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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Seyed Mohammadreza Mousavi Amirhossein Rahimian Zarif Javad Hamidzadeh Faculty of computer engineering and information technology Sadjad University Mashhad Iran Faculty of Electrical and Biomedical Engineering Sadjad University Mashhad Iran

ISBN: (数字)9798331525132

ISBN: (纸本)9798331525149

The lithium battery (LIB) has emerged as a prominent area of research in energy and industrial applications, due to its superior energy density and efficient energy delivery capabilities. This study addresses the critical requirement for accurate State of Health (SOH) estimation as a significant challenge. The primary objective of this study is to utilize key battery health characteristic parameters to improve the accuracy and computational speed of SOH. Significant gaps have been identified in recent methodologies, particularly in terms of SOH estimation accuracy, computational speed, and simulation parameters. To address these limitations, a novel hybrid neural network (NN) approach is proposed, integrating octave convolution (OCT) and long short-term memory (LSTM). To demonstrate the effectiveness of the proposed method in the optimization process, a comparative analysis is conducted against Convolutional Neural Networks (CNN), OCT, LSTM, and CNN-LSTM architectures. The results indicate a significant reduction in the mean squared error (MSE), decreasing from 80.21 percent in CNN to 0.0758 percent in the proposed OCT-LSTM model. Furthermore, the root mean square error (RMSE) exhibits a substantial reduction of 64.91 percent and 58.14 percent compared to the LSTM and CNN-LSTM models, respectively. The simulation parameters used in this study were reduced from 41,228 for the CNN-LSTM model to 38,668 for the OCT-LSTM model. Empirical results show that the proposed method outperforms state-of-the-art methods in the number of parameters, MSE, RMSE, and estimation accuracy. These findings highlight the method's enhanced efficiency, computational speed, and precision, emphasizing its potential as a significant advancement in this domain.

关键词： Renewable energy sources Accuracy Convolution Computational modeling Estimation computer architecture Lithium batteries Convolutional neural networks Root mean square Long short term memory

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Comparison between Single-Input and Multi-Input Classification with the Application of Canny Feature Extraction and Classification Algorithms on the Toraja Buffalo Dataset 19

Comparison between Single-Input and Multi-Input Classificati...

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

作者： Manga, Abdul Rachman Nanda, Assyahrin Handayani, Anik Nur Herwanto, Heru Wahyu Asmara, Rosa Andrie Lantara, Dirgahayu Department of Computer Science Universitas Muslim Indonesia Makassar Indonesia Department of Electrical Engineering Universitas Negeri Malang Malang Indonesia Information Technology Department State Polytechnic of Malang Malang Indonesia Faculty of Industrial Engineering Universitas Muslim Indonesia Makassar Indonesia

ISBN: (纸本)9798331507817

Data classification plays a crucial role in artificial intelligence, particularly in enhancing model accuracy. This study focuses on classifying Toraja buffalo, a livestock breed with significant cultural importance in South Sulawesi, Indonesia. While the Single Input Approach is commonly used for classification, it often fails to capture all the necessary attributes to effectively distinguish between racial traits. Therefore, this research aims to evaluate the effectiveness of a multi-input approach, which integrates multiple data inputs to improve classification performance compared to the Single Input method. We employed four classification techniques: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes, and Decision Tree, using both Single Input and Multi Input configurations. Model performance was assessed through Precision, Recall, F1 Score, and Accuracy metrics. The findings indicate that the Multi Input approach consistently outperformed the Single Input method. Notably, KNN achieved its best performance with Multi Input, recording an F1 Score of 0.7263 and an Accuracy of 0.7333, significantly surpassing the results obtained from Single Input. Similarly, SVM also demonstrated substantial performance enhancements with Multi Input. Overall, the study highlights the importance of incorporating a wider array of informative data to enhance the model's capability in accurately classifying specific categories, with KNN showing the most pronounced improvements. © 2025 IEEE.

关键词： Support vector machines

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Opticurve: an optimized informer-curvelet framework for enhanced hyperspectral image segmentation and classification

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International Journal of information technology (Singapore) 2025年 1-16页

作者： Shaw, Kailash Wou Onn, Choo Li, Baihua Department of Computer Science and Engineering (AIML) Vishwakarma Institute of Technology Maharashtra Pune 411037 India Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Negeri Sembilan Nilai 71800 Malaysia Department of Computer Science Loughborough University Loughborough LE11 3TU United Kingdom

