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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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Electrocardiogram Classification Using Convolutional Neural Network 5th

Electrocardiogram Classification Using Convolutional Neural ...

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5th International Conference on computing, Communications, and Cyber Security, IC4S 2023

作者： Kachhadiya, Kashyap Hasmani, Abdullah Gorasiya, Drashti Patel, Bansari Patel, Krishna Vadhavana, Vaishali Department of Computer Science and Engineering Devang Patel Institute of Advance Technology and Research Charotar University of Science and Technology Changa India

ISBN: (纸本)9789819773701

Circulation of Blood relies significantly precise electrical initiation. Arrhythmias can arise from any disturbance in the excitatory wave’s regular pattern in the heart. Electrocardiograms (ECG) are a popular diagnostic tool due to their speed, affordability, and non-invasive nature. This article presents a method for classifying ECGs that makes use of Convolutional Neural Network (CNNs) and the Continuous Wavelet Transform. Using Continuous Wavelet Transform (CWT), ECG signals are broken down into different time–frequency parts, while CNN extracts feature from the resulting 2D-scalogram. To further enhance the model considering the RR interval for arrhythmia diagnosis, 4 RR interval parameters are taken out and connected to CNN parameters to provide an ECG that is completely integrated classifier layer. This proposed model examines the impact of CNN and CWT on ECG classification alongside RR intervals. After training Accuracy of 97.27% was achieved. Based on the proposed method further models can designed for efficient clinical applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Digital Twin, Metaverse and Smart Cities in a Race to the Future 24

Digital Twin, Metaverse and Smart Cities in a Race to the Fu...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Zainab, Hijab E Bawanay, Narmeen Zakaria Jinnah University for Women Center for Computing Research Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798350384307

In the dynamic landscape of urban development, the synergy of the Metaverse, Digital Twins, and Smart Cities emerges as a potent catalyst for transformation. The Metaverse, a vibrant virtual space, collaborates with cutting-edge technologies, including Virtual Reality (VR), Augmented Reality (AR), the Internet of Things (IoT), and Artificial Intelligence (AI), crafting immersive experiences. Simultaneously, Digital Twins, serving as virtual replicas of physical entities, instigating an industrial revolution, while Smart Cities harness advanced technologies for sustainable urban living. This paper explores the applications of converging these technologies across diverse industries, showcasing how Digital Twins reshape conventional practices and amplify the concept of smart cities through the immersive realm of the Metaverse. However, the exploration unveils challenges, particularly in data privacy, interoperability, and ethical considerations. The study advocates for a balanced and informed approach, emphasizing that the symbiotic relationship between Digital Twins and the Metaverse should steer the evolution of Smart Cities toward security, innovation, and interconnected urban spaces. © 2023 IEEE.

关键词： Digital Twins Immersive Experiences Metaverse Smart Cities Sustainable Urban Living Urban Development

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Mobile Edge computing for Multi-Services Digital Twin-Enabled IoT Heterogeneous Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1845-1853页

作者： Liu, Weiqi Hossain, Mohammad Arif Ansari, Nirwan Auburn University at Montgomery Department of Computer Science and Computer Information Systems MontgomeryAL36117 United States Middle Tennessee State University Department of Engineering Technology MurfreesboroTN37132 United States New Jersey Institute of Technology Advanced Networking Laboratory Department of Electrical and Computing Engineering NewarkNJ07102 United States

A scheme for edge computing-enabled offloading in a digital twin (DT) enabled heterogeneous network (HetNet) of multi-services IoT devices (IDs) is proposed. This scheme optimizes the association and handover of IDs, offloading ratio, and resource allocation considering the number of IDs, deadline requirements, and resource capacities. The objective is to enhance future generation networks by considering the ID movement, diverse ID requests, and network heterogeneity. We formulate the problem as Joint ID assOciatIon, offloadiNg ratio, Wireless bandwidth and computing reSource allocation, and digital twin placEment (JOINWISE), aiming to minimize the task completion time of all IDs while considering ID movement. Since JOINWISE is a mixed-integer nonlinear problem, we decompose it into two sub-problems: the ID Association (IDA) problem and the offloading Ratio, DT plAcement, bandwiDth and computing resource allOcation (RADIO) problem. IDA can be solved by mapping it to a multi-dimensional multiple knapsacks problem. Due to the non-convexity, high dimension of decision variables, and dynamic HetNet environment of RADIO, we propose a deep deterministic policy gradient (DDPG) based reinforcement learning method to iteratively solve the two sub-problems. Simulation results have confirmed the effectiveness of our proposed scheme in tackling the JOINWISE problem. © 2015 IEEE.

