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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 Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

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computers, Materials & Continua 2024年第2期78卷 2245-2267页

作者： Ashraf S.Mashaleh Noor Farizah Binti Ibrahim Mohammad Alauthman Mohammad Almseidin Amjad Gawanmeh School of Computer Sciences Universiti Sains MalaysiaPenangMalaysia Department Computer Center Al-Balqa Applied UniversitySaltJordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan Department of Computer Science Tafila Technical UniversityTafilaJordan College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic *** IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection *** paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT *** logic addresses IoT threat uncertainties and ambiguities *** component settings are optimized using PSO to improve *** methodology allows for more complex thinking by transitioning from binary to continuous *** of expert inputs,PSO data-driven tunes rules and membership *** study presents a complete IoT botnet risk assessment *** methodology helps security teams allocate resources by categorizing threats as high,medium,or low *** study shows how CICIoT2023 can assess cyber *** research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

关键词： IoT botnet detection risk assessment fuzzy logic particle swarm optimization(PSO) cybersecurity interconnected devices

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Far-field optical propagators with user-defined object-plane pixel size for ptychography

Optics Continuum

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Optics Continuum 2025年第4期4卷 804-825页

作者： Pelekanidis, Antonios Eikema, Kjeld S. E. Witte, Stefan Advanced Research Center for Nanolithography Science Park 106 Amsterdam1098 XG Netherlands Department of Physics and Astronomy Vrije Universiteit De Boelelaan 1105 Amsterdam1081 HV Netherlands Imaging Physics Department Faculty of Applied Sciences Delft University of Technology Lorentzweg 1 Delft Netherlands

Lensless imaging techniques have been developed to visualize objects with high robustness and unprecedented resolution. Lensless imaging is based on the numerical reconstruction of the transmission or reflection function of a sample from optical diffraction measurements. Specifically, coherent diffractive imaging (CDI) and ptychography involve an iterative process of numerical propagation of coherent light waves between the sample and detector plane. However, in the standard propagation models, the pixel size of the reconstructed object image is typically fixed and wavelength-dependent, which limits CDI and broadband ptychography. Here we investigate three propagation models for far-field propagation that allow user-defined pixel size at the object plane. These propagators are the two-step Fresnel, scaled angular spectrum, and chirp-Z transform. We derive their analytical expressions and observe that all three models are mathematically equivalent, although they have a different physical origin. Each propagator can be written in two distinct versions, which conceptually represent propagation via different intermediate planes. We perform propagation simulations and ptychographic reconstructions on experimental data to compare the performance of these two different versions. We also investigate how sampling bandwidth requirements can affect the model accuracy due to physical errors associated with cropping of high spatial frequencies. Our results show that the choice of the intermediate plane can affect the reconstruction quality due to different sampling bandwidth requirements, which enables a wider choice of pixel sizes in the object plane. Our analysis provides guidelines for selecting an optimized object pixel size when performing reconstructions on broadband CDI and ptychography data. © 2025, Optica Publishing Group (formerly OSA). All rights reserved.

关键词： Z transforms

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Storage and retrieval of von Neumann measurements via indefinite causal order structures

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Physical Review A 2024年第4期110卷 042422页

作者： Paulina Lewandowska Ryszard Kukulski IT4Innovations Faculty of Physics Astronomy and Applied Computer Science ul. Łojasiewicza 11

This work presents the problem of learning an unknown von Neumann measurement of dimension d using indefinite causal structures. In the considered scenario, we have access to N copies of the measurement. We use the formalism of process matrices to store information about the given measurement that later will be used to reproduce its best possible approximation. Our goal is to compute the maximum value of the average fidelity function Fd(N) of our procedure. We prove that Fd(N)=1−Θ(1N2) for arbitrary but fixed dimension d. Furthermore, we present the SDP program (semi-definite program) for computing Fd(N). Basing on the numerical investigation, we show that for the qubit von Neumann measurements using indefinite causal learning structures provide better approximation than quantum networks, starting from N≥3.