Hyperspectral image (HSI) classification is crucial for applications in climate action, land use analysis, disaster risk reduction, and informed decision-making, given the complex spatial and spectral variations inherent in HSI data. Traditional methods struggle with accurately capturing these variations, necessitating more advanced techniques. This work introduces an Optimized Linformer-Curvelet (OptiCurve) Framework that integrates CNN-based feature extraction, Curvelet Transform for spatial detail capture, and Linformer for efficient feature representation. By combining these techniques, the model enhances HSI segmentation and classification, supporting improved outcomes in critical areas like environmental monitoring and disaster response. The framework is validated on four standard HSI datasets-Indian Pines, Pavia University, Kennedy Space Center, and Houston University-showing significant performance improvements over existing methods. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Climate action Disaster risk reduction Feature extraction Hyperspectral image classification Land use Linformer-Curvelet transform

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Enhancing Healthcare Data Security: Computational Efficiency of Blockchain-Based Digital Signatures 5th

Enhancing Healthcare Data Security: Computational Efficiency...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Xu, Li Goyal, S.B. Rajawat, Anand Singh Niranjala, T. H. A. Shyama Heshini City Graduate School City University Petaling Jaya46100 Malaysia Nanchang Institute of Science and Technology Nanchang China Faculty of Information Technology City University Petaling Jaya46100 Malaysia School of Computer Science and Engineering Sandip University Nashik India

ISBN: (纸本)9789819771776

This study aims to examine the computational efficiency of blockchain-based digital signature technologies with respect to healthcare data security. When thinking about the revolutionary opportunities and risks posed by healthcare data digitization, concerns regarding data privacy and security have come to the forefront. Blockchain technology, which is both decentralised and immutable, offers a promising alternative. Two of its primary uses, data integrity and identity authentication, are made possible by digital signature systems. We conducted a comprehensive analysis to find out how efficient different protocols are in healthcare computing. Among these protocols were EdDSA, ECDSA, and Schnorr signatures. Our research indicates that efficiency is significantly affected by protocol selection. Despite the strong security it provides, data validation delays may occur due to the increased processing expense generated by ECDSA. Schnorr signatures and EdDSA are viable solutions that offer a reasonable compromise between security and efficiency. By delving into optimization methodologies and associated difficulties, this research paved the road for the ethical application of blockchain technology in healthcare. The healthcare industry, policymakers, and IT developers all benefit from our study when it comes to selecting and improving methods for secure and efficient data storage. This study uniquely examines the computational efficiency of blockchain-based digital signature protocols in healthcare data security, addressing optimization methodologies and highlighting the suitability of Schnorr signatures and EdDSA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Ultimate Boundedness and Output Convergence of Prioritized Output Tracking Control Under Nonsmooth and Imperfect Feedback Linearization

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IEEE Transactions on Automatic Control 2025年

作者： An, Sang-Ik Lee, Dongheui Park, Gyunghoon University of Seoul School of Electrical and Computer Engineering Korea Republic of Tu Wien Autonomous Systems at Institute of Computer Technology Faculty of Electrical Engineering and Information Technology Austria Institute of Robotics and Mechatronics Germany

The concept of priority has been introduced to the robotic systems in 1980 s as an effort to overcome problems caused by singularity and it has attracted considerable attention from both the robotics and control societies. However, none of the previous works have successfully addressed two fundamental degenerate properties of singularity: nonsmoothness and imperfect inversion. This technical note proposes a prioritized output tracking control method that guarantees the ultimate boundedness and the convergence of higher priority outputs when the input-output feedback linearization becomes nonsmooth and imperfect by singularity. For that purpose, we firstly prioritize the input-output feedback linearization and discuss how two degenerate properties can occur when the system becomes singular. Then, we introduce the differential inclusion to deal with the nonsmooth internal dynamics and establish a condition using the upper Dini derivative for asymptotically stable zero dynamics. Also, we propose a strictly positive realness-based feedback gain design and find a condition related to an M-matrix in order to handle nonlinearities that appear in the imperfect feedback linearization. Finally, we combine these two results and show the ultimate boundedness and the output convergence. Additionally, we provide a motivational example with a planar four-link manipulator to illustrate the effectiveness and limitations of the proposed method. © 1963-2012 IEEE.

