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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Lian, Zhuotao Wang, Weiyu Su, Chunhua Sakurai, Kouichi Kyushu University Faculty of Information Science and Electrical Engineering Fukuoka Japan University of Aizu School of Computer Science and Engineering Aizuwakamatsu Japan

ISBN: (纸本)9798350351637

Federated learning has rapidly advanced as a privacy-preserving, distributed machine learning methodology. Protecting the intellectual property rights of federated models, however, poses significant challenges. Existing backdoor-based watermarking techniques suffer from complexity, inefficiency, and implementation challenges. In this paper, we present AggreMark, a novel watermarking approach that incorporates parallel server-side training and watermark embedding during the weighted averaging phase of federated learning. This method does not alter the original model structure or parameters related to the original learning task and adds no additional time overhead, ensuring its practicality for real-world applications. AggreMark is easy to implement and maintain, and it robustly preserves the model's high performance. We validate the method's effectiveness and robustness against pruning-based watermark removal attacks through experiments on CIFAR-10. © 2024 IEEE.

关键词： Federated learning

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Hybrid millimeter wave heterogeneous networks with spatially correlated user equipment

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Digital Communications and Networks 2024年第4期10卷 904-917页

作者： Arif Ullah Ziaul Haq Abbas Ghulam Abbas Fazal Muhammad Jae-Mo Kang Department of Computer Engineering College of IT ConvergenceChosun UniversityGwangju61452South Korea Faculty of Electrical Engineering GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Artificial Intelligence Kyungpook National UniversityDaegu41566South Korea

In this paper,we analyze a hybrid Heterogeneous Cellular Network(HCNet)framework by deploying millimeter Wave(mmWave)small cells with coexisting traditional sub-6GHz macro cells to achieve improved coverage and high data *** consider randomly-deployed macro base stations throughout the network whereas mmWave Small Base Stations(SBSs)are deployed in the areas with high User Equipment(UE)*** user centric deployment of mmWave SBSs inevitably incurs correlation between UE and *** a realistic scenario where the UEs are distributed according to Poisson cluster process and directional beamforming with line-of-sight and non-line-of-sight transmissions is adopted for mmWave *** using tools from stochastic geometry,we develop an analytical framework to analyze various performance metrics in the downlink hybrid HCNets under biased received power *** UE clustering we considered Thomas cluster process and derive expressions for the association probability,coverage probability,area spectral efficiency,and energy *** also provide Monte Carlo simulation results to validate the accuracy of the derived ***,we analyze the impact of mmWave operating frequency,antenna gain,small cell biasing,and BSs density to get useful engineering insights into the performance of hybrid mmWave *** results show that network performance is significantly improved by deploying millimeter wave SBS instead of microwave BS in hot spots.

关键词： Downlink cell association Heterogeneous cellular networks Integrated sub-6GHz and mmWave networks Millimeter wave communications Poisson cluster process

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Topology-adaptive Bayesian optimization for deep ring echo state networks in speech emotion recognition

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Neural Computing and Applications 2025年第1期37卷 399-416页

作者： Soltani, Rebh Benmohamed, Emna Ltifi, Hela University of Sfax BP 1173 Sfax3038 Tunisia Department of computer science Onaizah college of Engineering and Information Technology Oniazah colleges Saudi Arabia Computer Science and Mathematics Department Faculty of Sciences and Techniques of Sidi Bouzid University of Kairouan Kairouan Tunisia

Speech emotion recognition is a difficult task that is gaining attention in a variety of domains, including psychology, human–computer interaction, and speech processing. To recognize speech emotions, machine learning techniques such as convolutional neural networks and long short-term memory networks have been successfully used. However, these models often fail to capture the long-term temporal dynamics of emotional speech. In this work, data augmentation was performed on all datasets. The generated result was used to extract the discriminant features. Then we apply a deep echo state network with a ring topology. The Bayesian optimization (BO) algorithm was applied to the spectral radius of the reservoir matrix. Our approach reveals term-weighted and unweighted accuracies of (75.45%, 74.79%), (86.89%, 86.14%), (98.99%, 97.62%), and (62%, 61.24%) on EMO-DB, SAVEE, RAVDESS, and TESS datasets. The findings demonstrate that by using BO, we are able to determine an optimal spectral radius. Therefore, this approach balances the minimal complexity of reservoir design and high emotion recognition accuracy. Notably, we demonstrate that a simple, deterministically constructed cycle reservoir within DRESN is comparable to other methodologies. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Bayesian networks

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Research on Image Encryption Algorithm Based on Four-Dimensional Hyperchaotic System and Mandelbrot Set

Research on Image Encryption Algorithm Based on Four-Dimensi...

