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Proposed 3D chaos-based medical image cryptosystem for secure cloud-IoMT eHealth communication services

Journal of Ambient Intelligence and Humanized computing 2024年第1期15卷 1-28页

作者： El-Shafai, Walid Khallaf, Fatma El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electrical Engineering Faculty of Engineering Ahram Canadian University Giza 6th October City Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh84428 Saudi Arabia

Medical image encryption is a mandatory process in various healthcare, Internet of Medical Things (IoMT) and cloud services. This paper provides a robust cryptosystem based on a 3D chaotic map for the medical image encryption in secure IoMT and cloud services. The proposed encryption method depends on a 3D chaotic map. With this map, a nonlinear ciphering process is implemented for pixel value diffusion and position permutation. Five operations are conducted in the suggested cryptosystem on the medical images to be transmitted in order to attain a high security level. These operations are 3D chaos generation, chaos histogram equalization, row rotation, column rotation, and XOR operation. To validate the suggested cryptosystem, different medical images with different characteristics are tested. Moreover, the suggested cryptosystem and more recent state-of-the-art security systems are discussed and analyzed in a comparative study. Different assessment parameters are adopted to assess the suggested cryptosystem, which reveals high security performance. The simulation outcomes show that the suggested cryptosystem is trustworthy. Furthermore, it offers high robustness levels and recommended security levels for utilization in healthcare, IoMT and cloud service applications. It is observed that the amount of average entropy obtained by the proposed 3D chaos cryptosystem is 7.95 which is very close to its best value of 8, and the average NPCR value is 99.62% which is also much close to its supreme value of 99.60%. In addition, all other results scored for the test evaluation parameters on the suggested cryptosystem are superior to those of the traditional security systems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Cryptography

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Deep neural network-enabled battery open-circuit voltage estimation based on partial charging data

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Journal of Energy Chemistry 2024年第3期90卷 120-132,I0005页

作者： Ziyou Zhou Yonggang Liu Chengming Zhang Weixiang Shen Rui Xiong State Key Laboratory of Mechanical Transmission for Advanced Equipment&College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400000China Department of Vehicle Engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Department of Electrical Engineering Harbin Institute of TechnologyHarbin 150000HeilongjiangChina School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia

Battery management systems(BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion *** main function of the BMSs is to estimate battery states and diagnose battery health using battery open-circuit voltage(OCV).However,acquiring the complete OCV data online can be a challenging endeavor due to the time-consuming measurement process or the need for specific operating conditions required by OCV estimation *** addressing these concerns,this study introduces a deep neural network-combined framework for accurate and robust OCV estimation,utilizing partial daily charging *** incorporate a generative deep learning model to extract aging-related features from data and generate high-fidelity OCV *** analysis is employed to identify the optimal partial charging data,optimizing the OCV estimation precision while preserving exceptional *** validation results,using data from nickel-cobalt-magnesium(NCM) batteries,illustrate the accurate estimation of the complete OCV-capacity curve,with an average root mean square errors(RMSE) of less than 3 *** this level of precision for OCV estimation requires only around 50 s collection of partial charging *** validations on diverse battery types operating under various conditions confirm the effectiveness of our proposed *** cases of precise health diagnosis based on OCV highlight the significance of conducting online OCV *** method provides a flexible approach to achieve complete OCV estimation and holds promise for generalization to other tasks in BMSs.

关键词： Lithium-ion battery Open-circuit voltage Health diagnosis Deep learning

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AI-Based Automatic System for Assessing Upper-Limb Spasticity of Patients With Stroke Through Voluntary Movement

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IEEE Journal of Biomedical and Health Informatics 2024年第2期28卷 742-752页

作者： Lee, I-Jung Hu, Yu Hen Hsiao, Pei-Chi Yang, Shu-Yu Lin, Hsin-Te Chen, Yu-Chung Lin, Bor-Shing National Taipei University College of Electrical Engineering and Computer Science New Taipei City237303 Taiwan University of Wisconsin-Madison Department of Electrical and Computer Engineering MadisonWI53706 United States Chi-Mei Medical Center Department of Physical Medicine and Rehabilitation Tainan71004 Taiwan Chi Mei Hospital Department of Physical Medicine and Rehabilitation Tainan73657 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan

