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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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Domain Modelling For A Lightweight Convolutional Network Focused On Automated Exudate Detection in Retinal Fundus Images 9

Domain Modelling For A Lightweight Convolutional Network Foc...

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9th International Conference on information technology Trends, ITT 2023

作者： Aydin, Burcu Ataer Hussain, Muhammad Hill, Richard Al-Aqrabi, Hussain University of Huddersfield School of Computing and Engineering Huddersfield United Kingdom Higher Colleges of Technology Faculty of Computer Information Science Sharjah United Arab Emirates

ISBN: (纸本)9798350327502

Retinal Fundus images contain important signs implying various stages of diabetic retinopathy. One of the early signs of diabetic retinopathy is exudates. The timely detection of this sign from fundus images can prevent or at least suppress the advancement of this condition. To this effect, this research presents a CNN-based automated exudate detection architecture for the timely detection of this early sign. In order to overcome the challenges of a limited dataset several domain specific augmentations are proposed for improving the generalization capacity of the developed architecture. The lightweight architecture consists of only 6.42 million parameters, compared to Resnet-18 (11.69 million) whilst achieving an overall F1 score of 89% © 2023 IEEE.

关键词： Computer vision

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Multi-Scale Analysis Technique for Low Energy Data Aggregation 5th

Multi-Scale Analysis Technique for Low Energy Data Aggregati...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Alam, Intekhab Krishnamoorthy, Ramkumar Verma, Tushar K. Raghavendra, T.S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

The multiscale evaluation technique is a low-power facts aggregation method using numerous evaluation scales to mix data from several properties. The method is based on multiscale structures, in which more than one scale of facts can be leveraged to advantage notion into centered styles within an individual source or in the course of resources. Multiscale assessment allows an accurate and dependable example of facts in evaluation compared to conventional unmarried-scale techniques. It’s miles beneficial for studying structures with sparse or low-first-rate information, wherein more significance than the facts accumulated is needed to constitute the general device because it needs to be. Furthermore, this technique can detect styles in records from randomly sampled belongings or residences that might be temporally or spatially separated. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： conventional evaluation Multiscale

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Emotional changes induced by associative memory

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The European Physical Journal Special Topics 2025年 1-11页

作者： Ren, Xiyu Xu, Xianying Al-Barakati, Abdullah A. Cao, Yinghong School of Information Science and Engineering Dalian Polytechnic University Dalian China School of Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Associative memory and Hopfield neural networks are an important area where artificial intelligence and neuroscience are intertwined, and the core idea is to study the mechanisms of information storage and retrieval in the human brain. In this paper, two memristors are introduced into a three-dimensional Hopfield neural network as electronic synapses to simulate the associative memory function of the hippocampus in the human brain. To begin with, the rationality of the two memristors' designs is verified. Next, the effect on emotion produced when the synaptic weights are changed is investigated using numerical analysis. The firing pattern of Hopfield neural networks was also investigated. The changes made by different emotional states in response to the same associative memory stimulus are also analyzed. Finally, the DSP platform verifies the feasibility of the MATLAB simulation results. This paper provides a theoretical basis for the emotional design of intelligent robots.

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Exploring the Use of Transfer Learning for Automated Brain Tumor Segmentation 5th

Exploring the Use of Transfer Learning for Automated Brain T...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Savita Jaison, Feon Nautiyal, Kshitij Sunitha, B.J. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Switch getting to know has become increasingly famous in many aspects of pc vision, including picture segmentation. This paper explores switch-gaining knowledge for automated brain tumor segmentation using a deep getting-to-know structure known as U-net. U-internet is a convolution neural community version that has been used efficiently to ramify scientific image segmentation obligations in the beyond. This work utilizes a U-internet model skilled on a huge-scale brain tumor segmentation dataset. The version plays nicely at the utility of brain tumor segmentation, achieving an average cube coefficient of zero. Ninety-three on check scans. Moreover, qualitative consequences display that the U-net version produces segmentation masks with few false-nice mistakes. These consequences reveal the capability of switch getting to know for automatic mind tumor segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Developing Efficient Game Theory Approaches for Spectrum Sharing in 6G Networks 5th

Developing Efficient Game Theory Approaches for Spectrum Sha...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Ramola, Abhishek C. Francis, Jerrin Joe Savita Murugan, R. Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

This paper provides the development of green game idea procedures for spectrum sharing in upcoming 6G cellular networks. As dynamic spectrum get entry to keeps to advantage commercial interest, the want to broaden a sustainable framework to make sure powerful and relaxed spectrum sharing arises. To this stop, sport principle methods are seen as a legitimate candidate to facilitate robust spectrum sharing in 6G networks. This paper offers unique attention to the area of spectrum sharing and how the underlying game theory procedures may be leveraged to percentage spectrum efficiently. After an intensive review of existing methods and strategies, an overall performance evaluation of the numerous strategies is provided. Subsequently, the paper gives insights at the potential of game concept strategies in future 6G networks. Moreover, much game theoretic equipment are used for analytical as well as simulation experiments if you want to higher apprehend the dynamics of the spectrum sharing. Basic, this paper examines the effectiveness of each analytical and simulation-based totally game theoretic fashions for spectrum sharing in 6G networks, and gives a new version which may be used to improve the spectrum sharing dynamics in such networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spread spectrum communication

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Unraveling Disease-Specific Diffusion Patterns in Human Brain Using Diffusion Tensor Image Analysis 5th

