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Integrating Edge Intelligence with Blockchain-Driven Secured IoT Healthcare Optimization Model

computers, Materials & Continua 2025年第5期83卷 1973-1986页

作者： Khulud Salem Alshudukhi Mamoona Humayun Ghadah Naif Alwakid Department of Computer Science College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Department of Computing School of Arts Humanities and Social SciencesUniversity of RoehamptonLondonSW155PHUK

The Internet ofThings(IoT)and edge computing have substantially contributed to the development and growth of smart *** handled time-constrained services and mobile devices to capture the observing environment for surveillance *** systems are composed of wireless cameras,digital devices,and tiny sensors to facilitate the operations of crucial healthcare ***,many interactive applications have been proposed,including integrating intelligent systems to handle data processing and enable dynamic communication functionalities for crucial IoT ***,most solutions lack optimizing relayingmethods and impose excessive overheads for maintaining devices’***,data integrity and trust are another vital consideration for nextgeneration *** research proposed a load-balanced trusted surveillance routing model with collaborative decisions at network edges to enhance energymanagement and resource *** leverages graph-based optimization to enable reliable analysis of decision-making ***,mobile devices integratewith the proposed model to sustain trusted routes with lightweight privacy-preserving and *** proposed model analyzed its performance results in a simulation-based environment and illustrated an exceptional improvement in packet loss ratio,energy consumption,detection anomaly,and blockchain overhead than related solutions.

关键词： Smart cities load balancing blockchain health systems edge computing

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Accurate threat hunting in industrial internet of things edge devices

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Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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A review on network function virtualisation security challenges and design considerations

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International Journal of Networking and Virtual Organisations 2024年第4期31卷 368-393页

作者： Ahmad, Naveed Rathore, Muhammad Siraj Ngadi, Md Asri bin Faculty of Engineering University Teknologi Malaysia 81110 Malaysia Faculty of Computing Department of Computer Science Capital University of Science and Technology Islamabad46000 Pakistan

Network function virtualisation (NFV) offers several benefits to both network operators and end users. It is a more programmable and low-cost solution as compared to a traditional network. Since the network functions are implemented in software, there is a great opportunity of extending a network according to the changing requirements of the customers. However, at the same time, there are many security threats since the same commodity hardware is shared among multiple virtualised network functions. In literature, various NFV security threats are reported. In this work, our aim is to consolidate them in a more structured and organised manners. We highlight the different building blocks of a NFV framework and then explore how a particular component may become the victim of an attacker. Finally, we summarise our lesson learned and present open research issues which might be useful for NFV practitioners and researchers. © 2024 Inderscience Enterprises Ltd.

关键词： Network function virtualization

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Multi-Label Classification of Fundus Images with Optimized Vision Transformer Based Wasserstein Deep Convolutional Generative Adversarial Network

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SN computer science 2024年第7期5卷 842页

作者： Sreekumar, K. Vimina, E.R. Department of Computer Science & amp IT School of Computing Amrita Vishwa Vidyapeetham Kochi India

Early retinal disease diagnosis and treatment are essential for preventing the irreversible vision impairment. The patients in the clinical settings have various kinds of retinal disorders. Fundus image categorization is a multiple label categorization task since a fundus image contains one or more disorders. However, most previous research has focused on the diagnosis of a single fundus problem and there remain substantial obstacles to the simultaneous diagnosis of several fundus conditions. In this research work, Multi-Label Classification of Fundus Images with Optimized vision transformer based Wasserstein Deep Convolutional Generative Adversarial Network (MLC-FI-VF-WDCGAN) is proposed to accurately classify the Fundus disease images. Initially, the input image data is collected from Ocular Disease Intelligent Recognition (ODIR) dataset. The input images are preprocessed with Multivariate Fast Iterative Filtering (MFIF) for eliminating the noise and increasing the quality of input imageries. The pre-processing images are given to Multi-scale Neighborhood Feature Extraction (MS-NFE) method for extracting features such as, Geometric Features, Blood Vessel and Vascular Tortuosity for enhancing the classification accuracy. Then, the extracted features are provided to vision transformer based Wasserstein Deep Convolutional Generative Adversarial Network (VF-WDCGAN) for classifying the Fundus disease as Normal, Diabetic retinopathy, Glaucoma, Cataract, Hypertensive retinopathy and Myopia. The Vision-transformer acts as Discriminator in the WDCGAN and doesn’t reveal any adoption of optimization methods for calculating the ideal parameters in improved categorization accuracy. Hence, Giza Pyramids Construction Algorithm (GPCA) is proposed to improve the weight parameters of VF-WDCGAN. The MLC-FI-VF-WDCGAN technique is implemented in python and evaluated using several performances metrics such as accuracy, precision, recall. The performance of proposed MLC-FI-VF-WDCGAN met

