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Machine Learning Model for Wind Power Forecasting Using Enhanced Multilayer Perceptron

computers, Materials & Continua 2025年第5期83卷 2287-2303页

作者： Ahmed A.Ewees Mohammed A.A.Al-Qaness Ali Alshahrani Mohamed Abd Elaziz Computer Science and Artificial Intelligence Department College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia Department of Computer Damietta UniversityDamietta34517Egypt College of Physics and Electronic Information Engineering Zhejiang Normal UniversityJinhua321004China Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt

Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy *** enhances the efficiency and reliability of renewable energy *** approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy *** primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data *** research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original *** developed FHONO is then employed to optimize the MLP for enhanced wind power *** effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in *** results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R^(2))of 0.967 across all *** findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting.

关键词： Wind power forecasting multilayer perceptron fire hawk optimizer non-monopolize search

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Real-World Implementation of Offline Model-Free Reinforcement Learning for Thermostat Control

Real-World Implementation of Offline Model-Free Reinforcemen...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Ghane, Sara Elmaz, Furkan Jacobs, Stef Huybrechts, Thomas Verhaert, Ivan Mercelis, Siegfried Mannens, Erik University of Antwerp - imec IDLab - Faculty of Applied Engineering Antwerp Belgium University of Antwerp EMIB Faculty of Applied Engineering Antwerp Belgium University of Antwerp - imec IDLab Department of Computer Science Antwerp Belgium

ISBN: (纸本)9798331533892

This study introduces a model-free, offline Reinforcement Learning (RL) approach for optimizing the thermostat control in heating systems. Specifically, historical data from a real-world building was used to train the RL agent that is based on the Dueling Double Deep Q-Network (DQN) algorithm. The RL agent's objective is to balance thermal comfort with energy savings, by controlling indoor temperature set points. Traditional rule-based thermostat control methods often depend on fixed schedules and lack adaptability to varying conditions, resulting in energy inefficiencies. Training the RL model offline eliminates the requirement for live interaction, thereby preventing potential disruptions to occupant comfort. During evaluation, the RL model achieved an 18.66% reduction in energy use compared to a rule-based controller, while maintaining recommended indoor temperature levels. © 2025 IEEE.

关键词： Reinforcement learning

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Secure Chat System: Harnessing the Power of Hybrid Encryption for Enhanced Security and Confidentiality 12th

Secure Chat System: Harnessing the Power of Hybrid Encryptio...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Jerlin, M. Asha Shrivastav, Rochak Anusha, K. Devarajan, Ganesh Gopal Nagarajan, Senthil Murugan School of Computer Science and Engineering Vellore Institute of Technology Kelambakkam—Vandalur Rd Rajan Nagar Tamil Nadu Chennai600127 India Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi NCR Campus Delhi - Meerut Road Modinagar Uttar Pradesh Ghaziabad201204 India Department of Mathematics Faculty of Science Technology and Medicine University of Luxembourg Esch-Sur-Alzette Luxembourg

ISBN: (纸本)9789819788354

Strong and secure communication solutions are essential in a time when data breaches and privacy violations are becoming more common. The extensive design for a secure chat system is presented in this research study with the goal of protecting user privacy and guaranteeing the accuracy of data shared. The fundamental idea of this design is hybrid encryption, which carefully combines the strength of Rivest–Shamir–Adleman (RSA) asymmetric encryption with the effectiveness of Advanced Encryption Standard (AES) symmetric encryption. The system also includes advanced cryptographic features like Diffie–Hellman Key Exchange for symmetric key exchange and secure communication channels, Elliptic Curve Digital Signature Algorithm (ECDSA) for message authenticity, and HMAC-SHA-512 for message authentication. The secure distribution of RSA public keys is facilitated by Public Key Infrastructure (PKI), which enhances the confidentiality and authenticity of communication channels. JSON Web Tokens (JWT) are used to provide authentication, offering a scalable and secure method for user verification throughout login procedures. Furthermore, the system incorporates a bot element that proactively alerts users to occurrences related to entry and leave from chat rooms, hence enhancing transparency and privacy consciousness among users. The suggested architecture provides a solid answer to the problems of secure communication in modern digital settings by fusing these cryptographic primitives and features. This architecture has undergone extensive testing and validation, proving its effectiveness in protecting sensitive data and providing users with secure, frictionless communication experiences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Mitigating over-smoothing in Graph Neural Networks for node classification through Adaptive Early Embedding and Biased DropEdge procedures