Unraveling Disease-Specific Diffusion Patterns in Human Brai...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Prajapati, Sandhya Bhagawati, Rupam Alam, Intekhab Jebakumari, S. Adlin Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Diffusion tensor imaging (DTI) is a feasible approach to have a look at the structural connectivity of neuronal networks in vivo, as it presents insights into the microstructural and molecular properties of white depend fiber bundles. On this study, we used superior DTI and diffusion tensor image evaluation to analyze the unique diffusion patterns of several distinct diseases in human mind. We obtained statistics from healthful controls and cohorts of sufferers with a range of sicknesses, along with stroke, annoying mind injury, Alzheimer’s ailment, and Parkinson’s ailment, in addition to from postmortem brains of each wholesome and diseased people. Our findings emphasize the capability of the use of this technique to increase ailment-precise structural networks biomarkers for diagnosis and control of neurological illnesses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diffusion tensor imaging

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Enhancing Cardiovascular Disease Analytics with Demographic and Behavioral Risk Features through Unsupervised and Ensemble Learning Techniques 12

Enhancing Cardiovascular Disease Analytics with Demographic ...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Sem, Li Xiang Chua, Hui Na Jasser, Muhammed Basheer Wong, Richard T.K. Aldharhani, Ghassan Saleh Sunway University No. 5 Jalan Universiti School of Engineering and Technology Department of Computing and Information Systems Bandar Sunway Selangor Darul Ehsan47500 Malaysia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia UKM University Faculty of Information Science and Technology Selangor Bangi Malaysia

ISBN: (纸本)9798350391398

Cardiovascular diseases (CVD) are a leading health concern, projected to affect 23.6 million people by 2030. Despite extensive research, challenges remain in conventional diagnostic accuracy and limited healthcare access in low-income communities. Current computing methods for detecting CVD rely on costly medical tests. Subsequently, prior studies have documented many inconsistent insights on the development of CVD due to variations in study design and demographics. This research addresses these gaps by studying the potential of incorporating demographics and behavioral risk factors for CVD detection and experimenting with various modeling and sampling techniques to address imbalanced target distributions and improve detection accuracy. The resampled BRFSS dataset was subjected to regression analysis to capture demographic trends in CVD development, with two cross-referenced datasets for comparison. Association rule mining identified key risk factors linked to CVD. Classifiers, including XGBoost, Random Forest, SVM, Multilayer Perceptron, and Logistic Regression, were used to establish baseline performance, and these classifiers were integrated into CatBoost as a meta-classifier using a stacking ensemble approach. Our findings indicate that demographic factors significantly influence CVD development, but effects vary by region, culture, and lifestyle. Key risk factors such as diabetes, smoking, and stroke were identified as significant contributors to CVD. Our experiment results show that leveraging full features set with fine-tuned ensemble stacking techniques increases model performance and achieves an accuracy of 96%. Our research contributes valuable insights into the consistency of demographic effects and identifies key risk factors for CVD. The proposed ensemble model demonstrates exceptional performance, highlighting its potential as a cost-effective tool for early cardiac detection and improved healthcare accessibility. © 2024 IEEE.

关键词： Contrastive Learning

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Exploring the Use of Mobile Ad-hoc Networks for Real-Time Automotive Applications 5th

Exploring the Use of Mobile Ad-hoc Networks for Real-Time Au...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Raghavendra, R. Negi, Bichitra Singh Manasa, C.M. Mishra, Awakash Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper investigates using cell networks (MANETs) for real-time automotive packages. The car industry increasingly seeks to benefit from the latest technology to improve its merchandise, and cellular ad hoc networks can revolutionize the enterprise. This paper outlines the use of MANETs within the automobile industry and uses simulations to explore the benefits and constraints of implementing MANETs inside the automotive space. Additionally, the paper explores the safety and privacy concerns of MANETs and how automobile applications can use the era comfortably and competently. The findings of this observation provide the perception of how MANETs may be applied for automobile programs and highlight the potential of cell ad-hoc networks as a crucial technology for the industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mobile ad hoc networks

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Finite-Time Robust Controller Using Sliding Mode Approach for Grid-Connected Inverters

Finite-Time Robust Controller Using Sliding Mode Approach fo...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Hosseinabadi, Pooyan Alinaghi Mekhilef, Saad Pota, Hemanshu Kermadi, Mostefa University of New South Wales School of Engineering and Information Technology Australia Swinburne University of Technology School of Science Computing and Engineering Technologies Australia University of Malaya Power Electronics and Renewable Energy Research Laboratory Malaysia

ISBN: (纸本)9781665471640

This article suggests and experimentally implements a new finite-time robust current controller for grid-connected three-phase two-level inverters through an RL filter under unknown parameter variations. New advanced types of nonlinear sliding mode control laws are defined to ensure the strong robustness of the closed-loop system under the proposed controller against the variations of the filter parameters. Chattering elimination is addressed in the proposed sliding mode control laws. The finite-time stability notion is considered in the proposed approach to guarantee stability within finite settling time. The stability proof of the closed-loop system is presented utilizing the Lyapunov theory. The effectiveness of the suggested approach in fast and robust dynamic responses and alleviation of the chattering compared with the conventional sliding mode controller is demonstrated using simulation and experimental results. © 2023 IEEE.

关键词： finite-time stability grid-connected inverters robust control sliding mode

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