关键词： Feedback linearization

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Opposition-based multi-objective ant colony optimization framework for sustainable retrofitting: time–cost–energy–risk trade-offs

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Asian Journal of Civil engineering 2025年第5期26卷 2223-2239页

作者： Pokkuluri, Kiran Sree Chauhan, Trupti Ravindra Macedo, Víctor Daniel Jiménez Manjunath, T.C. Bharadwaj, Manish Sethi, Krushna Chandra Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Design Manipal School of Architecture and Planning Manipal Academic of Higher Education Manipal Karnataka Udupi India Mechanical Engineering Faculty Michoacan University of Saint Nicholas of Hidalgo Morelia Mexico Department of Computer Science and Engineering Rajarajeswari College of Engineering Bengaluru India Department of Management Studies Atal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior India Civil Engineering Department Centurion University of Technology and Management Gajapati India

The increasing emphasis on sustainable retrofitting of buildings has brought attention to the need for optimizing trade-offs among critical objectives, such as time, cost, energy consumption, and risk. This study proposes an opposition-based multi-objective ant colony optimization (OB-MOACO) framework to address these challenges effectively. The framework incorporates opposition-based learning to enhance solution exploration and convergence in multi-objective optimization problems, specifically tailored for sustainable retrofitting projects. The study develops a time–cost–energy–risk trade-off (TCERT) model with four objectives: minimizing retrofitting time, cost, energy consumption, and risks. The model integrates constraints related to budget, project timelines, and energy efficiency to ensure feasibility and sustainability. A detailed case study of retrofitting a mixed-use building is presented, encompassing 11 aspects such as structural reinforcement, energy efficiency enhancement, and accessibility improvements. Results demonstrate the superiority of OB-MOACO over conventional methods like NSGA-III, MODE, and MOPSO in achieving Pareto-optimal solutions. Key performance metrics, including hypervolume (HV) and inverted generational distance (IGD), highlight the model's efficiency in balancing competing objectives. The study contributes to advancing sustainable retrofitting practices by providing an innovative, cost-effective, and energy-efficient decision-making framework. Implications for practitioners and policymakers are discussed, alongside recommendations for future research. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Multi-objective optimization Opposition-based ant colony optimization Pareto optimization Sustainable retrofitting Time–cost–energy–risk trade-off

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Enhanced slime mould algorithm with chaotic and orthogonal optimization-based learning for improved severity prediction accuracy in malaria patient outcomes

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computers in Biology and Medicine 2025年第Pt B期192卷 110302-110302页

作者： Conteh, Ibrahim Musa Du, Qingguo School of Information Engineering Wuhan University of Technology Wuhan China Department of Computer Science Faculty of Engineering and Technology Earnest Bai Koroma University of Science and Technology Magburaka Sierra Leone

Malaria remains a critical health challenge in developing countries, particularly in Africa, where it disproportionately affects vulnerable populations. Accurate malaria severity prediction is important for proper treatment and improved patient survival rates. This study proposes an improved Slime Mould Algorithm (iSMA) to address the limitations of the standard SMA, including slow convergence, poor initialization, local optima entrapment, and imbalance between exploration and exploitation. The iSMA incorporates four key innovations: a Chaotic Initialization Strategy (CIS) to generate a diverse initial population, enhancing solution diversity;an enhanced Opposition-Based Learning (eOBL) mechanism to strengthen exploration;an Orthogonal Learning (OL) strategy to improve the balance between exploration and exploitation while accelerating convergence;and a Restart Strategy (RS) to reinitialize underperforming individuals, effectively preventing entrapment in local optima. Integrating the Random Forest (RF) feature selection process with the Support Vector Machine (SVM) classifier, forming the proposed new model RF-iSMA-SVM, classifies malaria into severe and no-severe cases. The iSMA performed better than nine other recent metaheuristic algorithms, as was broadly exhibited in the benchmark tests conducted on CEC2022 functions. When testing the RF-iSMA-SVM model on malaria datasets from health centers in Sierra Leone, the proposed model demonstrated superior predictive performance compared to recent conventional methods. The results yield an accuracy value of 0.981, specificity of 0.827, sensitivity of 0.974, and a Matthews Correlation Coefficient (MCC) score of 0.794. This shows the significant improvement over the standard techniques used for predicting the severity of malaria, which indicates great potential for healthcare practices and decision-making. These results support the model's reliability and effectiveness in improving the prediction of malaria severity. Th

关键词： Diseases

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Control of Master-Slave DC Microgrid with Optical Fiber Communication Link 15

Control of Master-Slave DC Microgrid with Optical Fiber Comm...