关键词： Quantum information theory Quantum networks

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A Robust Security Detection Strategy for Next Generation IoT Networks

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computers, Materials & Continua 2025年第1期82卷 443-466页

作者： Hafida Assmi Azidine Guezzaz Said Benkirane Mourade Azrour Said Jabbour Nisreen Innab Abdulatif Alabdulatif Technology Higher School Essaouira Cadi Ayyad UniversityEssaouira44000Morocco IMIA Laboratory MSIA TeamFaculty of Sciences and TechniquesMoulay Ismail University of MeknesErrachidia50050Morocco CRIL-CNRS Artois UniversityLens62300France Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityDiriyahRiyadh13713Saudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydah52571Saudi Arabia

Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating *** connectivitymakes it possible to harvest vast quantities of data,creating new opportunities for the emergence of unprecedented *** ensure IoT securit,various approaches have been implemented,such as authentication,encoding,as well as devices to guarantee data integrity and *** these approaches,Intrusion Detection Systems(IDS)is an actual security solution,whose performance can be enhanced by integrating various algorithms,including Machine Learning(ML)and Deep Learning(DL),enabling proactive and accurate detection of *** study proposes to optimize the performance of network IDS using an ensemble learning method based on a voting classification *** combining the strengths of three powerful algorithms,Random Forest(RF),K-Nearest Neighbors(KNN),and Support Vector Machine(SVM)to detect both normal behavior and different categories of *** analysis focuses primarily on the NSL-KDD dataset,while also integrating the recent Edge-IIoT dataset,tailored to industrial IoT *** results show significant enhancements on the Edge-IIoT and NSL-KDD datasets,reaching accuracy levels between 72%to 99%,with precision between 87%and 99%,while recall values and F1-scores are also between 72%and 99%,for both normal and attack *** the promising results of this study,it suffers from certain limitations,notably the use of specific datasets and the lack of evaluations in a variety of *** work could include applying this model to various datasets and evaluating more advanced ensemble strategies,with the aim of further enhancing the effectiveness of IDS.

关键词： IoT security intrusion detection RF KNN SVM EL NSL-KDD Edge-IIoT

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A novel U-net model for brain tumor segmentation from MRI images

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Alexandria Engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

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Completely entangled subspaces of entanglement depth k

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Physical Review A 2024年第1期110卷 012403-012403页

作者： Maciej Demianowicz Kajetan Vogtt Remigiusz Augusiak Institute of Physics and Applied Computer Science Faculty of Applied Physics and Mathematics Polish Academy of Sciences Aleja Lotników 32/46 02-668 Warsaw Poland Faculty of Physics

We introduce a class of entangled subspaces: completely entangled subspaces of entanglement depth k (k−CESs). These are subspaces of multipartite Hilbert spaces containing only pure states with an entanglement depth of at least k. We present an efficient construction of k−CESs of any achievable dimensionality in any multipartite scenario. Further, we discuss the relation between these subspaces and unextendible product bases (UPBs). In particular, we establish that there is a nontrivial bound on the cardinality of a UPB whose orthocomplement is a k−CES. Further, we discuss the existence of such UPBs for qubit systems.

关键词： Entanglement detection Quantum entanglement Quantum error correction Quantum information theory

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Findings of the Second Shared Task on Multilingual Coreference Resolution 6

Findings of the Second Shared Task on Multilingual Coreferen...

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CRAC Shared Task on Multilingual Coreference Resolution at 6th Workshop on Computational Models of Reference, Anaphora and Coreference, CRAC 2023

作者： Žabokrtský, Zdeněk Konopík, Miloslav Nedoluzhko, Anna Novák, Michal Ogrodniczuk, Maciej Popel, Martin Pražák, Ondřej Sido, Jakub Zeman, Daniel Charles University Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Prague Czech Republic University of West Bohemia Faculty of Applied Sciences Department of Computer Science and Engineering Pilsen Czech Republic Institute of Computer Science Polish Academy of Sciences Warsaw Poland

ISBN: (纸本)9781955917025

This paper summarizes the second edition of the shared task on multilingual coreference resolution, held with the CRAC 2023 workshop. Just like last year, participants of the shared task were to create trainable systems that detect mentions and group them based on identity coreference;however, this year’s edition uses a slightly different primary evaluation score, and is also broader in terms of covered languages: version 1.1 of the multilingual collection of harmonized coreference resources CorefUD was used as the source of training and evaluation data this time, with 17 datasets for 12 languages. 7 systems competed in this shared task. © CRAC 2023. All Rights Reserved.