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Knowledge-Based Systems 2025年 320卷

作者： Hoseinnia, Fateme Ghatee, Mehdi Chehreghani, Mostafa Haghir Department of Computer Science Faculty of Mathematics and Computer Science Amirkabir University of Technology Tehran Iran Department of Computer Engineering Amirkabir University of Technology Tehran Iran

Graph Neural Networks (GNNs) are widely used for tasks involving graph-structured data across various fields, including computer vision, biology, social media, and traffic prediction. Despite their substantial success, increasing the depth of GNNs can impair the discriminability of node representations, leading to a decline in performance for node classification tasks. This challenge is partly attributed to a phenomenon known as over-smoothing. This paper introduces an Adaptive Early Embedding (AEE) procedure between Graph Convolutional Network (GCN) layers. This method adaptively halts the aggregation of neighboring nodes before the final layer of the main network. By reducing the over-smoothing of node embeddings, we enhance the distinguishability of the data. Another important contribution of this work is using the inter-class Biased DropEdge (BDE) procedure, which effectively propagates beneficial information. The proposed model based on AEE+BDE can be integrated with baseline message-passing GNN models to mitigate over-smoothing challenges. Our experiments show that the proposed model outperforms baseline models. Additionally, we provide theoretical evidence supporting the effectiveness of the AEE and BDE procedures for node classification tasks. © 2025 Elsevier B.V.

关键词： Graph neural networks

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Synergizing vision transformer with ensemble of deep learning model for accurate kidney stone detection using CT imaging

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Alexandria engineering Journal 2025年 127卷 357-373页

作者： Alzughaibi, Arwa Alanazi, Adwan A. Alshahrani, Mohammed Hilali Jaghdam, Ines Hassaballa, Abaker A. Applied college Taibah University Madinah Saudi Arabia Department of Computer Science and Information College of Computer Science and Engineering University of Hail Hail Saudi Arabia Department of Computer Sciences Applied College Najran University Najran66462 Saudi Arabia Department of Computer Science and Information Technology Applied College Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Department of Mathematics Faculty of Science Northern Border University Arar73213 Saudi Arabia

The disease of Kidney stones is a risky disease for individuals all over the world. Many people with kidney stones in the early phase do not detect it as an illness, and it harms the organ gradually. Precise analysis of kidney illness is vital, as it is a major health concern that needs accurate detection for appropriate and effective treatment. CT scans are one of the most extensively accessible imaging models, and they are employed for effective diagnosis. Deep learning (DL) techniques are gradually identified as beneficial tools for analyzing illness in the medical field. However, present techniques employing deep networks often meet low accuracy and overfitting challenges, demanding further alteration for optimum performance. This study presents a Leveraging Flying Foxes Optimization with an Ensemble of Deep Learning for Accurate Kidney Stone Detection (LFFOEDL-AKSD) technique in CT scans. The presented LFFOEDL-AKSD technique mainly focuses on detecting kidney stones using CI imaging. At first, the presented LFFOEDL-AKSD technique applies the pre-processing phase, which involves image resizing for uniform CT scan dimensions and data augmentation through transformations like rotation and flipping to reduce overfitting, sobel filtering (SF) sharpens edges, and the data is separated into training, validation, and testing sets for model development. The presented LFFOEDL-AKSD technique employs the swin transformer (ST) model for the feature extraction method. Furthermore, the majority voting ensemble of three DL approaches, such as the graph convolutional network (GCN), temporal convolutional network (TCN), and three-dimensional convolutional autoencoder (3D-CAE) approaches, are employed to increase the precision and reliability of the kidney stone recognition. Finally, the presented LFFOEDL-AKSD technique implements the flying foxes optimization (FFO) approach for the hyperparameter tuning involved in the ensemble learning models. An extensive experiment is conduct

关键词： Flying foxes optimization Ensemble deep learning Kidney stone detection CI imaging Swin transformer

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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

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computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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Enhancing Elderly Care with Socially Assistive Robots: A Holistic Framework for Mobility, Interaction, and Well-Being

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IEEE Access 2025年 13卷 82698-82717页

