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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Alzeyadi, Haider Duran, Fecir Informatics Institute Department of Computer Science Gazi University Ankara Turkey Gazi University Engineering Faculty of Technology Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9781665493604

Sophisticated cyber threats are seen on Online Social Networks (OSNs) social media accounts automated to imitate human behaviours has an impactful effect on distorting public thoughts and opinions. OSNs are weaponized to diffuse deception, misinformation, and malicious activities, that forms a serious threat to society. The deceptive nature of imitating human behaviour has become a challenging and crucial task to detect automated accounts (socialbots). This research, however, proposes a hybrid metaheuristic optimisation algorithm for socialbot detection. Specifically, a hybrid B-Hill Climbing (B-HC) optimisation algorithm works in tandem with a k-NN nearest neighbour classifier to accurately select a relevant feature subset. It is applied to be tested for fake followers account on Twitter data. Experimental results showed that the proposed method is better than the traditional and the latest feature selection techniques as well as the rule-set methods. The B-HC alongside with k-NN method achieved promising results using only relevant feature subset. © 2023 IEEE.

关键词： Feature Selection

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BAHGRF^(3):Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation

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CAAI Transactions on Intelligence technology 2025年第2期10卷 387-401页

作者： Muhammad Abrar Ahmad Khan Muhammad Attique Khan Ateeq Ur Rehman Ahmed Ibrahim Alzahrani Nasser Alalwan Deepak Gupta Saima Ahmed Rahin Yudong Zhang Foundation University Islamabad IslamabadPakistan Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl KhobarSaudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadhSaudi Arabia Department of Computer Science Maharaja Agrasen Institute of TechnologyDelhiIndia United International University DhakaBangladesh School of Informatics University of LeicesterLeicesterUK

Biometric characteristics are playing a vital role in security for the last few *** gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is *** the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and *** models are selected based on the top-5 accuracy and less number of ***,both models are trained through deep transfer learning and extracted deep features fused using a voting *** the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best *** selected features are classified using several supervised learning *** CASIA-B publicly available dataset has been employed for the experimental *** this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.

关键词： deep learning feature fusion feature optimization gait classification indoor environment machine learning

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of computer Science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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Enabling LoRa energy awareness: A Multihop One-Hop Routing Protocol for Prolonging Network Lifetime 19

Enabling LoRa energy awareness: A Multihop One-Hop Routing P...

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19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Katsidimas, Ioannis Manolopoulos, Alexandros-Ioannis Nikoletseas, Sotiris University of Patras Department of Computer Engineering and Informatics Patras Greece University of Athens Department of Physics National and Kapodistrian Athens Greece University of Patras and Computer Technology Institute and Press Diophantus Department of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9798350346497

IoT applications often rely on battery-powered nodes that may be challenging to recharge, making energy efficiency a crucial concern. In this context, routing protocols play a vital role as they dictate the paths data packets take between nodes, directly influencing the energy consumption of IoT networks. Energy-aware routing protocols have been proposed to address this issue, with the aim of optimizing energy consumption by selecting paths that minimize the energy required for data transmission and reception. These protocols employ various strategies, such as distance-based, traffic-based, and hybrid schemes, to achieve energy efficiency in IoT networks. LoRaWAnprotocol has been adopted for various IoT applications with requirements in low-power and extended coverage. Over the recent years attempts have been made to replace the typical star topology employed by LoRa with multihop solutions, in an effort to further increase its capabilities. In this paper, we present a multihop algorithm that is based on an energy-balanced routing scheme adopted by LoRa nodes to increase energy efficiency and network lifetime. Multiple one-hop routes are configured for critical nodes and a different route is selected in each transmission. Additionally, critical nodes adjust their Spreading Factor and Transmission Power according to the distance of intermediate nodes. Simulation testing results verify the improved performance of our algorithm over the direct link approach. © 2023 IEEE.

关键词： Energy efficiency

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EfficientNetB0-based Automated Diabetic Retinopathy Classification in Fundus Images 3

EfficientNetB0-based Automated Diabetic Retinopathy Classifi...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Majaw, Ezekiel Alaric Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Venkatesan Ajibesin, Adeyemi Abel Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa

ISBN: (纸本)9798331532420

The most common cause of blindness among diabetics is diabetic retinopathy. This disease must be detected in its early stages as delaying treatment can result in permanent blindness. In today’s modern world with the development of technologies, automated techniques have improved the accuracy and efficiency of detecting and classifying this disease. This study suggests a deep learning model utilizing EfficientNetB0 that focuses on designing a neural network structure that requires fewer parameters and computations compared to traditional architectures. Further to help in increasing the accuracy, the image is being processed with methods like CLAHE which is an image processing algorithm that helps in enhancing the contrast while keeping the noise of the images as low as possible. Also, augmentation is being done on the images and SMOTE is applied to handle class imbalance. We collected the APTOS Blindness Detection dataset consisting of retinal fundus images. The accuracy that is being obtained from the proposed methodology is 98.78% indicating the model's effectiveness in detection and classifying between the various stages of diabetic retinopathy. © 2024 IEEE.

