagnosis in PV systems. Moreover, this study highlights the significance of data standardization and offers recommendations on how PdM, when combined with fault diagnosis, can utilize various data sources to anticipate faults in advance, assess their severity, and optimize system performance and maintenance activities. To the best of the authors’ knowledge, no such review study exists. © 2025

关键词： Predictive maintenance

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Designing Quantum-Safe Lattice-Based Multi-Authority CP-ABE Scheme for Blockchain-Enabled IoT-Based Consumer Healthcare Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Bagchi, Prithwi Bisht, Abhishek Das, Ashok Kumar Saxena, Neetesh Hossain, M. Shamim International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Cardiff University School of Computer Science and Informatics CardiffCF24 4AG United Kingdom King Saud University College of Computer and Information Sciences Department of Software Engineering Riyadh13273 Saudi Arabia

Despite edge computing reducing communication delays associated with cloud computing, privacy concerns remain a significant challenge when sharing data from edge-based consumer electronics (CE) or Internet-of-Things (IoT) devices. Ciphertext policy attribute-based encryption (CP-ABE) is a cryptographic tool that facilitates intricate and refined access control. It can deliver more flexible, secure, compact, and effective access control policies for the cloud data. In the case of a conventional CP-ABE scheme, the keys can only be issued and disseminated by a trusted central authority (CA). However, if the CA is compromised, the entire system becomes more susceptible to assaults or failures, which leads to a single-point failure. In this article, we propose a Ring-Learning with Errors (Ring-LWE)-based MA-CP-ABE scheme that is effectively resolved by the multi-authority CP-ABE (MA-CP-ABE), and it ensures the security against quantum attacks. The threshold secret sharing by Shamir and the Lagrange interpolation formula are applied in the key-generation and decryption procedures of the proposed scheme, which make it easier to segment and restore the private keys. The proposed scheme is implemented in the CE-enabled IoT-based smart healthcare applications using the blockchain technology as a secure storage. A detailed comparative study, security analysis and experimental results with the existing relevant schemes shows that the proposed scheme exhibits superior security and better efficiency as compared to other schemes, demonstrating its feasibility in practical IoT-based healthcare applications. © 2025 IEEE.

关键词： Quantum cryptography

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Label-Free Biomolecule Detection With InP/AlGaAs Charge Plasma Dielectric-Modulated Vertical TFET Using TaN as Metal Gate

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IEEE Access 2025年 13卷 89667-89684页

作者： Hashim Elshafie Abdulrahman Saad Alqahtani Azath Mubarakali Adarsh Rag S M. Venkatesh P. Parthasarathy Department of Computer Engineering College of Computer Science King Khalid University Abha Saudi Arabia College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Informatics and Computer Systems College of Computer Science King Khalid University Abha Saudi Arabia Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Electronics and Communication Engineering CMR Institute of Technology Bengaluru Karnataka India

This study introduces a label-free biosensing method for biomolecule detection utilizing an InP/AlGaAs charge plasma dielectric-modulated vertical tunnel field-effect transistor (InP/AlGaAs VTFET) featuring TaN as the metal gate. The device comprises a nanocavity beneath the gate metal adjacent to the tunnelling junction, where biomolecules engage with the dielectric material, resulting in fluctuations in the drain current. A twin metal gate architecture with laterally split dielectrics is employed to eliminate short-channel effects. The biosensor has exceptional sensitivity, attaining a peak drain current sensitivity of $2.5 \times 10$ cm2 for biomolecules such as albumin (k = 7). The gadget proficiently identifies biomolecules with varying dielectric constants and charge distributions, enabling adaptable label-free detection of diverse targets. The study examines the influence of dielectric constant on critical metrics such as Energy Band Diagrams, Drain Current, Drain Sensitivity, and subthreshold swing (SS). The overlap between the source and pocket regions, along with the introduction of an auxiliary gate, optimizes the electrical characteristics. Simulation results show that the proposed InP/AlGaAs VTFET achieves a maximum sensitivity of $3.5 \times 10 ^{3}$ , outperforming configurations without overlap ( $2 \times 10 ^{3}$ ), highlights the potential of proposed InP/AlGaAs VTFET for scalable, high-sensitivity, label-free biomolecule detection.

关键词： Biosensors Logic gates Sensitivity Tunneling Dielectric constant Performance evaluation Metals Biological system modeling TFETs Electrostatics

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Modelling and control of a multi-product pipeline transportation process

引用

International Journal of Information Technology (Singapore) 2025年 1-11页

作者： Jiménez-Cabas, Javier Piñeres-Espitia, Gabriel Rodelo-Serrano, Esteban Pamucar, Dragan Ramirez-Leon, Hermes Butt, Shariq Aziz Naz, Sumera Simic, Vladimir Computer Science and Electronic Department Universidad de La Costa Barranquilla Colombia Departamento de Ingeniería Mecánica Universidad Simón Bolívar Barranquilla Venezuela Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Industrial Engineering & Management Yuan Ze University Taoyuan Taiwan Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade 11010 Serbia Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea School of Systems and Technology Department of Computer Science University of Management and Technology Lahore Pakistan Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan

One of the most critical processes in the petroleum industry is transporting crude oil and its derivatives. Usually, it is done, due to easiness and economic aspects, through pipelines;several products, such as gasoline, kerosene, naphtha, and liquefied petroleum gas, among others, are moved in batch processes to both distribution and storage centers or transport ships. Since these transport systems transport different products, many works have focused on developing optimization strategies for scheduling these pipelines. That is, strategies that seek to optimize the use of the pipeline by establishing at what time points certain products must be transported. These strategies would be located at the planning level in the automation pyramid. In this work, on the other hand, different control strategies are proposed for a pipeline, but at the operation level. These strategies would be located at the control level, below the planning strategies in the automation pyramid. A pipeline of three pipe sections is modeled to develop the proposed control strategies, and the mathematical model is validated with actual data. The results show that, first, the proposed model approximates the actual behavior of the pipeline quite well and, second, that the combination of PID and feedforward control is an excellent alternative to implement control strategies for the operation of liquid transportation systems through pipelines. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： control strategies liquid transportation modeling of system

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AI-Powered Behaviour Analysis in Financial Services

AI-Powered Behaviour Analysis in Financial Services

引用

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

作者： R. Thalapathi Rajasekaran Muthu Selvam Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India College of Computing and Informatics University of North Carolina at Charlotte Charlotte North Carolina USA

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

This research investigates the application of state-of-the-art machine learning strategies, including deep learning, clustering, and reinforcement learning, to better understand user behavior in the banking sector. This strategy enriches personalisation, boosts customer engagement, and enhances operational efficiencies. Advanced neural network designs, including Recurrent Neural Networks (RNNs) and transformer architectures, are deployed to interpret transactional and communicative data. Various clustering methods, such as K-Means, DBSCAN, and Hierarchical Clustering, are utilised to classify users by their behavioral characteristics. Moreover, techniques like Deep Q-learning and multi-agent reinforcement learning are employed to adapt dynamically to user actions, thereby maximizing satisfaction. The research further highlights essential ethical issues concerning bias minimization, privacy protection, and the interpretability of models, ensuring the ethical deployment of machine learning practices. Through empirical validation, the suggested framework shows notable improvements in predicting defaults, refining marketing approaches, and increasing adoption rates. These findings underscore the revolutionary influence of machine learning in profiling user behavior, hinting at future developments in federated learning and quantum computing.

关键词： Industries Deep learning Ethics Adaptation models Q-learning Recurrent neural networks Computational modeling Refining Banking Transformers

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