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International Conference on Electrical, Computer and Communication engineering (ECCE)

作者： Md Abu Talha Sheikh Ishaque Riajul Karim Adil Mahmudul Hasan Saron Abdul Al Mahmud Riaz Shaikat Ahmed Al Shahriar Zishan Department of Electrical and Electronics Engineering (EEE) Faculty of Engineering (FE) American International University-Bangladesh (AIUB) Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Faculty of Engineering (FE) Ahsanullah University of Science and Technology (AUST) Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Faculty of Science & Technology (FST) American International University-Bangladesh (AIUB) Dhaka Bangladesh Faculty of Computing Engineering and Science Computer Science (CS) University of South Wales UK

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Efficient parking management is a significant challenge in urban areas due to increasing vehicle density and limited space availability. This paper presents an automated parking management system that integrates real-time image processing and Optical Character Recognition (OCR) to detect vacant parking spaces and identify vehicle number plates, including both Bengali and English formats. The system utilizes deep learning models to address challenges such as low lighting, unstructured parking layouts, and diverse number plate formats. It is designed with a robust backend infrastructure, enabling real-time updates and parking space reservations. Tested in both simulated and real-world environments, the system achieves a parking space detection accuracy of 95.6% and a number plate recognition accuracy of 92.3%, with an average response time of 2.4 seconds for updating parking availability. This system provides a scalable and efficient solution for improving urban parking management.

关键词： Space vehicles Deep learning Accuracy Layout Urban areas Lighting Automated parking Real-time systems Time factors License plate recognition

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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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Computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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A Bio-Inspired Metaheuristics Algorithm for Reduction of Coverage Holes in Optimizing the Performance of Next Generation Wireless Network (NGWN) 2nd

A Bio-Inspired Metaheuristics Algorithm for Reduction of Cov...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ajibade, Samuel-Soma M. Abdul Majeed, Anwar P. P. Oyebode, Oluwadare Joshua Jasser, Muhammed Basheer Ajayi, Babatunde Adedotun Alebiosu, David Olayemi Luo, Yang Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan47500 Malaysia Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Petaling Jaya47500 Malaysia Civil and Environmental Engineering Afe Babalola University Ekiti Ado Ekiti Nigeria Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Petaling Jaya47500 Malaysia Department of Cyber Security Faculty of Computing and Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

While Next Generation Wireless Networks are widely used, controlling them can be challenging regardless of whether a network is fully connected, there may be coverage gaps. In this study, we provide a technique for placing nodes in a wireless network while making sure that there is a minimum coverage hole in the network coverage area. We use the firefly algorithm to close the coverage gap in order to increase connectivity. The Firefly algorithm provides a novel solution for overcoming the drawbacks of traditional overburden hole detection techniques, such as downtime, low accuracy, and high operating costs, by installing sensor nodes in the appropriate locations and scheduling those network devices. The algorithm looks for the best locations where the NGWN sensor may be placed inside the designated zone in order to reduce the coverage hole area. The algorithm will treat randomly generated solutions as fireflies, and it will determine how bright they are based on how well they perform in terms of the goal function. Simulation results show that the proposed method decreases the covering hole area by 96%. The provided approach was suitable for NGWN since it reduced computation time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Operating costs

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File system performance comparison with KVM under different disk layout complexity 24

File system performance comparison with KVM under different ...

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Dordević, Borislav Janjić, Kristina Kraljević, Nenad Davidović, Nikola Institute Mihailo Pupin Belgrade Serbia School of Electrical and Computing Engineering of Applied Studies Belgrade Serbia University of East Sarajevo Faculty of Electrical Engineering RS East Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798331515799

This paper focuses on comparing file system performance under various disk layout complexities using the same hypervisor. KVM was selected as the hypervisor for this study, and two distinct disk layout complexities were chosen for the guest operating system side. Filebench was the benchmarking tool used due to its remarkable flexibility in simulating realistic application and server behaviors. CentOS 9 was used as the guest operating system, representing the Linux family. The experimental tests involved running one, two, three, and four virtual machines simultaneously. The paper study includes a mathematical model of the virtual environment with different disk layout complexities, followed by a real experiment presented as a case study. This model is used to interpret the results obtained from the experiment. © 2025 IEEE.

