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Secure Consumer-Centric Demand Response Management in Resilient Smart Grid as Industry 5.0 Application with Blockchain-Based Authentication

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1370-1379页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Illinois Institute of Technology Information Technology and Management ChicagoIL60616 United States

The development of the industrial Internet of Things and smart grid networks has emphasized the importance of secure smart grid communication for the future of electric power transmission. However, the current deployment of smart meters and wireless communication methods presents several security concerns that must be addressed to ensure sustainable and resilient smart grid networks for Industry 5.0, with a focus on consumer-centric technologies. In this context, we propose a privacy-focused authentication solution for secure consumer-centric demand response management in smart grid networks, incorporating consumer electronics. Our proposed protocol utilizes blockchain-based authentication to enhance resistance against potential attacks and ensure the integrity of demand response data. Additionally, we have incorporated a PUF to prevent physical attacks. We have conducted informal and formal security analyses to verify the effectiveness of our proposed protocol against various threats and to confirm the session key security. Our comparison study has demonstrated that our protocol provides superior security and added functionalities, with lower computation and communication costs compared to similar protocols. Furthermore, we have measured the computational time for implementing the blockchain component of our protocol while considering the impact of varying numbers of block additions and transactions per block. © 1975-2011 IEEE.

关键词： Authentication

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Proactive video caching based on federated learning and implicit feedback in mobile edge computing

Proactive video caching based on federated learning and impl...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Qian, Zhen Li, Guanghui Qi, Tao Dai, Chenglong Hu, Jia Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China University of Exeter Department of Computer Science United Kingdom

ISBN: (纸本)9798350351255

The rapid development of 5G and the widespread use of smart terminal devices bring explosive video traffic growth. Edge caching technology in mobile edge computing systems stores popular content of most interest to users in advance in edge servers closer to mobile users to alleviate the pressure of traffic congestion and excessive access latency caused by centralized storage. However, how to obtain popular videos while protect user data privacy with only implicit user feedback data, such as liking, viewing, favoriting, etc., is the key challenge. To tackle these challenges, we proposed a proactive video caching scheme based on federated learning and implicit feedback (FIPC). First, a mobile edge-cloud system model contained a three-tier network architecture is developed. Then, we introduced the federated process for training the denoised auto-encoder model and video caching in detail. Finally, experimental results in Movielens dataset show that, without user data leakage, the proposed FIPC scheme outperforms the baseline caching algorithm using user feedback data. © 2024 IEEE.

关键词： Mobile edge computing

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Text Localization and Enhancement of Mobile Camera based Complex Natural Bilingual Text Scene Images 2

Text Localization and Enhancement of Mobile Camera based Com...

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2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Hangarage, Venkata Mukarambi, Gururaj Department of Computer Science School of Computer Science Central University of Karnataka Kadaganchi Aland Road Karnataka Kalaburagi585367 India

In this paper, we proposed a text localization and enhancement of the Mobile camera based complex natural scene images using fixed coordinates for removal of unwanted non text regions and making it localized. The morphological erosion technique is applied for the smoothness of foreground text regions. Here, the experimental working nature of the proposed method is first localized color input image is converted into a gray level, then a median filter is applied for the removal of noises. After that, the input image is further converted into binary image. In addition to this, morphological dilation and erosion operations also applied for connecting the broken pixels. Further, the connected component segmentation technique is applied to extract the foreground text regions from the binary input image. The real time input samples are collected by using mobile camera from the various categories of natural scenes of monolingual/bilingual text such as Stone, Wall, Poster and Iron-based. A total of 140 real time samples of natural scene text images are considered for the experimental setup. The experimental results shown that 132 real time sample images are localized and then obtained the accuracy of 94.24%. Further, bilingual text regions are enhanced using hybrid approach and obtain the segmentation accuracy of 83.17%. The novelty of the paper is working on the real time data set and it contains a mixture of printed and handwritten natural scene text images. © 2024 Elsevier B.V.. All rights reserved.