关键词： Mobile edge computing

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An effective object detection via BS2ResNet and LTK-Bi-LSTM

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Multimedia Tools and Applications 2025年 1-20页

作者： V, Arulalan V, Premanand Kumar, Dhananjay Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu District Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering Vellore Institute Technology Chennai Campus Tamil Nadu Chennai India Department of Information Technology Anna University MIT Campus Tamil Nadu Chennai India

Video analytics faces complex challenges in object detection and classification. Deep learning based approaches have achieved remarkable success in past decade. However, existing object identification models that utilize backbone’s core features still present challenges due to their lack of semantic information. To address these issues, a novel object detection and classification framework utilizing Batch normalization and Softswish activation adapted ResNet (BS2ResNet) and Logistic Tanh Kaiming Bi-directional Long Short Term Memory (LTK-Bi-LSTM) techniques was proposed. The framework employs frame conversion, noise removal, and contrast elevation during frame pre-processing, followed by background subtraction using the Supreme Distance-centered Fuzzy C-Means (SD-FCM) clustering algorithm, and edge detection using the Hyperbolic Tangent Kernel Canny Edge Detector (HTKCED). BS2ResNet is then used for object detection, and features are extracted and passed to the LTK-Bi-LSTM neural network for object classification. The proposed system was found to improve object detection and classification accuracy, outperforming existing techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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Estimating Risk-aware Flexibility Areas for Electric Vehicle Charging Pools via AC Stochastic Optimal Power Flow

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Journal of Modern Power Systems and Clean Energy 2023年第4期11卷 1247-1256页

作者： Juan S.Giraldo Nataly Bañol Arias Pedro P.Vergara Maria Vlasiou Gerwin Hoogsteen Johann L.Hurink Energy Transition Studies Group Netherlands Organisation for Applied Scientific Research(TNO)Amsterdam1043 NTThe Netherlands Department of Electrical Engineering Mathematics and Computer ScienceUniversity of TwenteEnschede7522 NBThe Netherlands Intelligent Electrical Power Grids Group Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft2628 CDThe Netherlands

This paper introduces an AC stochastic optimal power flow(SOPF)for the flexibility management of electric vehicle(EV)charging pools in distribution networks under *** AC SOPF considers discrete utility functions from charging pools as a compensation mechanism for eventual energy not served to their charging *** application of the AC SOPF is described where a distribution system operator(DSO)requires flexibility to each charging pool in a day-ahead time frame,minimizing the cost for flexibility while guaranteeing technical *** areas are defined for each charging pool and calculated as a function of a risk parameter involving the uncertainty of the *** show that all players can benefit from this approach,i.e.,the DSO obtains a riskaware solution,while charging pools/tasks perceive a reduction in the total energy payment due to flexibility services.

关键词： Electric vehicle flexibility management stochastic optimal power flow(SOPF) risk awareness compensation mechanism

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Improved BEST-MAC protocol for WSN using optimal cluster head selection

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International Journal of Information Technology (Singapore) 2023年第2期15卷 859-875页

作者： Kumar, Rakesh Gangwar, Manju Department of Computer Science and Engineering National Institute of Technical Teachers Training and Research Chandigarh India

Use of wireless technology increases rapidly in present days because of convenient use for its users. Wireless sensor network is one of the widely used wireless technologies. Although wireless sensor network has many advantages over other communication technologies, but still anguish from some limitations like limited battery power, control overheads, scalability, data aggregation, throughput etc. MAC layer is considered as most power consuming part over communication. Numerous MAC protocols had been designed to enhance the lifetime of wireless sensor network. In this paper, we have chosen a TDMA based MAC protocol for our proposed work. In our proposed work we try to improve existing work i.e. BEST-MAC protocol using artificial intelligence. We considered two performance parameters that are energy efficiency and throughput of wireless sensor network to improve the life span of WSN using optimal cluster head selection process. Our proposed work is implemented with MATLAB version R2012a and results are included in this paper. The results shows the energy is saved by 3.085% and throughput is improved by 51.26% in the proposed work as compared to BEST-MAC protocol. © 2019, Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Bitmap-assisted Cluster head Control overheads Energy efficiency MAC protocol Scalability Wireless sensor networks (WSN)

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Deep Learning Classification Techniques on Detecting Diabetic Retinopathy Dataset 7