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Crafting the Future: Developing and Evaluating a Digital Mindset Competence Model for the Industrial Craft Sector 16

Crafting the Future: Developing and Evaluating a Digital Min...

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16th International Conference on Knowledge Management and Information Systems, KMIS 2024 as part of 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024

作者： Kocak, Seyma Pawlowski, Jan University of Jyväskylä Faculty of Information Technology Jyväskylä Finland University of Applied Sciences Ruhr West Institute of Computer Science Bottrop Germany

ISBN: (纸本)9789897587160

The recent development of digitization has significantly influenced various sectors of the economy, and the Industrial Craft Sector is no exception. The transition to digital technologies and processes is inevitable and holds the potential for increasing efficiency and creating competitive advantages. This research used the Design science methodology to develop a Digital Mindset Competence Model. This model comprises eight dimensions specifically tailored to the requirements and challenges of the Industrial Craft Sector. These dimensions aim to promote and strengthen the digital mindset among professionals in the Industrial Craft Sector. To ensure the validity and relevance of this model, experts from the Industrial Craft Sector were involved in a qualitative methodology. The combination of scientific methodology and practical experience ensures a comprehensive perspective and guarantees the applicability of the developed model. The results of this research underscore the importance of digital transformation in the Industrial Craft Sector and the necessity of a digital mindset. The developed Digital Mindset Competence Model provides a targeted approach to promoting digital competencies in the Industrial Craft Sector and guides future developments in this area. It becomes evident that an appropriate digital mindset is essential to optimally leverage the potentials of digitization in the Industrial Craft Sector and successfully navigate continuous change. This scholarly contribution contributes to raising awareness of the significance of a digital mindset in the Industrial Craft Sector. It forms a basis for further investigations and practical applications within digital transformation. © 2024 by SCITEPRESS – science and technology Publications, Lda.

关键词： Design science Research Digital Competence Digital Mindset Industrial Craft Sector

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Comparative Analysis of COVID-19 Detection Methods Based on Neural Network

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computers, Materials & Continua 2023年第7期76卷 1127-1150页

作者： Inès Hilali-Jaghdam Azhari A Elhag Anis Ben Ishak Bushra M.Elamin Elnaim Omer Eltag Mohammed Elhag Feda Muhammed Abuhaimed S.Abdel-Khalek Computer Sciences and Information Technology Programs Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Mathematics and Statistics College of ScienceTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Quantitative Methods Higher Institute of ManagementUniversity of Tunis1073Tunisia Department of Computer Science College of Science and Humanities in Al-SulailPrince Sattam Bin Abdulaziz UniversityAl KharjSaudi Arabia Department of Computer Science Faculty of Science and ArtsKing Khalid UniversityAbha61421Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohagEgypt

In 2019,the novel coronavirus disease 2019(COVID-19)ravaged the *** of July 2021,there are about 192 million infected people worldwide and 4.1365 million *** present,the new coronavirus is still spreading and circulating in many places around the world,especially since the emergence of Delta variant strains has increased the risk of the COVID-19 pandemic *** symptoms of COVID-19 are diverse,and most patients have mild symptoms,with fever,dry cough,and fatigue as the main manifestations,and about 15.7%to 32.0%of patients will develop severe *** are screened in hospitals or primary care clinics as the initial step in the therapy for *** transcription-polymerase chain reaction(PCR)tests are still the primary method for making the final diagnosis,in hospitals today,the election protocol is based on medical imaging because it is quick and easy to use,which enables doctors to diagnose illnesses and their effects more *** to this approach,individuals who are thought to have COVID-19 first undergo an X-ray session and then,if further information is required,a CT-scan *** methodology has led to a significant increase in the use of computed tomography scans(CT scans)and X-ray pictures in the clinic as substitute diagnostic methods for identifying *** provide a significant collection of various datasets and methods used to diagnose COVID-19,this paper provides a comparative study of various state-of-the-art *** impact of medical imaging techniques on COVID-19 is also discussed.

关键词： Neural networks frameworks intelligent systems COVID-19 CT

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