Spasticity is a common complication for patients with stroke, but only few studies investigate the relation between spasticity and voluntary movement. This study proposed a novel automatic system for assessing the severity of spasticity (SS) of four upper-limb joints, including the elbow, wrist, thumb, and fingers, through voluntary movements. A wearable system which combined 19 inertial measurement units and a pressure ball was proposed to collect the kinematic and force information when the participants perform four tasks, namely cone stacking (CS), fast flexion and extension (FFE), slow ball squeezing (SBS), and fast ball squeezing (FBS). Several time and frequency domain features were extracted from the collected data, and two feature selection approaches based on recursive feature elimination were adopted to select the most influential features. The selected features were input into five machine learning techniques for assessing the SS for each joint. The results indicated that using CS task to assess the SS of elbow and fingers and using FBS task to assess the SS of thumb and wrist can reach the highest weighted-average F1-score. Furthermore, the study also concluded that FBS is the optimal task for assessing all the four upper-limb joints. The overall result shown that the proposed automatic system can assess four upper-limb joints through voluntary movements accurately, which is a breakthrough of finding the relation between spasticity and voluntary movement. © 2013 IEEE.

关键词： Learning systems

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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Artificial Neural Network Machine Learning Model for Malicious Action Detection and Prevention of MANET Assets 1

Artificial Neural Network Machine Learning Model for Malicio...

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1st International Conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Mani, G. Prabakaran, R. University College of Engineering Department of Computer Science and Engineering Tamil Nadu Kancheepuram India Department of Electrical and Electronics Engineering Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9798350361780

The main objective of this paper is to create a novel architecture of a machine learning model to identify and detect the malicious attacks occur in MANET statically and dynamically. Earlier research works have proposed various secured routing protocols for malicious activity detection whereas they are all detecting the attacker after the malicious action created in the network. However, the problem is, the MANET needs a novel method to identify and detect the malicious action to prevent the network, data, and other properties of the network. Hence this paper proposed an Artificial Neural Network model for learning, diagnosing, and detecting abnormal activities present in the MANET, and eliminate the corresponding actions and the node from the network function. Three different dataset is created in the network before and after deploying the nodes in the network, routing table, and intermediate node information input to ANN for analyzation. The simulation is created in NS-2 software and the results are verified based on the network performance with respect to remaining energy, throughput, packet delivery ratio, and packet loss. The performance of the ANN is evaluated by comparing its output with the other earlier methods and prove its efficiency. © 2024 IEEE.

关键词： Network security

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Detection of myocardial infarction diseases based on entropy, optimized stockwell time-frequency transform and extreme learning machine

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International Journal of Information Technology (Singapore) 2025年 1-7页

作者： Besfat, Henok Mezemr Galmecha, Demissie Jobir Singh, Ram Sewak Mishra, Satyasis Electronics and Communication Engineering Department College of Electrical Engineering and Computing Adama Science and Technology University P.Box. 1888 Adama Ethiopia Department of ECE Centurion University of Technology and Management Gajapati India

Early and accurate detection of myocardial infarction (MI) is pivotal in enhancing cardiac function, as it can result in a heart attack. Detecting MI arrhythmias from electrocardiogram (ECG) signals presents challenges in accurately extracting patterns, such as Q, R, S and T wave segments. This paper proposes an efficient automated MI detection based on optimized Stockwell transform (OptST), Renyi entropy, dispersion entropy, and extreme learning machine (ELM) algorithm. The OptST is employed to decompose the ECG signal to four frequency band. The renyi and dispersion entropy features are then extracted in each frequency band for training and validation of ELM model to classify the five MI diseases. To identify clinically significant features, a Wilcoxon rank sum test is conducted, and to test the robustness of the proposed method 5-fold cross-validation has been applied. The simulation results have demonstrated that the proposed model has achieved a highest accuracy of 96.86%, specificity of 98.04%, recall of 92.16%, and F-score of 92.18% for sine activation function at hidden number of 300. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Dispersion entropy ELM Myocardial infarction Renyi entropy Stockwell transform