Deep Learning Classification Techniques on Detecting Diabeti...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Revathi, B. Usharani, C. Kezial Elizabeth, S.K. Nagaraj, P. Nithya, D. Ramco Institute of Technology Department of Artificial Intelligence And Data Science Virudhunagar India Mangayarkarasi College of Engineering Department of Computer Science And Engineering Madurai India Kalasalingam Academy of Research And Education Department of Computer Science And Engineering Virudhunagar Krishnankoil India Theni Kammavar Sangam College of Technology Department of Computer Science And Engineering Theni India

ISBN: (纸本)9798350359299

Deep learning algorithms can summarize images to understand how to carry out necessary tasks. The purpose of this study is to compare several deep learning methods. Both experience-based and explanation-based learning are possible in deep learning. The most widely utilized algorithms, such as Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), Generative Adversarial Networks (GAN), Radial Basis Function Networks (RBFN), and Deep Belief Networks (DBN), and the Diabetic Retinopathy dataset is utilized in this study to evaluate the effectiveness of the algorithms. A comparative study of the classifiers reveals that CNN performs more accurately than the other approaches. © 2024 IEEE.

关键词： Classification (of information)

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Innovations in Agri-Tech: A Review of Artificial Intelligence Applications and Challenges in Modern Agriculture 2

Innovations in Agri-Tech: A Review of Artificial Intelligenc...

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2nd International Conference on Advanced computing and Communication Technologies, ICACCTech 2024

作者： Agarwal, Rupanshi Bhardwaj, Isha Sharma, Ashish Kumar Sanghi, Akash Agarwal, Gaurav Invertis University Department of Computer Science and Engineering Bareilly India Kothiwal Institute of Technology and Professional Studeis Department of Applied Science and Humanities Moradabad India Invertis University Department of Computer Applications Bareilly India

ISBN: (纸本)9798331519056

Artificial intelligence (AI) is driving a massive shift in the agriculture industry, which is essential to both global food security and economic stability. This review article explores the use of AI technologies in agriculture, outlining their current and potential future applications while charting their evolution. We examine several AI applications such as computer vision, machine learning, robots and the (IoT) Internet of Things, highlighting their contributions to increasing efficiency, streamlining resource allocation, and strengthening decision-making. Machine learning techniques are utilized for predictive analytics in disease identification and crop yield estimation, while computer vision supports plant health monitoring and harvesting automation. Planting, weeding, and harvesting chores are revolutionized by robotics, and data collecting for precision agriculture is made easier by IoT devices in conjunction with sensors. Additionally, by offering indepth analyses of the soil and crops, drones and satellite imagery are essential to precision farming. The technological, financial, and social barriers to the broad adoption of AI are also covered in this study. These barriers include poor data quality, expensive implementation, and moral dilemmas with regard to labour displacement and environmental effects. In this paper, we propose that the future of global food security and sustainability depends on the strategic application of AI in agriculture. In order to fully realize the potential of artificial intelligence in agriculture Strategic implementation will determine the sustain ability and future of global food security. In this order, this study attempts to direct future research and policy development by offering a thorough overview of existing technology, applications, and difficulties. © 2024 IEEE.

关键词： Smart agriculture

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Seer: Accelerating Block chain Transaction Execution by Fine-Grained Branch Prediction 51st

Seer: Accelerating Block chain Transaction Execution by Fine...

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51st International Conference on Very Large Data Bases, VLDB 2025

作者： Zhang, Shijie Cheng, Ru Liu, Xinpeng Xiao, Jiang Jin, Hai Li, Bo National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Increasingly popular decentralized applications (dApps) with complex application logic incur significant overhead for executing smart contract transactions, which greatly limits public block chain performance. Pre-executing transactions off the critical path can mitigate substantial I/O and computation costs during execution. However, pre-execution does not yield any state transitions, rendering the system state inconsistent with actual execution. This inconsistency can lead to deviations in pre-execution paths when processing smart contracts with multiple state-related branches, thus diminishing pre-execution effectiveness. In this paper, we develop Seer, a novel public block chain execution engine that incorporates fine-grained branch prediction to fully exploit pre-execution effectiveness. Seerpredicts state-related branches using a two-level prediction approach, reducing inconsistent execution paths more efficiently than executing all possible branches. To enable effective reuse of pre execution results, Seer employs checkpoint-based fast-path execution, enhancing transaction execution for both successful and unsuccessful predictions. Evaluations with realistic block chain workloads demonstrate that Seer delivers an average of 27.7× transaction-level speedup and an overall 20.6× speedup in the execution phase over vanilla Ethereum, outperforming existing block chain execution acceleration solutions. © 2025, VLDB Endowment, All rights reserved.

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