作者： Naseer, Fawad Nasir Khan, Muhammad Tahir, Muhammad Addas, Abdullah Kashif, Hasnain Beaconhouse International College Computer Science and Software Engineering Department Faisalabad38000 Pakistan Government College University Lahore Electrical Engineering Department Lahore54000 Pakistan Sir Syed University of Engineering and Technology Department of Software Engineering Karachi75300 Pakistan Prince Sattam Bin Abdulaziz University College of Engineering Department of Civil Engineering Al-Kharj11942 Saudi Arabia King Abdulaziz University Faculty of Architecture and Planning Landscape Architecture Department Jeddah21589 Saudi Arabia University of South Asia Department of Computer Science Lahore54000 Pakistan

The global elderly population is projected to double by 2050, creating challenges in mobility, social isolation, and cognitive decline. Socially Assistive Robots (SARs) offer a promising solution, yet gaps remain in integrating mobility assistance with cognitive and emotional support. The research developed a comprehensive framework for SARs, emphasizing user-centered design, technological innovation, and cultural adaptability. A mixed-methods approach was employed with 200 participants aged 60+, from diverse settings, including rehab centers, retired groups, old age communities and nursing homes. Results demonstrated a 33.3% improvement in mobility scores and a 40% increase in social engagement. Health metrics revealed a 22% reduction in falls, a 15% rise in physical activity, and a 90% user satisfaction rate. Gender and age differences were evident, with males aged 71+ showing higher mobility improvements (35.6%) and females aged 71+ reporting the highest satisfaction (93%). Long-term data indicated sustained benefits, with participants retaining 80% of mobility gains and social engagement levels 38% above baseline after six months. These findings highlight the importance of culturally sensitive and user-centered SAR designs in promoting elderly well-being. The research offers a robust foundation for integrating SARs into elderly care systems, emphasizing adaptability, sustainability, and ethical considerations for future implementation. © 2013 IEEE.

关键词： Nursing

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Extremum Encoding for Joint Baseband Signal Compression and Time-Delay Estimation for Distributed Systems

Extremum Encoding for Joint Baseband Signal Compression and ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Weiss, Amir Kochman, Yuval Wornell, Gregory W. Faculty of Engineering Bar-Ilan University Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Israel Research Laboratory of Electronics Massachusetts Institute of Technology United States

ISBN: (纸本)9798350368741

The ubiquitous time-delay estimation (TDE) problem becomes nontrivial when sensors are non-co-located and communication between them is limited. Building on the recently proposed "extremum encoding" compression-estimation scheme, we address the critical extension to complex-valued signals, suitable for radio-frequency (RF) baseband processing. This extension introduces new challenges, e.g., due to unknown phase of the signal of interest and random phase of the noise, rendering a naïve application of the original scheme inapplicable and irrelevant. In the face of these challenges, we propose a judiciously adapted, though natural, extension of the scheme, paving its way to RF applications. While our extension leads to a different statistical analysis, including extremes of non-Gaussian distributions, we show that, ultimately, its asymptotic behavior is akin to the original scheme. We derive an exponentially tight upper bound on its error probability, corroborate our results via simulation experiments, and demonstrate the superior performance compared to two benchmark approaches. © 2025 IEEE.

关键词： compression for estimation data compression distributed estimation max-index estimator Time-delay estimation

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Hierarchical Transfer Multi-task Learning Approach for Scene Classification 27th

Hierarchical Transfer Multi-task Learning Approach for Scen...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Khoshkangini, Reza Tajgardan, Mohsen Jamali, Mahtab Ljungqvist, Martin Georg Mihailescu, Radu-Casian Davidsson, Paul Internet of Things and People Research Center Department of Computer Science and Media Technology Malmö University Malmö Sweden Faculty of Electrical and Computer Engineering Qom University of Technology Qom Iran Axis Communications AB Lund Sweden

ISBN: (纸本)9783031781063

This paper presents a novel Hierarchical Transfer and Multi-task Learning (HTMTL) approach designed to substantially improve the performance of scene classification networks by leveraging the collective influence of diverse scene types. HTMTL is distinguished by its ability to capture the interaction between various scene types, recognizing how context information from one scene category can enhance the classification performance of another. Our method, when applied to the Places365 dataset, demonstrates a significant improvement in the network’s ability to accurately identify scene types. By exploiting these inter-scene interactions, HTMTL significantly enhances scene classification performance, making it a potent tool for advancing scene understanding and classification. Additionally, this study explores the contribution of individual tasks and task groupings on the performance of other tasks. To further validate the generality of HTMTL, we applied it to the Cityscapes dataset, where the results also show promise. This indicates the broad applicability and effectiveness of our approach across different datasets and scene types. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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