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A Web 3.0 Integrated Blockchain Enabled Access System Augmented by Meta-Heuristic Cognitive Learning Framework for Mitigating Threats in IoT Enabled Consumer Electronic Devices

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1201-1210页

作者： Aruna, Etikala Sahayadhas, Arun Kalpana, Ponugoti Khan, Surbhi B. Quasim, Tabrez Almusharrf, Ahlam VELS Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Pallavaram Chennai India AVN Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad501510 India University of Salford School of Science Engineering and Environment SalfordM5 4WT United Kingdom Chandigarh University University Centre for Research and Development Mohali India Chitkara University Centre for Research Impact and Outcome India University of Bisha College of Computing and Information Technology Department of Computer Science and Artificial Intelligence Bisha Saudi Arabia King Khalid Univeersity Saudi Arabia

Consumer Electronic Devices have become an open network model because of the infusion of the Internet of Things (IoT) and other communication technologies such as 5G/6G. Though these devices have provided the high-end sophistication even to common person, but it has proved its darker side by triggering more security breaches and privacy problems. Hence, securing and authenticating these Internet enabled consumer devices has become a probable issue to be solved for safer and secured communication. Therefore, this paper presents a novel fusion of Web 3.0-based Blockchain (WBC) and Deep learning (DL) technique for securing consumer electronic devices in an IoT ecosystem. The proposed framework k(MTD-BCAM) is devised into two components: Multiple-Threat Detection(MTD) and Access Management Mechanism(AMM). In the first component, a DL model is applied for threat detection, whereas WBC is meant for an efficient authentication process. Furthermore, a novel residual fast-gated recurrent neural network is proposed. To reduce the complexity, the komodo Mlipir optimization (KMO) approach is used to tune the hyper-parameters of the network. The comprehensive experimental outcome study of the proposed approach employs NSL-KDD datasets in which the distinct metrics of both DL and Blockchain (BC) are measured and analyzed. Results demonstrated the superior accuracy of the model by achieving 99.78% with less computational time and higher transaction speed. Additionally, the statistical validation and security strength of the model are also analyzed and examined with the varied state-of-art models. © 2025 IEEE. All rights reserved.

关键词： Recurrent neural networks

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Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

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Chinese Physics B 2024年第4期33卷 418-433页

作者：周新卫蒋东华 Jean De Dieu Nkapkop Musheer Ahmad Jules Tagne Fossi Nestor Tsafack 吴建华 Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang 332020China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 511400China Department of Electrical Engineering and Industrial Computing University Institute of TechnologyDoualaCameroon Department of Computer Engineering Jamia Millia IslamiaNew Delhi 110025India Department of Physics Faculty of ScienceUniversity of YaoundeCameroon Electrical Engineering Department and Industrial Computing of ISTAMA University of DoualaDoualaCameroon

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词： cellular neural network memristor hardware circuit compressive sensing privacy data protection

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Robust Control for Uncertain Discrete-Time Linear Systems Using Reinforcement Learning With Discount Factor

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2783-2791页

作者： Ding, Yuntian Yang, Yuxiao Yan, Zhilian Tai, Weipeng Research Institute of Information Technology Anhui University of Technology Ma’anshan243032 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215000 China School of Electrical & Information Engineering Anhui University of Technology Ma’anshan243032 China School of Computer Science and Technology Anhui University of Technology Ma’anshan243032 China

This paper focuses on robust control for uncertain discrete-time linear systems. We transform the robust control issure into an optimal control issure for an auxiliary system. The desired control gains are obtained by solving coupled algebraic Riccati equations. We propose a model-free off-policy reinforcement learning algorithm that incorporates a discount factor to get an approximate solution to the equation, using only measured data and eliminating the need for information about system dynamics. This algorithm requires adding probing noise to control inputs to maintain persistent excitation condition. We show that the probing noise does not introduce bias in the solution of the Bellman equation. The effectiveness of the proposed algorithm is verified through a simulation example, which analyzes the impact of the discount factor and the probing noise. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Riccati equations

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2D Wound Image Segmentation Using Deep Learning Algorithms 2

2D Wound Image Segmentation Using Deep Learning Algorithms

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Sivakumar, Soubraylu Abhiram, V.S.V. Rama, P. Kallakrinda, Vikram Kumar, T. Rajesh Saravanan, P. Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798331541217

The purpose of wound segmentation is to accurately identify and delineate wound areas in images, enabling precise analysis of metrics like size, shape, and spread. This aids healthcare professionals in assessing wound severity and monitoring treatment progress. This research focuses on developing an automated wound image segmentation tool using the Deep learning Algorithms. The objective is to enhance wound assessment accuracy by precisely identifying and segmenting wound areas in digital images. Three models are employed to find the effectiveness in segmenting the wound which includes U- Net, DeepLabV3+ and Mask-CNN. The deep learning models are trained on a massive dataset of wound pictures and masks;they then provide segmented outputs, which are examined for wound characteristics and severity. The U-Net model has obtained better performance in segmenting the wound with an accuracy of 92.3%. This work seeks to incorporate advanced capabilities into a user-friendly interface for practical use in clinical settings. Initial results demonstrate promising segmentation accuracy, with plans for further enhancements in future. © 2024 IEEE.