关键词： Linux

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Enabling Quadruple-D Compensation Coil Integration for Efficient Power Transfer to Receiver in Wireless Power Transfer Systems for EV Charging

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Erel, Mehmet Zahid Imtiaz, Tabish Elsanabary, Ahmed Mubin, Marizan Soon, Tey Kok Mekhilef, Saad Ankara Yildirim Beyazit University Department of Energy Systems Engineering Ankara Turkey Universiti Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Port Said University Faculty of Engineering Department of Electrical Engineering Port Said Egypt Universiti Malaya Department of Computer System and Technology Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Swinburne University of Technology School of Science Computing and Engineering Technologies Hawthorn Australia Presidency University Department of Electrical and Electronics Engineering Karnataka Bengaluru India

Wireless power transfer (WPT) presents a promising approach for charging electric vehicles (EVs), offering increased convenience, reliability, and safety for EV customers. Recently, the incorporation of multiple coil structures in wireless charging systems has addressed various operational challenges, enhanced power transfer, and provided a scalable solution for EVs. However, cross-coupling issues and space limitation problems arise when multiple coils are introduced to the WPT system. In this paper, a novel integrated compact coupler is proposed, including two naturally decoupled coils on the transmitter side, while both are coupled with the receiver coil to improve the power transfer capability of the system. ANSYS Maxwell is employed to simulate the proposed coupler and evaluate its performance under misalignment operations. The proposed wireless charging system inherently detects misalignment direction based on the variation of output voltage with respect to x-misalignment. As a result, the experimental prototype of the designed wireless WPT system presents a peak efficiency of 97.08% at 1.147-kW output power for an air gap of 120 mm under fully-aligned condition. Additionally, the designed compensation coil can transfer power of 243.76-W under fully-aligned condition and increases reliability in power transfer during misalignment operations. © 2013 IEEE.

关键词： Inductive power transmission

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FragMangro: A cross-domain zero-shot model for monitoring fragmented mangrove ecosystems

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Journal of King Saud University - Computer and Information Sciences 2025年第4期37卷 1-22页

作者： Zhang, Ruoxin An, Angela Cen, Mingcan Zhang, Chong Huangfu, Aimee Vinnakota, Sunny Song, Shuxiang Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin 541004 China Key Laboratory of Integrated Circuits and Microsystems (Guangxi Normal University) Education Department of Guangxi Zhuang Autonomous Region Guilin 541004 China Faculty of Science Engineering and Built Environment Deakin University Geelong VIC Australia Academies Australasia Polytechnic 628 Bourke Street Melbourne 3000 Australia

Monitoring fragmented mangrove ecosystems presents significant challenges due to their sparse distribution and the limitations of traditional detection methods, which often suffer from poor convergence and high rates of false positives and false negatives. To address these issues, we propose FragMangro, a cross-domain zero-shot detection model designed to enhance the accuracy and generalization of mangrove detection. FragMangro integrates parallel multi-scale convolution for progressive dimensional upscaling, improving spectral feature extraction. Additionally, it employs a dynamic learning rate and iterative control algorithm, which adaptively adjusts learning rates and training epochs based on loss monitoring to prevent suboptimal convergence. We conducted extensive experiments in three major mangrove reserves in Guangxi, China. The results demonstrate that FragMangro achieves over 98% accuracy, an average F1 Score exceeding 85%, and the Intersection over Union(IoU) surpassing 74%, significantly outperforming conventional methods such as support vector machines (SVM), random forests (RF), and k-nearest neighbors (KNN). Specifically, in the Beilun estuary, FragMangro exhibits a 0.96% improvement in accuracy over RF, a 17.24% higher F1 Score than SVM, and an 18.08% higher IoU than KNN. Furthermore, the model’s adaptive learning rate and dynamic adjustment strategy enhance convergence efficiency and feature representation. FragMangro serves as an effective tool for ecological monitoring and mangrove conservation, with broader implications for environmental protection and sustainable ecosystem management. © The Author(s) 2025.

关键词： Adaptive learning rate and iteration rounds Attention fully convolutional network Deep learning Scattered mangroves Zero-shot detection

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Multi-party bidirectional teleportation through squeezed noisy channels

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Optik 2025年 327卷

作者： Seida, C. El Allati, A. Ali, A. Al-Kuwari, S. Laboratory of R&D in Engineering Sciences Faculty of Sciences and Techniques Al-Hoceima Abdelmalek Essaadi University Tetouan Morocco Qatar Center for Quantum Computing College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

In this paper, we investigate the squeezing and the decoherence effect on the multi-part bidirectional quantum teleportation (Mb-QT) of single qubit states. We consider three realistic situations: first, the squeezed generalized amplitude damping (SGAD) channel affects the entangled channel of the Mb-QT protocol;secondly, the initial states are affected by SGAD. Thirdly, SGAD affects the entangled channel and the initial states at the same time. We evaluate the geometric means of the bipartite concurrences, as a monotonic measure of entanglement, and the teleportation fidelity. Our results show that these quantities are independent of the squeezing parameter. Furthermore, increasing the number of thermal photons amplifies the entanglement and increases the efficiency of teleportation in the first situation. However, in the last two situations, an increase in the number of thermal photons decreases the teleportation fidelity. © 2025 Elsevier GmbH

关键词： Quantum channel

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R-WhONet: recalibrated wheel odometry neural network for vehicular positioning using transfer learning

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Neural computing and Applications 2025年第9期37卷 6547-6565页