关键词： Contrastive Learning

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Optimizing NOMA Transmissions to Advance Federated Learning in Vehicular Networks

Optimizing NOMA Transmissions to Advance Federated Learning ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Chen, Ziru Ni, Zhou Guan, Peiyuan Wang, Lu Cai, Lin X. Hashemi, Morteza Li, Zongzhi Illinois Institute of Technology Department of Electrical and Computer Engineering Chicago United States University of Kansas Department of Electrical Engineering and Computer Science Lawrence United States University of Oslo Department of Informatics Oslo Norway Illinois Institute of Technology Department of Civil Engineering Chicago United States

ISBN: (纸本)9798350351255

Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. However, drivers are often reluctant to share their data due to privacy concerns. The Federated Vehicular Network (FVN) is a promising technology that tackles these concerns by transmitting model parameters instead of raw data, thereby protecting the privacy of drivers. Nevertheless, the performance of Federated Learning (FL) in a vehicular network depends on the joining ratio, which is restricted by the limited available wireless resources. To address these challenges, this paper proposes to apply Non-Orthogonal Multiple Access (NOMA) to improve the joining ratio in a FVN. Specifically, a vehicle selection and transmission power control algorithm is developed to exploit the power domain differences in the received signal to ensure the maximum number of vehicles capable of joining the FVN. Our simulation results demonstrate that the proposed NOMA-based strategy increases the joining ratio and significantly enhances the performance of the FVN. Index Terms - Federated Vehicular Network, NOMA © 2024 IEEE.

关键词： Data privacy

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Cross-Layer Distributed Attack Detection Model for the IoT

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International Journal of Ambient Computing and Intelligence 2022年第1期13卷 1-17页

作者： Ahmed, Hassan I. Nasr, Abdurrahman A. Abdel-Mageid, Salah M. Aslan, Heba K. Informatics Department Electronics Research Institute Cairo Egypt Computer Engineering Faculty of Engineering Al-Azhar University Cairo Egypt Computer Engineering Department College of Computer Science and Engineering Taibah University Saudi Arabia Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Egypt

The security of IoT that is based on layered approaches has shortcomings such as the redundancy, inflexibility, and inefficiently of security solutions. There are many harmful attacks in IoT networks such as DoS and DDoS attacks, which can compromise the IoT architecture in all layers. Consequently, cross layer approach is proposed as an effective and practical security defending mechanism. Cross-layer distributed attack detection model (CLDAD) is proposed to enhance security solutions for IoT environments. CLDAD presents a general detection method of DDoS in sensing layer, network layer, and application layer. CLDAD is based on big data analytics techniques, which enable the detection process to be performed in a distributed way, so the model can detect DDoS attacks in any layer on-the-fly and the model support the scalability of the IoT environment. CLDAD is tested based on three datasets, namely artificial jamming attack dataset, BoT-IoT dataset, and BoT-IoT-based HTTP. The results showed that the proposed model is efficient in detecting attacks in the three layers of the IoT. Copyright © 2022, IGI Global.

关键词： Big data

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AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

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Digital Communications and Networks 2024年第2期10卷 292-303页

作者： Yuteng Xiao Kaijian Xia Hongsheng Yin Yu-Dong Zhang Zhenjiang Qian Zhaoyang Liu Yuehan Liang Xiaodan Li School of Information and Control Engineering China University of Mining and TechnologyXuzhou221116China Department of Informatics University of LeicesterLeicesterLE17RHUK The Affiliated Changshu Hospital of Soochow University Changshu215500China School of Computer Science and Engineering Changshu Institute of TechnologySuzhou215500China

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other ***,it is important to construct a digital twin ***,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted *** this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)***,we fuse the spatial-temporal graph based on the interrelationship of spatial ***,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding ***,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)*** module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like *** dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted *** on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

关键词： Adaptive adjacency matrix Digital twin Graph convolutional network Multivariate time series prediction Spatial-temporal graph

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Blockchain-based knowledge-aware semantic communications for remote driving image transmission

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Digital Communications and Networks 2025年第2期11卷 317-325页

作者： Yangfei Lin Tutomu Murase Yusheng Ji Wugedele Bao Lei Zhong Jie Li Department of Computer and Network Engineering University of Electro-CommunicationsChofuTokyo 182-8585Japan Information Technology Center Nagoya UniversityNagoya 464-8601Japan National Institute of Informatics(NII) Tokyo 101-8430Japan Hohhot Minzu College Hohhot 010051China Toyota Motor Corporation Tokyo 112-8701Japan Department of Computer Science and Engineering Shanghai Jiaotong UniversityShanghai 200240China

Remote driving,an emergent technology enabling remote operations of vehicles,presents a significant challenge in transmitting large volumes of image data to a central *** requirement outpaces the capacity of traditional communication *** tackle this,we propose a novel framework using semantic communications,through a region of interest semantic segmentation method,to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise *** solve the knowledge base inconsistencies inherent in semantic communications,we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge *** system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient ***,the implementation of blockchain sharding handles differentiated knowledge bases for various tasks,thus boosting overall blockchain *** results show a great reduction in latency by sharding and an increase in model accuracy,confirming our framework's effectiveness.

关键词： Semantic communication Remote driving Semantic segmentation Blockchain Knowledge base management

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Selected Combinatorial Problems Through the Prism of Random Intersection Graphs Models 1

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13th International Symposium on Algorithms and Complexity, CIAC 2023

作者： Spirakis, Paul G. Nikoletseas, Sotiris Raptopoulos, Christoforos Department of Computer Science University of Liverpool Liverpool United Kingdom Computer Technology Institute Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece

ISBN: (数字)9783031304484

ISBN: (纸本)9783031304477

We discuss a simple, yet general family of models, namely Random Intersection Graphs (RIGs), initially introduced by Karoński et al. [4] and Singer-Cohen [10]. In such models there is a universe of labels and each one of n vertices selects a random subset of. Two vertices are connected if and only if their corresponding subsets of labels intersect. A formal definition is given below: © 2023, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph theory

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