关键词： Image segmentation

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Determination and Classification of Diabetes Mellitus Applying Ensemble Machine Learning Classifiers 1

Determination and Classification of Diabetes Mellitus Applyi...

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

作者： Patel, Siddharth Hassan, Zayd Iniyan, S. SRM Institute of Science and Technology Department of Computer Science and Engineering Kaatankulathur Tamilnadu Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Kaatankulathur Tamilnadu Chennai India

ISBN: (纸本)9798350361780

This paper addresses the accurate and early prediction of the onset and progression of diabetes, an important aspect in the management of this chronic metabolic disease given a variety of information like the history of the diseased person and his day-to-day routine factors by using state-of-the-art machine learning techniques to obtain predictive dynamic models. The proposed work not only demonstrates the effectiveness of different algorithms but also highlights the importance of feature engineering to refine predictions. This work uses ensemble classifiers and voting classifier algorithms to develop better accuracy for diabetes prediction. Combining the results of multiple base classifiers like KNN, SVM, RFC, and decision tree ensemble methods aims to enhance the predictive ability and chances of improvement in the patient. The treatment adopted is different from patient to patient based on the result of the treatment. In this research, we utilize a combination of ensemble techniques and a voting classifier to construct a comprehensive diabetes prediction model. This model is evaluated using real-world data. Our findings reveal the exceptional effectiveness of ensemble classifiers and the voting classifier, underscoring their potential to achieve precision and the possibility of recovery and probability. These outcomes not only enable early intervention but also offer valuable insights into the care of individual patients. © 2024 IEEE.

关键词： Decision trees

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Adversarial Attacks Using Carlini Wagner

Adversarial Attacks Using Carlini Wagner

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Baddukonda, Tarun Aditya Kanakam, Ronit Kumar Yasotha, B. Srm Institute of Science and Technology Department of Computer Science and Engineering Kaatankulathur Tamilnadu Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kaatankulathur Tamilnadu Chennai India

ISBN: (纸本)9798350382693

Adversarial attacks in the Artificial Intelligence have received extensive coverage in the latest years. Such attacks have affected deep learning models and neural networks due to the weaknesses that are part of the AI systems. This exposure has seen classification errors compromises and the undermining of the model performance. Understand the critical mechanisms of adversarial attacks is critical to developing strong defense mechanisms that would protect AI systems from the malevolent interventions. This abstract provides a summary of adversarial attacks as a prevalent threat to the dependability and resilience of AI technologies thus the need for continuous research and development. © 2024 IEEE.

关键词： Adversarial machine learning

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Machine Learning Based Earlier Identification of Liver Disease Using Ultrasound Images 1st

Machine Learning Based Earlier Identification of Liver Disea...

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1st International Conference on Intelligent computing, Smart Communication, and Network Technologies, ICICSCNT 2023

作者： Saravanakumar, C. Prakash, M. Deepak Kumar, A. Ashokkumar, C. Department of Computer Engineering Government Polytechnic College Purasawalkam Chennai600012 India School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9783031759567

Liver disease begins in the ovaries and is especially hazardous for women. As a result, abnormal cells can potentially spread to other organs. Liver disease is a type of dangerous development that affects the ovaries in females, and it is difficult to detect at an early stage, which is why it remains one of the leading causes of death from disease. Unquestionable confirmation of intrinsic and natural components is crucial for the development of novel systems to detect and eliminate hazard. We propose the detection and categorization of liver illness in ultrasound pictures using hybrid machine learning techniques. Using Machine Learning Algorithms and a vote-based classifier to classify liver tissues as fatty or normal based on ultrasound picture attributes and voting. Our created method provides four major contributions: first, the classification of liver pictures as normal or fatty is performed without a segmentation phase. Secondly, compared to our suggested effort, the datasets in earlier works were inadequate. The third contribution is a collection of 26 characteristics. On the basis of the presented methods, sigmoid radial basis function neural network (SRBFNN) and Gray-Level Co-Occurrence Matrix are the recovered features (GLCM). The fourth contribution is the voting classifier that is utilised to determine the type of liver tissue. Fruit Fly Optimization concludes the categorization process (FFO). We have achieved a classification accuracy of 98.24% using our proposed approach. Convolutional Neural Network (CNN), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF), and Naive Bayes are compared with our proposed system (NB). The experimental results demonstrate that the classification accuracy of our suggested system is superior to that of the leading classification strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Radial basis function networks

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The neutron transport equation in exact differential form

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science China(Physics,Mechanics & Astronomy) 2025年第7期Mechanics & Astronomy) .卷 87-107页

作者： Dong Liu Yang Liu Haoning Dang Kai Wang Bin Zhang Fei Wang Zhouyu Liu Yong Jiang Nuclear Power Institute of China State Key Laboratory of Advanced Nuclear Energy Technology College of Computer Science (School of Software and School of Artificial Intelligence) Sichuan University Zhejiang Institute of Modern Physics School of PhysicsZhejiang University School of Mathematics and Statistics Xi'an Jiaotong University School of Energy and Power Engineering Xi'an Jiaotong University