作者： Onyekpe, Uche Szkolnik, Alicja Palade, Vasile Kanarachos, Stratis Fitzpatrick, Michael E. Research Centre for Computational Science and Mathematical Modelling Coventry University Priory Road CoventryCV1 5FB United Kingdom African Institute for Artificial Intelligence Accra North Legon Ghana University of Birmingham BirminghamB15 2TT United Kingdom Faculty of Engineering and Computing Coventry University Priory Road CoventryCV1 5FB United Kingdom

This paper proposes a transfer learning approach to recalibrate our previously developed Wheel Odometry Neural Network (WhONet) for vehicle positioning in environments where Global Navigation Satellite Systems (GNSS) are unavailable. The WhONet has been shown to possess the capability to learn the uncertainties in the wheel speed measurements needed for correction and accurate positioning of vehicles. These uncertainties may be manifested as tyre pressure changes from driving on muddy and uneven terrains or wheel slips. However, a common cause for concern for data-driven approaches, such as the WhONet model, is usually the inability to generalise the models to a new vehicle. In scenarios where machine learning models are trained in a specific domain but deployed in another domain, the model’s performance degrades. In real-life scenarios, several factors are influential to this degradation, from changes to the dynamics of the vehicle to new pattern distributions of the sensor’s noise, and bias will make the test sensor data vary from training data. Therefore, the challenge is to explore techniques that allow the trained machine learning models to spontaneously adjust to new vehicle domains. As such, we propose the Recalibrated-Wheel Odometry neural Network, based on transfer learning, that adapts the WhONet model from its source domain (a vehicle and environment on which the model is initially trained) to the target domain (a new vehicle on which the trained model is to be deployed). Through a performance evaluation on several GNSS outage scenarios—short-term complex driving scenarios such as on roundabouts, sharp cornering, hard-brake and wet roads (drifts), and on longer-term GNSS outage scenarios of 30s, 60s, 120s and 180s duration—we demonstrate that a model trained in the source domain does not generalise well to a new vehicle in the target domain. However, we show that our new proposed framework improves the generalisation of the WhONet model to new vehicles in

关键词： Inertial navigation systems

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CNN-Based Circular Economy in E-Waste Management with Blockchain and IoT Integration

CNN-Based Circular Economy in E-Waste Management with Blockc...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Santhuja, P. Suresh, A. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

Increasing electronic waste (e-waste) is a serious environmental and financial problem that calls for creative ideas for effective resource recovery and recycling. This paper proposes an IoT- and Blockchain-integrated e-waste management system based on Convolutional Neural Networks (CNNs) to boost automated waste classification and, thus, circular economy practices. While Blockchain guarantees open and safe tracking of waste transportation, IoT sensors and RFID tags allow real-time monitoring of e-waste. Training on a collection of 50,000 e-waste images, a CNN-based image classification model attained an accuracy of 96.2% in classifying components into recyclables, reusables, and hazardous items. Using automated decision-making, the system showed a 28% reduction in processing time and a 32% gain in sorting efficiency over conventional approaches. Blockchain incorporation enhanced traceability through 100% secure transaction records, lowering fraud and illegal disposal. Experimental data show that this method improves 23% resource recovery rates by promoting sustainable e-waste management. The proposed architecture minimizes environmental effects by encouraging ethical e-waste disposal and a closed-loop recycling system. These results show how Blockchain-secured, AI-driven IoT devices might help to advance circular economic ideas for world e-waste management. © 2025 IEEE.

关键词： Electronic Waste

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Optimizing Cotton Yield Forecasting With Deep Learning and Multispectral Remote Sensing

Optimizing Cotton Yield Forecasting With Deep Learning and M...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Srinivas, Porandla Suresh, A. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

Precise forecasting of cotton yields is crucial for enhancing agricultural productivity and optimizing supply chain efficiency. Traditional forecasting techniques often inadequately account for intricate environmental interactions that influence crop development. This research utilizes ResNet-50, a deep Convolutional Neural Network (CNN), to examine multispectral remote sensing data for accurate yield estimates. The approach analyses high-resolution satellite images, extracting essential vegetation indicators and spatial patterns to improve forecasting precision. The system underwent training and validation using a dataset including 10,000 multispectral images representing diverse climatic and soil conditions. Experimental findings indicate that the ResNet-50 model achieved an accuracy of 98%, achieved by an R2 score of 0.96, indicating a robust connection between predicted and actual yield values. The Root Mean Square Error (RMSE) was reduced to 0.59 tons per acre, improving traditional regression-based models. A comparative examination using other CNN architectures revealed that ResNet-50 enhanced accuracy compared to traditional deep learning models. These results underscore the possibility of integrating deep learning with remote sensing for accurate yield prediction. The proposed approach improves data-driven decision-making for farmers and policymakers, facilitating sustainable precision agriculture and efficient resource management. © 2025 IEEE.

关键词： Precision agriculture

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