关键词： Fundus diseases Giza pyramids construction algorithm Multi-scale neighborhood feature extraction Multivariate fast iterative filtering ODIR dataset Topological information embedded convolutional neural network Vision transformer based Wasserstein deep convolutional generative adversarial network

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CSG_IACO: an efficient stigmergic-based improved ACO routing strategy to determine the effective path based on traffic density in V2V networks

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1883-1908页

作者： Kumari, J. Jesy Janet Thangam, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Vehicular ad hoc networks (VANETs) are an essential element and building block of the autonomous vehicle system. VANETs, a subcategory of mobile ad hoc networks (MANETs), stand out due to certain predetermined attributes. In VANETs, optimal path routing offers solutions to address issues like traffic density, node mobility, link failure, and bandwidth. Finding the optimal VANET road path between source and destination conditions is difficult. Extensive paths increase network overhead, communication costs, and failure probability, reducing routing efficiency. We present the new traffic density-based stigmergic ant colony optimization routing (CSG_IACO) to improve routing performance. Improved ant colonies stimulate the proposed CSG_IACO, which measures inter-vehicular density. Ant behavior serves as the inspiration for the improved ACO technique, a meta-heuristic methodology. Phase 1 of the CSG_IACO involves using the traffic density to form a cluster among the vehicles. Phase 2 of the suggested approach is Stigmergy-based ant colony optimization, which guides ants to indirectly communicate with each other to act upon the change in the environment to find a best path. This improves protocol efficiency in all aspects. The simulations using NS 2.35 and SUMO indicate that the proposed system considerably reduces obstacles to finding the optimal path compared to existing systems. The simulations show that our suggested scheme performs better than AODV-DATRLD, ACO-DATRLD, and CaCOIoV in terms of performance including the count of delivered packets, throughput, end-to-end delay, the average count of cluster formations per cluster head, and the number of dropped packets. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Ant colony optimization Cluster Optimal path QoS Stigmergy Traffic density VANETs

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Hyper-personalized employment in urban hubs: multimodal fusion architectures for personality-based job matching

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Neural computing and Applications 2024年 1-16页

作者： Jain, Dipika Sangwan, Saurabh Raj Kumar, Akshi Department of Computer Science and Engineering Delhi Technological University New Delhi India School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computing Goldsmiths University of London London United Kingdom

In the evolving landscape of smart cities, employment strategies have been steering towards a more personalized approach, aiming to enhance job satisfaction and boost economic efficiency. This paper explores an advanced solution by integrating multimodal deep learning to create a hyper-personalized job matching system based on individual personality traits. We employed the First Impressions V2 dataset, a comprehensive collection encompassing various data modalities suitable for extracting personality insights. Among various architectures tested, the fusion of XceptionResNet with BERT emerged as the most promising, delivering unparalleled results. The combined model achieved an accuracy of 92.12%, an R2 score of 54.49%, a mean squared error of 0.0098, and a root mean squared error of 0.0992. These empirical findings demonstrate the effectiveness of the XceptionResNet + BERT in mapping personality traits, paving the way for an innovative, and efficient approach to job matching in urban environments. This work has the potential to revolutionize recruitment strategies in smart cities, ensuring placements that are not only skill-aligned but also personality-congruent, optimizing both individual satisfaction and organizational productivity. A set of theoretical case studies in technology, banking, healthcare, and retail sectors within smart cities illustrate how the model could optimize both individual satisfaction and organizational productivity. © The Author(s) 2024.

关键词： Deep learning

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Say what you want, I'm not listening! A conversational self-reflection robot that does not parse user speech

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i-com 2023年第1期22卷 19-32页

作者： González, Adriana Lorena Geiskkovitch, Denise Y. Young, James E. Department of Computer Science University of Manitoba Winnipeg Canada Department of Computing and Software McMaster University Hamilton Canada

We present a conversational social robot behaviour design that draws from psychotherapy research to support individual self-reflection and wellbeing, without requiring the robot to parse or otherwise understand what the user is saying. This simplicity focused approached enabled us to intersect the well-being aims with privacy and simplicity, while achieving high robustness. We implemented a fully autonomous and standalone (not network enabled) prototype and conducted a proof-of-concept study as an initial step to test the feasibility of our behaviour design: whether people would successfully engage with our simple behaviour and could interact meaningfully with it. We deployed our robot unsupervised for 48 h into the homes of 14 participants. All participants engaged with self-reflection with the robot without reporting any interaction challenges or technical issues. This supports the feasibility of our specific behaviour design, as well as the general viability of our non-parsing simplicity approach to conversation, which we believe to be an exciting avenue for further exploration. Our results thus pave the way for further exploring how conversational behaviour designs like ours may support people living with loneliness. © 2023 the author(s), published by De Gruyter, Berlin/Boston.

