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IEEE Networking Letters 2025年第1期7卷 56-60页

作者： Mishra, Shruti Aggarwal, Virat Pal, Sujata Agarwal, Vidushi Indian Institute of Technology Ropar Department of Computer Science and Engineering Rupnagar140001 India

Payment channels support off-chain transactions by enhancing transaction speed and reducing fees in the main blockchain. However, the costs and complexity of the network increase as we increase the size of the network. To address these challenges, we propose Heuristic-Based Routing with Scheduling (HeRo) combining heuristic-based routing and scheduling techniques in Payment Channel Networks (PCNs). HeRo achieves a cost reduction of 32.71% and 73.08% compared with multi-charge PCN (MPCN-RP) and Dijkstra algorithms, respectively. © 2019 IEEE.

关键词： Network routing

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EARLY PREDICTION OF ALZHEIMER'S DISEASE USING MAGNETIC RESONANCE IMAGES (MRI) IN LEVERAGING DEEP LEARNING MODELS 2

EARLY PREDICTION OF ALZHEIMER'S DISEASE USING MAGNETIC RESON...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Kalpana, T. ThamilSelvan, R. Chitra, K. Rojaa, K.M. Santhiya, M. Sowmiyavarshan, A.D. Department of Computer Applications Kongu Engineering College Erode India Department of Computer Science and Engineering Kongu Engineering College Erode India

ISBN: (纸本)9781837242672

Alzheimer's disease is a well-known illness characterized by memory loss and cognitive decline. Since current treatments work best in the early stages, early detection is vital for effective management. The Magnetic Resonance Imaging (MRI) plays a key role in diagnosing Alzheimer's by providing detailed images of brain structures. Advances in deep learning have further improved MRI-based diagnosis through automated image ***2 and MobileNetV3 are lightweight convolutional neural networks (CNNs) known for their high performance and speed, making them ideal for resource-limited and real-time ***3 is a widely used CNN, known for its strong performance in image classification tasks. By training these deep learning models on complete MRI datasets, they offer the potential to aid doctors in making accurate and timely diagnoses. This approach couldlead to better outcomes and improved quality of life for *** research aims to enhance early detection and treatment methods for Alzheimer's disease, contributing to advancements in deep learning models. © The Institution of engineering & Technology 2024.

关键词： Convolutional neural networks

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Rice spikelet’s disease detection using hybrid optimization model and optimized CNN

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Soft Computing 2024年第21期28卷 12787-12806页

作者： Patel, Bharati Department of Computer Science and Engineering Faculty of Science and Technology ICFAI University Raipur India

Rice fields all across the world are affected by spikelet sterility, often known as rice spikelet's disease. It is characterized by the improper development of spikelet’s, which lowers grain output and quality. For optimal management and the avoidance of yield losses, this disease must be discovered early. In this study, a deep learning-based approach utilizing optimization techniques was proposed for accurate segmentation and disease detection in rice crops. The research aimed to address the vulnerability of rice crops to disease attacks from seed germination to mature spikelets. The proposed model consisted of three major phases: pre-processing, segmentation, feature extraction and disease detection. Gaussian filtering was employed to preprocess the raw rice crop images, while a new hybrid Whale Customized Gravitational Optimization Algorithm was utilized for segmentation. Disease detection was performed using a hybrid deep learning model called HybridNet, which combines convolutional neural networks (CNN) with an optimized recurrent neural network (RNN) model. The dataset intended for this proposed project is sourced from the CABI PlantwisePlus Knowledge Bank. The proposed model for rice spikelet disease detection achieved high accuracy with Sensitivity (0.9269), Specificity (0.9756), and Accuracy (0.9634). This indicates that the model effectively identifies and detects diseases in rice spikelets, demonstrating its reliable performance in disease management and crop protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Forecasting electricity demand in Türkiye: A comprehensive review of methods, determinants, and policy implications

Journal of Energy Systems

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Journal of Energy Systems 2025年第1期9卷 132-158页

作者： Elbaş, Hakan Bilgin, Turgay Tugay Bursa Technical University Graduate School Department of Intelligent Systems Engineering Bursa Turkey Bursa Technical University Faculty of Engineering and Natural Sciences Department of Computer Engineering Bursa Turkey

This review examines the methods, determinants, and forecasting horizons used in electricity demand forecasting in Türkiye. The study investigates how Türkiye's electricity demand is influenced by economic, climatic, socio-demographic, and technological factors, and explores the evolving landscape of forecasting techniques, from traditional statistical models to advanced machine learning and hybrid approaches. The research addresses three key questions: The significant determinants of electricity demand in Türkiye, the most effective forecasting methods, and the application of these insights to improve energy planning and policy development. Through a systematic analysis of peer-reviewed literature, official reports, and case studies, the study reveals the complex interplay of factors affecting electricity demand and the increasing sophistication of forecasting methodologies. Economic growth, industrial production, climate change, urbanization, and technological advancements emerge as primary drivers of demand, while artificial neural networks and hybrid models demonstrate superior forecasting capabilities. The study highlights the importance of integrated modeling approaches, sector-specific strategies, and the incorporation of climate projections in long-term planning. It also emphasizes the need for aligning energy policies with broader economic and environmental objectives. This review provides valuable insights for researchers, policymakers, and industry stakeholders, offering a comprehensive framework for understanding and improving electricity demand forecasting. © 2025 Erol Kurt. All rights reserved.