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作者： Sun, Yanling Jiang, Tianjiao Liu, Baoxing Li, Yingqi Xiao, Mingkun Su, Qun Faculty of Science Shenyang Jianzhu University Shenyang China School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China

In view of the problems of small key space and poor key sensitivity in classical image encryption algorithm, the characteristics of chaotic system and M set are analyzed, and a new image encryption algorithm is proposed in this paper. The parameter values generated by SHA-256 algorithm are substituted into the hyper-chaotic Lorenz system to form the initial values of the input plaintext image. Then pseudo-random sequences are generated by hyper-chaotic Lorenz system, and the sequences are scrambled and diffused. The image matrix is generated according to Mandelbrot set, and the image is scrambled again with the sequence generated by different iteration times, and the encrypted image is finally obtained by XOR operation. The experimental results show that the proposed image encryption algorithm has good performance in various evaluation criteria, the key space value of the algorithm is large enough, high key sensitivity and the correlation of adjacent pixels is significantly reduced, and the information entropy is close to the ideal value. Thus this paper proposes image encryption algorithm has good security, can satisfy the requirements of the digital image encryption technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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An Improved Farmland Fertility Algorithm with Hyper-Heuristic Approach for Solving Travelling Salesman Problem

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computer Modeling in engineering & sciences 2023年第6期135卷 1981-2006页

作者： Farhad Soleimanian Gharehchopogh Benyamin Abdollahzadeh Bahman Arasteh Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmiaIran Department of Software Engineering Faculty of Engineering and Natural ScienceIstinye UniversityIstanbulTurkey

Travelling Salesman Problem(TSP)is a discrete hybrid optimization problem considered *** aims to discover the shortest Hamilton route that visits each city precisely once and then returns to the starting point,making it the shortest route *** paper employed a Farmland Fertility Algorithm(FFA)inspired by agricultural land fertility and a hyper-heuristic technique based on the Modified Choice Function(MCF).The neighborhood search operator can use this strategy to automatically select the best heuristic method formaking the best ***-Kernighan(LK)local search has been incorporated to increase the efficiency and performance of this suggested approach.71 TSPLIB datasets have been compared with different algorithms to prove the proposed algorithm’s performance and *** results indicated that the proposed algorithm outperforms comparable methods of average mean computation time,average percentage deviation(PDav),and tour length.

关键词： Travelling salesman problem optimization farmland fertility optimization algorithm Lin-Kernighan

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Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

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computers, Materials & Continua 2024年第7期80卷 139-161页

作者： Roseline Oluwaseun Ogundokun Joseph Bamidele Awotunde Hakeem Babalola Akande Cheng-Chi Lee Agbotiname Lucky Imoize Department of Multimedia Engineering Kaunas University of TechnologyKaunas44249Lithuania Department of Computer Science Landmark UniversityOmu Aran251103Nigeria Department of Computer Science Faculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria Department of Telecommunication Science University of IlorinIlorin230003Nigeria Department of Library and Information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina Department of Computer Science and Information Engineering Fintech and Blockchain Research CenterAsia UniversityTaichung City41354TaiwanChina Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr UniversityBochum44801Germany

Mobile technology is developing *** phone technologies have been integrated into the healthcare industry to help medical ***,computer vision models focus on image detection and classification ***2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to *** leads to increased *** biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational ***,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is *** pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory *** proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and *** contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable *** model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class *** the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is *** testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,a

关键词： Retinal images ocular disorder deep transfer learning disease identification mobile device

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Using Supervised Machine Learning Model For Predicting Fake Deliveries 6

Using Supervised Machine Learning Model For Predicting Fake ...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Sadek, Mohamed A. Fouad, Khaled M. Faculty of Information Technology and Computer Science Nile University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer Science and Engineering New Mansoura University Egypt

ISBN: (纸本)9798350373875

The home delivery service is a crucial part of the delivery process. Still, it has the highest financial and trustworthiness costs because it's the only part that directly touches the customer. If it works, the customer will be happy, and the company will make money, but if it doesn't, the customer will be skeptical, and the product will not be trusted. One big issue with the delivery process that this study will try to address is the prevalence of fraudulent or inaccurate deliveries. Making the most money possible from each journey is the primary goal of most riders. The delivery management system allows riders to falsely record orders as completed, allowing them to receive many orders simultaneously. Because the consumer usually gets a message or notification saying their item has been delivered when it hasn't, this weakens their faith in the organization and messes up the delivery process. Return to Origin (RTO) rates rose, and consumer confidence in the brand fell due to the fraudulent shipments. The suggested approach is based on ML models that are supervised. © 2024 IEEE.

