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Blockchain and IIoT Enabled Solution for Social Distancing and Isolation Management to Prevent Pandemics

computers, Materials & Continua 2023年第7期76卷 687-709页

作者： Muhammad Saad Maaz Bin Ahmad Muhammad Asif Muhammad Khalid Khan Toqeer Mahmood Elsayed Tag Eldin Hala Abdel Hameed College of Computing and Information Sciences Karachi Institute of Economics&TechnologyKarachiPakistan Department of Computer Science Lahore Garrison UniversityLahorePakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Faculty of Engineering and Technology Future University in Egypt New Cairo11835Egypt Faculty of Computer and Information Systems Fayoum UniversityFaiyumEgypt Khaybar Applied College Taibah UniversityMedinaSaudi Arabia

Pandemics have always been a nightmare for humanity,especially in developing *** lockdowns are considered one of the effective ways to deal with spreading such ***,developing countries cannot afford such solutions because these may severely damage the country’s ***,this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations *** novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things(IIoT)and blockchain-enabled *** to existing studies,the immutable and tamper-proof contact tracing and quarantine management solution is *** use of advanced technologies and information security is a critical area for practitioners in the internet of things(IoT)and corresponding ***,this study also emphasizes information security,end-to-end solution,and experimental ***,a wearable wristband is proposed,incorporating 4G-enabled ultra-wideband(UWB)technology for smart contact tracing mechanisms in industries to comply with standard operating procedures outlined by the world health organization(WHO).Secondly,distributed ledger technology(DLT)omits the centralized dependency for transmitting contact tracing ***,a privacy-preserving tracing mechanism is discussed using a public/private key cryptography-based authentication ***,based on geofencing techniques,blockchain-enabled machine-to-machine(M2M)technology is proposed for quarantine *** step-by-step methodology and test results are proposed to ensure contact tracing and quarantine *** existing research studies,the security aspect is also considered in the realm of *** practical implementation of the proposed solution also obtains the *** results indicate the successful implementation of blockchain-enabled contac

关键词： Blockchain contact tracing distributed ledger technology geo-fencing internet of things industrial internet of things isolation management social distancing ultra-wideband

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Optimal Energy Forecasting Using Hybrid Recurrent Neural Networks

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Intelligent Automation & Soft Computing 2023年第4期36卷 249-265页

作者： Elumalaivasan Poongavanam Padmanathan Kasinathan Kulothungan Kanagasabai Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Electrical and Electronics Engineering Agni College of TechnologyChennai600130India Department of Information Science and Technology Anna UniversityChennai600025India

The nation deserves to learn what India’s future energy demand will be in order to plan and implement an energy *** energy demand will have to be fulﬁlled by an adequate mix of existing energy sources,considering the con-straints imposed by future economic and social changes in the direction of a more sustainable *** energy demand,on the other hand,is a tricky task because it is inﬂuenced by numerous *** a result,an macro model with only a few factors that may be predicted globally,rather than a detailed ana-lysis for each of these variables,is *** this work,a hybrid approach is proposed for identifying the optimal generation-based mix of electricity systems in *** approach is developed by combing Recalling-Enhanced Recurrent Neural Network(RERNN)and Giza Pyramids Construction(GPC).RERNN pos-sesses selective memory features whereas GPC is a meta-heuristic algorithm that deals with different sets of *** goal of this approach is to assess the present load requirements and production proﬁle to understand the current requirement and its production *** on regards to electricity load proﬁle involving the generation of all power plants,capacity factors and transmission limits were gathered from different sources including the Indian National Load Dispatch Centre,Central Electricity Authority database,annual reports of India and regional load dispatching *** proposal has introduced the RERNN technique to analyze the optimal time series whereas the GPC technique is involved in the optimization of RERNN *** present work is simu-lated in MATLAB and the performance assessed with different *** present technique gives an effective results in terms of accuracy,computational efﬁciency and feasibility.