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A Comparative Analysis of Swarm Algorithms for Enhancing Communication in Drone Networks 15

A Comparative Analysis of Swarm Algorithms for Enhancing Com...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Chowdary, Chava Sriya Ruthwik, Dovala Sai, Kancharla Nitish Rishik, Suthapalli Sampath, Nalini Anand, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

The introduction of drone technology has transformed a variety of businesses, from surveillance and monitoring to delivery services. However, effective communication among drones is critical for guaranteeing smooth operations, particularly in tough areas. This research study provides a comparative analysis of swarm intelligence algorithms for improving communication in drone networks. The study focuses on comparing the performance of various swarm algorithms in terms of communication count, with impediments represented by forest density ranging from 0 to 50 percent. Performance indicators, notably communication counts, are used to evaluate each algorithm’s success in facilitating drone communication. The major performance statistic is communication count, which is defined as the number of drone interactions. The study tries to determine the most effective swarm intelligence algorithm for drone communication in forested situations by examining how each method’s communication count changes as forest density increases. The discoveries will help to construct stable and communication-efficient drone networks for a variety of applications including forestry, search and rescue, and environmental monitoring. Understanding the impact of forest density on communication and the efficiency of various swarm algorithms provides a way to optimize drone network communication strategies for real-world circumstances. ©2024 IEEE.

关键词： Drones

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Social Network Hashtag Analysis for the 75th Year of India’s Independence 1

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8th International Conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2023

作者： Veeramanohar, A. Nishanth, A.J. Vishvajit, S. Ramya, G.R. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9789819976331

ISBN: (纸本)9789819976324

The social media platform Twitter is all about what’s happening in the world. Twitter has been the core of spread of information when compared with other social media platforms. Anything and everything break open on twitter first before it reaches the other social media platforms, it is unlike any other and everything can be found in one place (Yum in Soc Sci Q 101(4):1642–1647, 2020;Mishra et al. in 2016 International conference on information technology (InCITe)—the next generation IT summit on the theme—internet of things: connect your worlds, pp 148–153, 2016). It is observed that people turn to twitter users first when they want to know something. This study explores how the influential twitter users play a crucial role in social networks. This study aimed to analyze the hashtag of the 75th year of Indian Independence tweets using social network analysis (SNA). SNA techniques are performed to measure certain centrality measures and plot their graphs to find the most influential user in the network. Sentiment analysis is performed on the dataset to determine the positive and negative emotions surrounding the 75th year of Indian Independence. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Edge-Driven Industrial computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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Acoustic Emission Recognition Based on a Three-Streams Neural Network with Attention

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computer Systems Science & engineering 2023年第9期46卷 2963-2974页

作者： Kang Xiaofeng Hu Kun Ran Li College of Information and Engineering Xuzhou University of TechnologyXuzhouJiangsu221000China College of Electrical and Power Engineering China University of Mining and TechnologyXuzhouJiangsu221116China Department of Electrical Electronic and Computer EngineeringUniversity of Western AustraliaPerthAustralia

Acoustic emission(AE)is a nondestructive real-time monitoring technology,which has been proven to be a valid way of monitoring dynamic damage to *** classification and recognition methods of the AE signals of the rotor are mostly focused on machine *** that the huge success of deep learning technologies,where the Recurrent Neural Network(RNN)has been widely applied to sequential classification tasks and Convolutional Neural Network(CNN)has been widely applied to image recognition tasks.A novel three-streams neural network(TSANN)model is proposed in this paper to deal with fault detection *** on residual connection and attention mechanism,each stream of the model is able to learn the most informative representation from Mel Frequency Cepstrum Coefficient(MFCC),Tempogram,and short-time Fourier transform(STFT)spectral *** results show that,in comparison with traditional classification methods and single-stream CNN networks,TSANN achieves the best overall performance and the classification error rate is reduced by up to 50%,which demonstrates the availability of the model proposed.

关键词： Convolutional neural network attention mechanism acoustic emission fault detection

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