关键词： Median filters

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A new hybrid MobileNet-MLP model for secured data communication using signature verification and deep learning

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Iran Journal of computer science 2024年第1期8卷 233-252页

作者： Pandiyaraju, V. Ganapathy, Sannasi Thangaramya, K. Nahar, R. Raunak Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department Computer Science and Engineering Education National Institute of Technical Teachers Training and Research (NITTTR) Bhopal India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Signature verification plays a critical role in various industries, including finance and document authentication. However, traditional verification techniques have limitations, such as a lack of robustness and an inability to effectively capture intricate patterns and relationships within the signature images. This paper introduces an innovative approach that addresses the drawbacks by integrating conventional machine learning methods with deep learning techniques. For this purpose, we design a new hybrid MobileNet model using deep learning to extract features from the signature images. Here, the Multilayer Perceptron (MLP) classifier is also used to carry out the binary classification over the extracted features. The experimental results of the proposed model demonstrate that the amalgamated technique surpasses the conventional methods and underscores the possibilities of merging traditional machine learning and deep learning in signature verification. Moreover, the proposed model overcomes the limitations of the traditional signature verification techniques by leveraging the strengths of both deep learning and traditional machine learning. Finally, it obtains a more robust and efficient solution and proves it by providing high security to the data in terms of accuracy. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural network Feature extraction MobileNet Multilayer perceptron Signature verification

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Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

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computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan Malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

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Utilizing Social Network Analysis to Model Social Media Post Popularity with Bayesian Belief Networks

Utilizing Social Network Analysis to Model Social Media Post...

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2024 International Conference on Big data Analytics in Bioinformatics, DABCon 2024

作者： Srivastav, Manoj Kumar Gupta, Somsubhra Som, Subhranil School of Computer Science Swami Vivekananda University Barrackpore India Bhairab Ganguly College Department of Computer Science Kolkata India

ISBN: (纸本)9798350378047

Social Network Analysis (SNA) explores social structures, online interactions, and relationships in social graphs. Using vertices (members) and edges (relationships), SNA represents social networks where members connect through friendships. Platforms like Facebook and Instagram facilitate connection, communication, and information sharing. This paper proposes a novel approach using Bayesian Belief Networks (BBNs) to model social media post popularity. BBNs capture the complex factors influencing popularity, considering followers, engagement, and total likes. This research investigates BBNs' potential to model relationships between user characteristics (followers) and post engagement (engagement rate) in influencing total likes received. The paper proposes a BBN framework with three nodes representing these factors. The methodology defines thresholds to categorize posts into 'high' and 'low' for followers and engagement rate. Conditional probability tables are then constructed based on these categories, allowing calculation of the probability of a post receiving high likes given its follower base and engagement level. This study demonstrates the feasibility of using BBNs to model social media post popularity. The results reveal that both follower count and engagement rate play a significant role, with a high engagement rate having a stronger influence on total likes, even for posts with a lower follower base. © 2024 IEEE.

关键词： BBNs Engagement Rate Follower Count Post Popularity Social Media Social Network Analysis (SNA)

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Caching Strategies in NDN Based Wireless Ad Hoc Network:A Survey

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computers, Materials & Continua 2024年第7期80卷 61-103页

作者： Ahmed Khalid Rana Asif Rehman Byung-Seo Kim Department of Computer Science FAST School of ComputingNational University of Computer and Emerging SciencesLahore54000Pakistan Department of Software and Communications Engineering Hongik UniversitySejong City30016Republic of Korea