关键词： Electricity generation demand RERNN GPC time series prediction

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Exploring Quantum Machine Learning Algorithms for Enhanced Data Classification and Clustering in Complex Systems - Integration with Gradient Boosting and K-means for Improved Performance

Exploring Quantum Machine Learning Algorithms for Enhanced D...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Babu, M. Arun, M. Pushparani, S. Muthukrishnan, A. Poongavanam, N. Rajalakshmi Engineering Çollege Department of Information Technology Thandalam Chennai India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

This research focuses on the implementation of quantum machine learning with the classical models such as Gradient Boosting and K-means clustering for efficient classification and clustering of most complex datasets in advanced systems. Taking advantage of the large datasets that are handled by the qc, this work examines Quantum Support Vector Machines (QSVM), Quantum Boosting, and Quantum K-means. To compare the efficiency of these hybrid models three datasets - the healthcare, the financial, and the cyber-security datasets - are used. Our findings indicate that quantum optimization algorithms achieve better accuracy in clustering in comparison to the classical counterparts and are computationally faster. This work shows how quantum machine learning can lead to the enhancement of difficult data processing tasks, a major issue in the quantum computing domain, with specific reference to resources and high-dimensional data. The opportunity to integrate quantum and classical models successfully is the best basis for further developments in machine learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Integrating Grey Wolf Optimizer with U-Net for Precision Retinal Vessel Segmentation in Fundus Images 1st

Integrating Grey Wolf Optimizer with U-Net for Precision Ret...

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1st International Conference on Data engineering and Machine Intelligence, ICDEMI 2023

作者： Sengan, Sudhakar Lambture, Bhagyashree Lazar, Arokia Jesu Prabhu Benarji, Bejjam S. N. Bakkiyaraj, Jebaranjani Sharma, Dilip Kumar Lakineni, Prasanna Kumar Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India Department of AI&DS AISSMS IOIT Pune411001 India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad501401 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of Mathematics Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Computer Science and Engineering Sri Venkateswara College of Engineering Andhra Pradesh Tirupathi517502 India

ISBN: (纸本)9789819776153

Retinal Vessel Segmentation (RVS) is pivotal in diagnosing and monitoring ocular diseases. While Deep Learning (DL) methodologies, particularly Convolutional Neural Networks (CNN), have revolutionized medical image analysis, achieving consistent and superior segmentation accuracy remains challenging. The conventional U-Net architecture, despite its merits, presents limitations in terms of adaptability and precision. This research article introduces a novel, "Hybrid U-Net Deep Neural Network" tailored for RVS in Fundus Images (FI). This approach integrates the Grey Wolf Optimizer (GWO) to fine-tune U-Net’s hyperparameters and elevate its segmentation accuracy. The GWO’s intrinsic ability to search for optimal solutions optimizes the U-Net’s performance. Comparative analyses on benchmark datasets, HRF and STARE, underscore this model’s superior segmentation results. Through rigorous quantitative and qualitative evaluations, the hybrid model significantly enhances RVS, positioning it as a promising tool for clinical and research applications in ophthalmology. This research demonstrates the potential of integrating optimization algorithms with Deep Learning (DL) and paves the system for future advancements in Medical Image Segmentation (MIS). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Lightweight Secure Communication Scheme Based on PUF for In-vehicle Controller Area Networks

Lightweight Secure Communication Scheme Based on PUF for In-...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Jiang, Haoran Wei, Yehua Li, Wenjia Li, Jiangwei Hunan Normal University College of Information Science and Engineering Changsha China New York Institute of Technology Department of Computer Science New York United States

ISBN: (纸本)9798350367942

The Controller Area Network (CAN) is the most widely used protocol for data transmission in in-vehicle networks. However, the lack of a robust security scheme makes it vulnerable to various cyber-attacks, posing significant threats to automotive functional safety. The limited computing resources of Electronic Control Units (ECUs), restricted network bandwidth, and high real-time requirements make it challenging to apply conventional encryption and authentication mechanisms. This paper proposes a lightweight secure communication scheme using encryption and authentication based on Physically Unclonable Function (PUF) technology. We first designed a ring oscillator PUF with a multi-loop comparison circuit (MLC-ROPUF) suitable for in-vehicle networks with constrained resources. The MLC-ROPUF is used to generate key parameter for the lightweight encryption algorithm Salsa20, which is applied to distribute shared key parameters to different ECUs. After receiving the CAN message, ECU utilizes the output response of MLC-ROPUF to generate the key stream of Salsa20 and decrypt the data segment of the CAN message, obtain the shared key parameters. The SM3 algorithm is then utilized to generate shared keys for confidential communication based on the obtained key parameters. Simultaneously, ECU identity authentication is completed to reduce communication costs. To validate the proposed scheme, we conducted experiments on a designed prototype system, and the results show that the proposed method performs well in terms of resource consumption and real-time capabilities. © 2024 IEEE.