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15th International Renewable Energy Congress, IREC 2025

作者： Alfergani, Asma Shaltami, Farhat M. Alfaitor, Khalid Ateea Khalil, Ashraf Mohamed, Faisal University of Benghazi Electrical and Electronics Engineering Department Benghazi Libya College of Electrical and Electronics Technology - Benghazi Benghazi Libya University of Benghazi Faculty of Information Technology Department of Computer Networks and Communications Benghazi Libya Dtu Engineering Technology Technical University of Denmark Kongens LyngbyDK-2750 Denmark Authority of Natural Science Research and Technology Tripoli Libya

ISBN: (纸本)9798331516772

In this paper, a model for the master-slave DC Microgrid over a fiber optic link is proposed. The delay margin is calculated according to Lyapunov-Krasovskii functional using the LMI toolbox in Matlab with the binary iteration algorithm. The optical fiber link is implemented using optisystem simulator. The optical fiber link is set to transmit the digitalized reference current from master controller to the slave controller. The link tested at 100Mbps, 1Gbps and 2.5Gbps data rate values and several optical fiber lengths and wavelength of 1550nm and 1400nm. The results show that the performance of the link is sufficient at received power more than -30dBm for all data rate values at 1550nm. While at 1400nm, the attenuation rate is very high. Therefore, the link must be 10km to achieve acceptable performance. This wavelength is used in this simulation to avoid the C band and O band, which used for telecommunication purposes. © 2025 IEEE.

关键词： MATLAB

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Optimizing Inventory Management using Demand Forecasting based Ensemble Stacking Model with Hyperparameter Tuning 1

Optimizing Inventory Management using Demand Forecasting bas...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Pavithra, V. Abbas, Haayder M. Manjula, B.M. Chidambaram, P.K. Jothilakshmi, S. Srm Institute of Science and Technology Faculty of Science and Humanities Department of Computer Science and Applications Ramapuram Chennai India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Information Technology Mamallapuram India

ISBN: (纸本)9798331529246

In recent days, the effective inventory management relies on accurate demand forecasting for effective management of business balance stocks. However, the traditional forecasting methods often struggled with different complexities such as variability, overstocking and wasted resources. In this research, the Ensemble Stacking Model with Hyperparameter Tuning (En-Stacking-HT) is proposed for effective optimized inventory management. Initially, the data is collected from M5 accuracy completion dataset and further preprocessed with data imputation, z-score normalization. These preprocessing techniques handles the missing values in data and standardizes the data in significant range. Then, Seasonal and Trend Decomposition (STL) is employed for extracting seasonality and trend features from preprocessed data by using Locally Estimated Scatterplot Smoothing (LOESS). Finally, the proposed En-Stacking-HT is utilized for inventory management prediction and optimized through hyperparameter tuning for predicting effective optimized inventory management for demand forecasting. From the results, the proposed En-Stacking-HT attained better results in terms of Mean Absolute Error (MAE) (16.82), Root Mean Square Error (RMSE) (22.85) and Mean Absolute Percentage Error (MAPE) (4.97%) when compared to existing Random Forest (RF). © 2025 IEEE.

关键词： demand forecasting ensemble stacking model hyperparameter tuning optimized inventory management seasonality and trend decomposition

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Innovative metallization pattern and technique for industrial rear emitter p-type tunnel oxide passivated contact solar cells

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Journal of Industrial and engineering Chemistry 2025年 147卷 628-635页

作者： Khokhar, Muhammad Quddamah Yousuf, Hasnain Rahman, Rafi Ur Aida, Maha Nur Madara, Polgampola Chamani Jony, Jaljalalul Abedin Dao, Vinh-Ai Pham, Duy Phong Yi, Junsin Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Future Energy Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Physics Faculty of Applied Sciences HCMC University of Technology and Education Ho Chi Minh City700000 Viet Nam College of Information and Communication Engineering Sungkyunkwan University Suwon16419 Korea Republic of

In this study, we aim to enhance the efficiency of p-type tunnel oxide passivated contact (p-TOPCon) solar cells by improving charge carrier extraction while achieving higher fill factor (FF) and open circuit voltage (Voc). We explore various potential methods for the photovoltaic industrial procedure, including optimizing metallization patterns and techniques, such as the utilization of a high efficiency grid pattern (HEGP) in place of the conventional straight finger-line pattern (SFLP). Co-firing conditions, peak firing temperature, and belt speed are investigated for their impact on FF enhancement, along with evaluating the effects of single and double print processes. Furthermore, we examine the efficacy of Nafion treatment as a surface modification technique to enhance FF. Experimental results indicate that optimizing co-firing conditions and employing Nafion treatment significantly increased FF to approximately 83.26% in p-TOPCon solar cells. Adjusting peak firing temperature and belt speed, as well as implementing the double print process, demonstrate superior FF enhancement compared to the single print process. Additionally, Nafion treatment contributes to FF improvement by passivating surface defects and enhancing charge carrier collection. Overall, this study provides valuable insights into optimizing co-firing conditions and surface treatment techniques to enhance p-TOPCon solar cell efficiency (Eff) up to 23.50%, thus advancing high-efficiency photovoltaic technology. © 2024 The Korean Society of Industrial and engineering Chemistry

关键词： Metallizing

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