Wireless Ad Hoc Networks consist of devices that are wirelessly *** Ad Hoc Networks(MANETs),Internet of Things(IoT),and Vehicular Ad Hoc Networks(VANETs)are the main domains of wireless ad hoc *** is used in wireless ad hoc *** is based on Transmission Control Protocol(TCP)/Internet Protocol(IP)network where clients and servers interact with each other with the help of IP in a pre-defined *** fetches data from a fixed *** redundancy,mobility,and location dependency are the main issues of the IP network *** these factors result in poor performance of wireless ad hoc *** main disadvantage of IP is that,it does not provide in-network ***,there is a need to move towards a new network that overcomes these *** data Network(NDN)is a network that overcomes these *** is a project of Information-centric Network(ICN).NDN provides in-network caching which helps in fast response to user *** NDN in wireless ad hoc network provides many benefits such as caching,mobility,scalability,security,and *** considering the certainty,in this survey paper,we present a comprehensive survey on Caching Strategies in NDN-based Wireless *** cachingmechanism-based results are also *** the last,we also shed light on the challenges and future directions of this promising field to provide a clear understanding of what caching-related problems exist in NDN-based wireless ad hoc networks.

关键词： Content centric network Internet of Things mobile ad hoc network named data network vehicular ad hoc network

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Integrating Low-order and High-order Functional Connectivity for Meta-stable State Transition based Brain Disorder Identification

Integrating Low-order and High-order Functional Connectivity...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pei, Shengbing Wang, Yan He, Fan Lv, Zhao Zhang, Chao Guan, Jihong Anhui University School of Computer Science and Technology Anhui China Tongji University Department of Computer Science and Technology Shanghai China

ISBN: (纸本)9798350386226

Dynamic functional connectivity network (FCN) can effectively mine meta-stable state transition within the period of data acquisition time, which is related to neurological diseases. However, conventional FCN directly describes the correlation between two brain regions in a meta-stable state, it is low-order FCN. In fact, the connection between two brain regions within the meta-stable state also has a changing pattern, which can reveal the functional consistency between two connections over time, we denote the changing pattern between two regions as high-order FCN. Here, we propose an end-to-end method that integrates low-order and high-order dynamic FCNs for better brain disorder identification. First, a sliding window operation is adopted to capture meta-stable states. Then, a matrix variate normal distribution based approach is employed to construct the low-order and high-order FCNs for each meta-stable state. Finally, a two-stage Transformer is designed to extract meta-stable state transition feature for classification. Experimental results on the ADHD-200 and ABIDE datasets indicate that: 1) our proposed method integrate multi-level connectivity information of dynamic brain functions, thereby effectively improving the identification of brain disorders;2) the proposed two-stage Transformer is more effective in feature extraction than the well-known CNN-LSTM architecture;3) high-order FCN help locate biomarkers that low-order FCN cannot be determine, including brain regions as well as functional connections between brain regions, which contribute significantly to the diagnosis of brain disorders. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Thread User Sentiment Analysis Based on Text using LGBM, SVM, and Logistic Regression Algorithm 6

Thread User Sentiment Analysis Based on Text using LGBM, SVM...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Chao, Nicholas Vincent Jo, Rafael Nabiilah, Ghinaa Zain Moniagaa, Jurike V. School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Social media is growing rapidly as a medium for online communication. Through social media, users can share opinions, stories or other things. The messages shared tend to have various meanings, such as positive, negative or neutral. The text shared sometimes has a negative meaning that can disturb other users. So based on this, this study will conduct a Text-Based Thread User Sentiment Analysis using Light Gradient Boosting Machine (LGBM), Support Vector Machine (SVM), and Logistic Regression. Our comparative investigation using a large dataset of 37,000 annotated text samples from the Thread application shows that SVM outperforms other methods with an accuracy of 89.24%. The accuracy rates for LGBM and logistic regression are 83.89% and 86.91%, respectively. One of the evaluation metrics is precision, which consistently shows superior SVM performance. These findings indicate that Support Vector Machine (SVM) is the most efficient algorithm for sentiment analysis in textual data, indicating its reliability and suitability. © 2024 IEEE.

关键词： Logistic regression

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