关键词： Authentication

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Predictive Modeling of Epidemic Diseases based on Vector-Borne Diseases using Support Vector Machine with Extreme Gradient Boost 1

Predictive Modeling of Epidemic Diseases based on Vector-Bor...

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1st International Conference on software, Systems and Information technology, SSITCON 2024

作者： Satish, E.G. Haq, Quazi Mohd. Imranul Mahaboob John, Y.M. Hassan, Muntather Muhsin Jothi, E. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India University of Nizwa College of Arts and Sciences Department of Biological Sciences and Chemistry Nizwa Oman Mahendra College of Engineering Department of Electronics and Communication Engineering Salem India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350352931

In recent years, the globalization and progressive climatic changes lead to increase in the occurrence of Vector Borne Diseases (VBD) from endemic regions to non-endemic regions. The common forms of VBD includes malaria, chikungunya, dengue fever, yellow fever and plague. The existing methods for predicting epidemic diseases did not handle non-linear relationships and complex interactions. Therefore, this paper proposes Support Vector Machine with Extreme Gradient Boost (SVM-XGBOOST) for predicting epidemic diseases based on vector borne diseases. Initially, vector borne diseases dataset is taken which contains epidemiological data and information related to climate. Then, this data is preprocessed by using cross validation to generalize the model well and it evaluates the quality of data. After that, Least Absolute Shrinkage and Selection Operator (LASSO) or (L1Regularization) is used for selecting features which selects the relevant features automatically and handles noisy data. finally, classification is done by using SVM-XGBOOST which handles non-linear relationships and handles complex interactions. The proposed SVM-XGBOOST achieved better results in terms of accuracy (98.45%), Precision (95.50%), Recall (97.98%), and F1 score (9 7. 2 5 %). © 2024 IEEE.

关键词： Vectors

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Stackelberg Game-Based Pricing and Offloading for the DVFS-Enabled MEC Systems

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2502-2515页

作者： Mei, Jing Zeng, Cuibin Tong, Zhao Yang, Zhibang Li, Keqin Hunan Normal University College of Information Science and Engineering Changsha410081 China Changsha University in Changsha Hunan Province Key Laboratory of Industrial Internet Technology and Security Hunan 410022 China Hunan University College of Information Science and Engineering Changsha410012 China National Supercomputing Center Hunan Changsha410082 China State University of New York Department of Computer Science New YorkNY12561 United States

Due to the limited computing resources of both mobile devices (MDs) and the mobile edge computing (MEC) server, devising reasonable strategies for MD task offloading, MEC server resource pricing, and resource allocation is crucial. In this paper, a scenario is considered, comprising multiple MDs and a single MEC server. Each MD has a divisible task in each time slot, allowing for partial offloading and the option to discard parts of the task. The MEC server contains multiple computing units with the same computing power, and its computing resources can be dynamically adjusted through dynamic voltage and frequency scaling (DVFS) according to the size of tasks offloaded by MDs. At any given time slice, a Stackelberg game is formulated based on the strategies of the MDs and the strategy of the MEC server. An iterative evolution algorithm is employed to explore the optimal strategies for MDs and the MEC server. Simulation results demonstrate that both parties can reach an equilibrium state through the game, and these experiments confirm that the algorithm effectively enhances system efficiency. © 2004-2012 IEEE.