关键词： Sales

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From 5G to 6G: The Role of AI, Machine Learning, and Deep Learning in Wireless Systems 4

From 5G to 6G: The Role of AI, Machine Learning, and Deep Le...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gote, Pradnyawant M. Kumar, Praveen Verma, Prateek Yesankar, Prajyot Pawar, Adesh Saratkar, Saniya Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

The swift progression of wireless communication technologies-specifically from 5G to 6G is an approach that could be the most significant revolutionary leap towards changing connectivity and data transmission forever. Additionally, algorithms of machine learning (ML) and deep learning (DL) in 5G and 6G can offer promising advancements toward predictive maintenance, dynamic spectrum management, and enhanced security. The review of the potentially transformative nature of these technologies underlines the need for future directions of research efforts called for in circumventing challenges towards making full capability exploitation of 6G possible. © 2025 IEEE.

关键词： Adversarial machine learning

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Machine Learning Enabled Novel Real-Time IoT Targeted DoS/DDoS Cyber Attack Detection System

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computers, Materials & Continua 2024年第9期80卷 3655-3683页

作者： Abdullah Alabdulatif Navod Neranjan Thilakarathne Mohamed Aashiq Department of Cybersecurity College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of ICT Faculty of TechnologyUniversity of ColomboColombo00700Sri Lanka Department of Computer Science and Engineering Faculty of EngineeringSouth Eastern University of Sri LankaOluvil32360Sri Lanka

The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber *** the myriad of potential attacks,Denial of Service(DoS)attacks and Distributed Denial of Service(DDoS)attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of *** IoT devices often lack the inherent security measures found in more mature computing platforms,the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT *** this study,we investigate the effectiveness of three machine learning(ML)algorithms:extreme gradient boosting(XGB),multilayer perceptron(MLP)and random forest(RF),for the detection of IoTtargeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing stateof-the-art,and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS *** CICIoT2023 dataset was derived from the latest real-world IoT traffic,incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering,the data was fed into our models for both training and validation,where findings suggest that while all threemodels exhibit commendable accuracy in detectingDoS/DDoS attacks,the use of particle swarmoptimization(PSO)for feature selection has made great improvements in the performance(accuracy,precsion recall and F1-score of 99.93%for XGB)of the ML models and their execution time(491.023 sceonds for XGB)compared to recursive feature elimination(RFE)and randomforest feature importance(RFI)*** proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in *** inv

关键词： Machine learning Internet of Things(IoT),DoS,DDoS cybersecurity intrusion prevention network security,feature optimization,sustainability

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Research on Human Activity Recognition Based on Random Forest Classifier

Research on Human Activity Recognition Based on Random Fores...

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2023 IEEE International Conference on Control, Electronics and computer Technology, ICCECT 2023

作者： Feng, Zhiyong Shi, Yike Zhou, Duomu Mo, Liping Jishou University The School of Computer Science and Engineering Jishou416000 China Jishou University Faculty of Computer Science and Technology Computer Science and Engineering Jishou416000 China

ISBN: (纸本)9798350399028

The current machine learning algorithms classify human activities with inaccurate accuracy, poor generalization ability of the model, and poor classification effect. Proposing to use Random Forest classifier to classify the samples. The classifier has the advantage of good generalization ability, high accuracy, and the ability to handle a large number of sample data. After comparing with BP neural networks, Naive Bayesian networks, and decision trees, the random forest classifier achieved an accuracy of 98% on the test set, much higher than the 55%, 87%, and 88% of the other algorithms. In addition, we also tested the generalization ability of the model using the K-Folder cross-validation method, which yielded an average accuracy of 95.5%, also much higher than the 49.9%, 85%, and 90% of the other classifiers. The experimental results show that the random forest classifier has significantly improved in accuracy and generalization ability compared with BP neural network by 78% and 91%, respectively. Therefore, the superiority of the random forest classifier in human activity classification is proved. © 2023 IEEE.

关键词： Decision trees

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