关键词： Demand response

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Protein data in the identification and stage prediction of bronchopulmonary dysplasia on preterm infants: a machine learning study

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International Journal of Information Technology (Singapore) 2024年第1期16卷 387-392页

作者： Jeyananthan, Pratheeba Bandara, K.M.D.D. Nayanajith, Y.G.A. Department of Computer Engineering Faculty of Engineering University of Jaffna Jaffna Sri Lanka

Bronchopulmonary Dysplasia (BPD) is a chronic lung disease mostly affecting the premature newborns who are in the need for oxygen therapy. Main reason for this disease is underdeveloped lungs which need the help of ventilator to expand and breath. This is a very serious disease without a specific test for diagnosis. As treatment is very important for this disease in order to improve the lung function of the baby, on time diagnosis is crucial. Hence, this study checks the potential of protein data in the diagnosis of BPD, and also in the prediction of BPD stage. Mutual information is used in the selection of relevant features of each study. Selected set of features are used with different machine learning algorithms and the accuracies among the models are compared. By this comparison, this study reveals the best number of features in each of the prediction along with the best machine learning algorithm. The highest accuracy value obtained in the diagnosis model shows that it can be used in practice to improve the diagnosis accuracy. However, it seems that prediction of the disease stage is a far complex problem which needs further improvement in its accuracy. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Bronchopulmonary dysplasia (BPD) Feature selection GO analysis Machine learning models Performance comparison Protein data

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A metaheuristic algorithm based on a radial basis function neural networks

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Neural Computing and Applications 2024年第35期36卷 22119-22147页

作者： Rivera-Aguilar, Beatriz A. Cuevas, Erik Zaldívar, Daniel Pérez-Cisneros, Marco A. Department of Electronic and Computer Engineering University of Guadalajara CUCEI Jalisco Guadalajara44430 Mexico

Metaheuristic methods are optimization techniques generally based on natural or physical phenomena. Although these processes inspire, the underlying mechanisms of these phenomena still need to be fully understood. Translating these mechanisms into algorithmic solutions may lead to unclear strategies, whose performance can exhibit unpredictable behavior. On the other hand, a radial basis function neural network (RBFNN) is a powerful method for approximating highly complex functions through a training process. Its key advantage lies in its interpretable structure, which allows us to understand the underlying relationships among the input–output data. This paper proposes a new metaheuristic algorithm based on the architecture of an RBFNN. Under this approach, an RBFNN is trained to approximate the objective function based on initial input data distributed from the search space. After training, the radial basis functions and weights associated with the neurons that maintain a strong influence or contribution to obtain the highest values of the cost function are determined. The parameters of these radial basis functions define the promissory regions of the input space that are used to produce the individuals of the new population through the Latin hypercube sampling technique. Therefore, during the optimization process, different regions of the search space represented by radial basis functions are explored and exploited as the accuracy of the cost function approximation increases. The experimental results demonstrated the success of our approach, as it outperformed the established metaheuristic algorithms on various benchmark functions. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Cost functions

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A Privacy-Preserving Data Aggregation Protocol for Internet of Vehicles with Federated Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Xu, Zisang Zhang, Ruirui Liang, Wei Li, Kuan-Ching Gu, Ke Li, Xiong Huang, Jialun Computer and Communication Engineer Institute Changsha University of Science and Technology Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Department of Computer Science and Information Engineering Providence University Taichung Taiwan Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Federated learning (FL) is widely used in various fields because it can guarantee the privacy of the original data source. However, in data-sensitive fields such as Internet of Vehicles (IoV), insecure communication channels, semi-trusted RoadSide Unit (RSU), and collusion between vehicles and the RSU may lead to leakage of model parameters. Moreover, when aggregating data, since different vehicles usually have different computing resources, vehicles with relatively insufficient computing resources will affect the data aggregation efficiency. Therefore, in order to solve the privacy leakage problem and improve the data aggregation efficiency, this paper proposes a privacy-preserving data aggregation protocol for IoV with FL. Firstly, the protocol is designed based on methods such as shamir secret sharing scheme, pallier homomorphic encryption scheme and blinding factor protection, which can guarantee the privacy of model parameters. Secondly, the protocol improves the data aggregation efficiency by setting dynamic training time windows. Thirdly, the protocol reduces the frequent participations of Trusted Authority (TA) by optimizing the fault-tolerance mechanism. Finally, the security analysis proves that the proposed protocol is secure, and the performance analysis results also show that the proposed protocol has high computation and communication efficiency. IEEE

关键词： Vehicles

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Multilevel Thresholding-based Medical Image Segmentation using Hybrid Particle Cuckoo Swarm Optimization