关键词： Robots

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Multiclass Classification for Cyber Threats Detection on Twitter

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computers, Materials & Continua 2023年第12期77卷 3853-3866页

作者： Adnan Hussein Abdulwahab Ali Almazroi College of Computer Science and Engineering Department of Computer ScienceAL-Ahgaff UniversityMukallaYemen College of Computing and Information Technology at Khulais Department of Information TechnologyUniversity of JeddahJeddahSaudi Arabia

The advances in technology increase the number of internet systems *** a result,cybersecurity issues have become more *** threats are one of the main problems in the area of ***,detecting cybersecurity threats is not a trivial task and thus is the center of focus for many researchers due to its *** study aims to analyze Twitter data to detect cyber threats using a multiclass classification *** data is passed through different tasks to prepare it for the *** Frequency and Inverse Document Frequency(TFIDF)features are extracted to vectorize the cleaned data and several machine learning algorithms are used to classify the Twitter posts into multiple classes of cyber *** results are evaluated using different metrics including precision,recall,F-score,and *** work contributes to the cyber security research *** experiments revealed the promised results of the analysis using the Random Forest(RF)algorithm with(F-score=81%).This result outperformed the existing studies in the field of cyber threat detection and showed the importance of detecting cyber threats in social media *** is a need for more investigation in the field of multiclass classification to achieve more accurate *** the future,this study suggests applying different data representations for the feature extraction other than TF-IDF such as Word2Vec,and adding a new phase for feature selection to select the optimum features subset to achieve higher accuracy of the detection process.

关键词： Cybersecurity cyber threat detection artificial intelligence machine learning Twitter

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Unraveling Dementia Severity: A Deep Learning Approach for Brain MR Image-Based Prediction and Misclassification Analysis 3

Unraveling Dementia Severity: A Deep Learning Approach for B...

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3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Nair, Rekha R. Babu, Tina Singh, Tripty Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Alzheimer's disease is the most prevalent cause of dementia, and its early diagnosis is crucial to prevent the progression to severe stages where cognitive abilities are severely impaired. This research paper presents an innovative approach to predict the severity of dementia through classification and grading. The research introduces an innovative adaptation of the DEMNET model, referred to as the DEMENtia network model. The research implements a novel methodology leveraging Convolutional Neural Networks (CNNs) to identify significant patterns within unorganized web-based data collections. The investigation employs a dataset com- prising four categories, obtained from the Kaggle platform. The developed model demonstrates exceptional performance, achieving 99.9% accuracy during training, 97.4% accuracy in testing, and an overall precision of 0.975. The DEMENtia network model suc- cessfully categorizes individuals into four groups: those without dementia, and those with moderate, mild, or very mild dementia. The model achieves a remarkable accuracy of 99.20% in classifying the Moderate demented class, a significant advantage over existing approaches. To understand this behavior, conducted an in-depth analysis by visualizing the pixel intensity distribution over the space. The proposed model validity has been confirmed through validation by a team of neurologists, ensuring its potential for real-world clinical applications. By accurately predicting dementia severity, the proposed model can aid in early diagnosis and treatment planning, contributing to improved patient care and management. © 2025 Elsevier B.V.. All rights reserved.

关键词： Convolutional neural networks

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Crypto Trading Platform: MERN Stack with Real-Time Data and Social Features 2

Crypto Trading Platform: MERN Stack with Real-Time Data and ...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Gurupriya, M. Mouhitha, A. Gujjula, Saranya Ankireddypalli, Okesh Reddy Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798331505745

The increasing complexity of cryptocurrency markets necessitates the development of efficient portfolio management tools that provide real-time tracking, price updates, and market awareness. This paper focuses on an advanced concept of a cryptocurrency wallet service provision that provides a summary of available coins, their prices, available quantities, and customers' transaction histories. Developed for the 'frontend' using *** and for the 'backend' using ***, which uses the CoinGecko API for receiving live market prices and collecting curated news blog articles from other APIs. The data related to the wallets are efficiently managed since the application is built based on the MongoDB schema, and in addition to this, the user authentication is also integrated. A responsive design of the user interface allows for showing both a portfolio summary and the latest news related to cryptocurrencies, thus suggesting an 'everything in one place' solution for users interested in cryptocurrencies. This system shows how real-time data integration could be implemented, how the system is friendly to users, and how the system manages the content. This system could be expanded in the future with predictive analytics and an AI recommendation system for the portfolio management and contents. © 2025 IEEE.

关键词： Financial markets

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