Derived from the Boltzmann equation,the neutron transport equation describes the motions and interactions of neutrons with nuclei in nuclear devices such as nuclear *** collision or fission effect are described as integral terms which arrive in an integro-differential neutron transport equation(IDNT).Only for mono-material or simple geometries conditions,elegant approximation can simplify the transport equation to provide analytic *** solve this integro-differential equation becomes a practical engineering *** development of deep-learning techniques provides a new approach to solve them but for some complicated conditions,it is also time *** optimize solving the integro-differential equation particularly under the deep-learning method,we propose to convert the integral terms in the integro-differential neutron transport equation into their corresponding antiderivatives,providing a set of fixed solution constraint conditions for these antiderivatives,thus yielding an exact differential neutron transport equation(EDNT).The paper elucidates the physical meaning of the antiderivatives and analyzes the continuity and computational complexity of the new transport equation *** illustrate the significant advantage of ENDT,numerical validations have been conducted using various numerical methods on typical benchmark *** numerical experiments demonstrate that the EDNT is compatible with various numerical methods,including the finite difference method(FDM),finite volume method(FVM),and *** to the IDNT,the EDNT offers significant efficiency advantages,with reductions in computational time ranging from several times to several orders of *** EDNT approach may also be applicable for other integro-differential transport theories such as radiative energy transport and has potential application in astrophysics or other fields.

关键词： neutron transport equation exact differential form integro-differential equation deep learning antiderivative transformation

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An Interpretation of the Challenges and Solutions for Agriculture-based Supply Chain Management using Blockchain and IoT 7

An Interpretation of the Challenges and Solutions for Agricu...

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7th International Conference on computing Methodologies and Communication, ICCMC 2023

作者： Surya, M. Manohar, S. Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Tamilnadu India

ISBN: (纸本)9781665464086

Healthcare, the smart grid, and supply chain management are some of the industries where IoT(Internet of Things) technology is improving. Also, it improves the way humans communicate with each other, the atmosphere, and the people around them. Internet of things achieves this by using wearable technology, embedded sensors, and automated systems. The number, types, and sizes of IoT devices are increasing. There are issues with security, authenticity, reliability, and scalability. Because of the expected growth in IoT, IoT systems must be reliable and maintainable. Blockchain has helped solve IoT network problems. Blockchain improves trust, immutability, transparency, and release of agricultural data collection, processing, and sharing. Blockchain and IOT will streamline precision farming and transform normal farms into the Internet of Smart Farms. This study describes the IoT systems have issues, and blockchain-based solutions are suggested. Additionally, it discusses current research on the degree to which blockchain technology and Internet of Things networks. IoT integration inside the blockchain issues are also covered. This article proposes to double connect IoT and blockchain using recent computing techniques. This research work intends to make data processing and storage decentralized, secure and anonymous, and seamless and fast using blockchain technology and services. © 2023 IEEE.

关键词： Cloud computing architecture

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The highest melting point material:Searched by Bayesian global optimization with deep potential molecular dynamics

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Journal of advanced Ceramics 2023年第4期12卷 803-814页

作者： Yinan Wang Bo Wen Xingjian Jiao Ya Li Lei Chen Yujin Wang Fu-Zhi Dai Artificial Intelligence for Science Institute Beijing 100084China Science and Technology on Advanced Functional Composite Laboratory Aerospace Research Institute of Materials&Processing TechnologyBeijing 100076China College of Computer Science and Technology Heilongjiang Institute of TechnologyHarbin 150001China Institute for Advanced Ceramics School of Materials Science and EngineeringHarbin Institute of TechnologyHarbin 150001China Key Laboratory of Advanced Structural-Functional Integration Materials&Green Manufacturing Technology Harbin Institute of TechnologyHarbin 150001China DP Technology Bejing 100080China

The interest in refractory materials is increasing rapidly in recent decades due to the development of hypersonic ***,the substance that has the highest melting point(Tm)keeps a secret,since precise measurements in extreme conditions are overwhelmingly *** the present work,an accurate deep potential(DP)model of a Hf-Ta-C-N system was first trained,and then applied to search for the highest melting point material by molecular dynamics(MD)simulation and Bayesian global optimization(BGO).The predicted melting points agree well with the experiments and confirm that carbon site vacancies can enhance the melting point of rock-saltstructure *** solid solution with N is verified as another new and more effective melting point enhancing approach for HfC,while a conventional routing of the solid solution with Ta(e.g.,HfTa_(4)C_(5))is not suggested to result in a maximum melting *** highest melting point(~4236 K)is achieved with the composition of HfCo.638No.271,which is~80 K higher than the highest value in a Hf-C binary *** mechanism of the N addition is believed to be unstable C-N and N-N bonds in liquid phase,which reduces liquid phase entropy and renders the liquid phase less *** improved melting point and less gas generation during oxidation by the addition of N provide a new routing to modify thermal protection materials for the hypersonic vehicles.

关键词： melting point(T_(m)) carbides carbonitrides deep potential(DP) Bayesiannglobal optimization(BGO)

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