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Recent Advances in computer Science and Communications 2024年第5期17卷 12-23页

作者： Kumar, Dharmendra Solanki, Anil Kumar Ahlawat, Anil Kumar Department of Computer Science & Engineering KIET Group of Institutions NCR Delhi Ghaziabad India Department of Computer Science & Engineering Bundelkhand Institute of Engineering and Technology Jhansi India Affiliated to Dr. APJ Abdul Kalam Technical University Lucknow India

Background: The most important aspect of medical image processing and analysis is image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including object representation and characterization, measuring of features, and even higher-level procedures. The problem with image segmentation is recognition and perceptual completion while segmenting the image. However, these issues can be resolved by multilevel optimization techniques. However, multilevel thresholding will become more computationally intensive with increasing thresholds. Optimization algorithms can resolve these issues. Therefore, hybrid optimization is used for image segmentation in this research work. Methods: The researchers propose a Multilevel Thresholding-based Segmentation using a Hybrid Optimization approach with an adaptive bilateral filter to resolve the optimization challenges in medical image segmentation. The proposed model utilizes Kapur's entropy as the objective function in the nature-inspired optimization algorithm. Results: The result is evaluated using parameters such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The researchers perform result analysis with variable thresholding levels on KAU-BCMD and mini-MIAS datasets. The highest PSNR, SSIM, and FSIM achieved were 31.9672, 0.9501, and 0.9728 respectively. The results of the hybrid model are compared with state-of-the-art models, demonstrating its efficiency. Conclusion: The research concludes that the proposed Multilevel thresholding-based segmentation using a Hybrid Optimization approach effectively solves optimization challenges in medical image segmentation. The results indicate its efficiency compared to existing models. The research work highlights the potential of the proposed hybrid model for improving image processing and analysis in the medical field. © 2024 Bentham Science Publishers.

关键词： Image segmentation

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Enhancing E-Commerce Text Classification: A GRU-Based Approach for Improved Product Understanding 5th

Enhancing E-Commerce Text Classification: A GRU-Based Approa...

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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Nazmul Abdal, Md. Shaown, Shahanaz Islam Islam, Khairum Aman, Rutba Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Uttara University Dhaka Bangladesh Department of Computer Science and Engineering University of Dhaka Dhaka Bangladesh

ISBN: (纸本)9789819791118

In the burgeoning landscape of e-commerce, the ability to accurately classify product texts is paramount for enhancing user experience and driving business success. Traditional approaches to text classification often struggle with the nuances and complexities inherent in e-commerce product descriptions. In this paper, we propose a novel approach utilizing Gated Recurrent Unit (GRU) to address these challenges and improve product understanding in e-commerce text classification tasks. Our model leverages the inherent sequential nature of product descriptions, effectively capturing long-range dependencies and semantic relationships within the text. We use a standard dataset in extended trials to demonstrate the superiority of our GRU-based approach over conventional methods in terms of classification accuracy and robustness across diverse product categories. Furthermore, we conduct comprehensive analyses to gain insights into the inner workings of our model and its ability to learn meaningful representations of e-commerce text data. The performance of the model is compared using several cutting-edge techniques, including Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) in order to show that our model is superior at correctly classifying e-commerce texts. The experimental findings show that the suggested model performs competitively in classifying e-commerce texts, surpassing other approaches with an accuracy of 98.35%. Our findings underscore the potential of GRU-based approaches for advancing the state-of-the-art in e-commerce text classification, offering promising avenues for future research and practical applications in the domain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Marketplaces

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A novel trusted hardware-based scalable security framework for IoT edge devices

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Discover Internet of Things 2024年第1期4卷 1-26页

作者： Khan, Mohd Hatami, Mohsen Zhao, Wenfeng Chen, Yu Department of Electrical and Computer Engineering Binghamton University BinghamtonNY13902 United States

The Internet of Things (IoT) devices are pervasively deployed and embedded into our daily lives. Over several years, the massive assimilation of IoT devices has given rise to smart cities, smart factories, smart farms, etc. While IoTs can collect immense amounts of user and environmental information, the lack of adequate security measures is among the major concerns about information security and privacy. With the ever-increasing presence of IoTs, information flow between IoT devices in a single or multi-layered network is becoming increasingly vulnerable to attacks. Although several software-based solutions have been developed over the years that improve the security of resource-constrained IoT devices, there are limited efforts on hardware-oriented IoT security solutions. This paper proposes a Trusted HArdware-based Scalable Secure Architecture (THASSA) for IoT Edge systems. Leveraging standard cryptographic algorithms, THASSA provides a secure mechanism for data transfer and off-loads cryptography operations to trusted hardware. The modularized THASSA architecture allows devices to be added or removed without user intervention. Also, THASSA could be scaled to multiple subsystems using multiple trusted hardware to form IoT clusters with secure data path(s) for mission-critical applications. An experimental study has verified the correctness and effectiveness of THASSA. © The Author(s) 2024.

关键词： Internet of things

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