关键词： Mobile edge computing

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Data Privatization and Security using Bit Plane Complexity Segmentation 1

Data Privatization and Security using Bit Plane Complexity S...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Suresh Kumar, S. Chidambaram, G. Vijayalakshmi, S. Computer Science and Engineering PSG College of Technology Coimbatore India

ISBN: (纸本)9781665460842

Advances in digital communication play an important role in our daily lives in today's world. In general, information security systems fall into two categories: encryption and information hiding. The storage and transmission of sensitive personal data have become a partof everyday life for professional and personal well-being. Therefore, the issue of secure storage and transmission of confidential information is interest to many researchers. Steganography is a collective method to hide sensitive data in unobtrusive digital media such as video, audio, and images. One of the biggest challenges in developing steganography systems is finding the right balance between metrices like reliability, security, and data privacy. This project provides information about how the Bit Plane Complexity Steganography (BPCS) technology is applied toboth grayscale and color imaging techniques and assesses performance parameters such as data concealment, security and stability based on the peak signal-to-noise ratio (PSNR) and Mean Square Error (MSE) with various test cases to explain how it works for different files and images. © 2022 IEEE

关键词： Cryptography

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Data-driven Seeing Prediction for Optics Telescope:from Statistical Modeling,Machine Learning to Deep Learning Techniques

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Research in Astronomy and Astrophysics 2022年第12期22卷 152-165页

作者： Wei-Jian Ni Quan-Le Shen Qing-Tian Zeng Huai-Qing Wang Xiang-Qun Cui Tong Liu College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266510China Nanjing Institute of Astronomical Optics&Technology National Astronomical ObservatoriesNanjing 210042China

Predicting seeing of astronomical observations can provide hints of the quality of optical imaging in the near future,and facilitate flexible scheduling of observation tasks to maximize the use of astronomical *** approaches to seeing prediction mostly rely on regional weather models to capture the in-dome optical turbulence *** to the developing of data gathering and aggregation facilities of astronomical observatories in recent years,data-driven approaches are becoming increasingly feasible and attractive to predict astronomical *** paper systematically investigates data-driven approaches to seeing prediction by leveraging various big data techniques,from traditional statistical modeling,machine learning to new emerging deep learning methods,on the monitoring data of the Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST).The raw monitoring data are preprocessed to allow for big data *** we formulate the seeing prediction task under each type of modeling framework and develop seeing prediction models through using representative big data techniques,including ARIMA and Prophet for statistical modeling,MLP and XGBoost for machine learning,and LSTM,GRU and Transformer for deep *** perform empirical studies on the developed models with a variety of feature configurations,yielding notable insights into the applicability of big data techniques to the seeing prediction task.

关键词： methods:data analysis methods:statistical telescopes

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Detection of Stage of Cancer Using Machine Learning and Deep Learning 5th

Detection of Stage of Cancer Using Machine Learning and Deep...

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5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Ashtagi, Rashmi Akkalkot, Afsha Imran Kotecha, Ketan Maginmani, Umme Habiba Patil, Pushpalata Patil, Abhijeet Computer Engineering Department Vishwakarma Institute of Technology Pune India Computer Engineering Department College of Engineering Pune Technological University Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India Computer Science and Engineering SECAB Institute of Engineering and Technology Affiliated to Visvesvaraya Technological University Vijayapur India Computer Science and Engineering Faculty of Engineering and Technology Exclusively for Women Sharnbasva University Kalburagi India RPD PU College of Arts and Commerce Belagavi India

ISBN: (纸本)9789819761029

Melanoma, an aggressive form of skin cancer known for its rapid spread, poses a significant threat and is responsible for a substantial number of fatalities. The accurate classification of cancer stages is crucial for effective diagnosis, particularly during surgical treatment. This paper introduces two methods for categorizing melanoma cancer stages. Initial system distinguishes between stage 1 and stage 2, while the second system extends the classification to include stage 3 melanoma. Leveraging a Convolutional Neural Network (CNN) algorithm, the proposed system incorporates the Similarity Measure for Text Processing (SMTP) as a loss function. The experimental results showcase the efficacy of different loss functions, with a particular focus on the proposed SMTP loss function, which outperforms several others designed for classification problems. This algorithm stands out as a more efficient approach for melanoma stage classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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