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A Review of Smart Contract Blockchain Based on Multi-Criteria Analysis:Challenges and Motivations

computers, Materials & Continua 2023年第5期75卷 2833-2858页

作者： Norah M.Alshahrani M.L.Mat Kiah B.B.Zaidan A.H.Alamoodi Abdu Saif Department of Computer System&Technology Faculty of Computer Science&ITUniversity of MalayaLembah Pantai50603Kuala LumpurMalaysia Department of Computer Science Applied CollageKing Khalid UniversityP.O.Box 62569AbhaSaudi Arabia Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlin64002Taiwan Department of Computing Faculty of ArtsComputing and Creative Industry(FSKIK)Universiti Pendidikan Sultan IdrisTanjung MalimMalaysia Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia

A smart contract is a digital program of transaction protocol(rules of contract)based on the consensus architecture of *** contracts with Blockchain are modern technologies that have gained enormous attention in scientific and practical applications.A smart contract is the central aspect of a blockchain that facilitates blockchain as a platformoutside the cryptocurrency *** development of blockchain technology,with a focus on smart contracts,has advanced significantly in recent ***,research on the smart contract idea has weaknesses in the implementation sectors based on a decentralized network that shares an identical *** paper extensively reviews smart contracts based on multi-criteria analysis,challenges and ***,implementing blockchain in multicriteria research is required to increase the efficiency of interaction between users via supporting information exchange with high *** blockchain in the multi-criteria analysis is necessary to increase the efficiency of interaction between users via supporting information exchange and with high confidence,detecting malfunctioning,helping users with performance issues,reaching a consensus,deploying distributed solutions and allocating plans,tasks and joint *** smart contract with decision-making performance,planning and execution improves the implementation based on efficiency,sustainability and ***,the uncertainty and supply chain performance lead to improved users’confidence in offering new solutions in exchange for problems in smart *** includes code analysis and performance,while development performance can be under development.

关键词： Blockchain smart contract MCDM MADM

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Mitigation for cloud computing security risks and governance

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International Journal of Cloud Computing 2022年第5-6期11卷 560-567页

作者： Jabez, J. Narmadha, R. Porkodi, S. Devi, L. School of Computing Sathyabama Institute of Science and Technology Chennai India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai India College of Engineering and Computer Science Lebanese French University Erbil Iraq PGCS Department Muthayammal College of Arts and Science Rasipuram India

The recent prosperous of cloud computing has become the biggest revenue for originalities with the issues of intricate servers that need regular maintenance, security, power and cooling systems throughout. Hence, it is extremely significant for any cloud service earner is to offer dense security models and policies to all users involves. Although creating best suited security strategies might necessitate enormous amount of reserve and endurance, it will bounce the casual for end user to relay on a computerised security system throughout. The cloud environs supplementary strongly secure, qualifying security hazards, appropriate controls must be imposed. Major contest is to consider cloud security and governance explanation together. In this segment, the emphasis will be in the contests involved in preserving a dependable security policy computerisation in at the application layer, network layer and host level of cloud computing. Moreover, securities related to models will be discussed to recruit for those security applications which are more composite and active. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Security systems

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Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

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IEEE Transactions on Circuits and Systems for Video technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

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Carrying Out CNN Channel Pruning in a White Box

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IEEE Transactions on Neural Networks and Learning Systems 2023年第10期34卷 7946-7955页

作者： Zhang, Yuxin Lin, Mingbao Lin, Chia-Wen Chen, Jie Wu, Yongjian Tian, Yonghong Ji, Rongrong Xiamen University Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen361005 China Youtu Laboratory Tencent Shanghai200233 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu30013 Taiwan Peng Cheng Laboratory Shenzhen518000 China Peking University Department of Computer Science and Technology Beijing100871 China School of Informatics and the Institute of Artificial Intelligence Xiamen University Media Analytics and Computing Laboratory Department of Artificial Intelligence Xiamen361005 China

Channel pruning has been long studied to compress convolutional neural networks (CNNs), which significantly reduces the overall computation. Prior works implement channel pruning in an unexplainable manner, which tends to reduce the final classification errors while failing to consider the internal influence of each channel. In this article, we conduct channel pruning in a white box. Through deep visualization of feature maps activated by different channels, we observe that different channels have a varying contribution to different categories in image classification. Inspired by this, we choose to preserve channels contributing to most categories. Specifically, to model the contribution of each channel to differentiating categories, we develop a class-wise mask for each channel, implemented in a dynamic training manner with respect to the input image's category. On the basis of the learned class-wise mask, we perform a global voting mechanism to remove channels with less category discrimination. Lastly, a fine-tuning process is conducted to recover the performance of the pruned model. To our best knowledge, it is the first time that CNN interpretability theory is considered to guide channel pruning. Extensive experiments on representative image classification tasks demonstrate the superiority of our White-Box over many state-of-the-arts (SOTAs). For instance, on CIFAR-10, it reduces 65.23% floating point operations per seconds (FLOPs) with even 0.62% accuracy improvement for ResNet-110. On ILSVRC-2012, White-Box achieves a 45.6% FLOP reduction with only a small loss of 0.83% in the top-1 accuracy for ResNet-50. Code is available at https://***/zyxxμWhite-Box. © 2012 IEEE.

关键词： Visualization

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Reinforcement Learning Driven Smart Charging Algorithms to Enhance Battery Lifespan and Grid Sustainability

Reinforcement Learning Driven Smart Charging Algorithms to E...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Sivakumar, S. Yadav, Mali Ram Bharathi, A. Akila, D. Dineshkumar, P. Banupriya, V. Vel Tech Rangarajan Dr.Sagunthala R And D Institute Of Science And Technology Department Of Electrical And Electronics Engineering Chennai India Lovely Professional University Department Of Physical Education Punjab India Vels Institute Of Science Technology And Advanced Studies Department Of Information Technology Pallavaram India Saveetha College Of Liberal Arts And Sciences Simats Department Of Computer Applications Chennai India Sona College Of Technology Department Of Information Technology Tamil Nadu Salem India M. Kumarasamy College Of Engineering Department Of Computer Science And Business Systems Karur India

ISBN: (纸本)9798350371567

This study investigates the use of RL-driven smart charging algorithms, particularly the Deep Deterministic Policy Gradients (DDPG) model, to maximise grid sustainability and battery lifetime. The current state of this field's study is lacking in real-time adaptation to different settings and in handling the ever-changing nature of charging profiles. By comparison, the suggested approach demonstrates a significant efficiency boost through the use of RL principles, which allow the model to learn and adjust its charging tactics. In specifically, the strategy contributes to a 10% improvement in total grid efficiency while cutting peak demand by 25%, minimising charge-discharge cycles by 20%, and reducing battery degradation rates by 15%. Not only do these measurements show that the suggested method works, but they also show how unique it is in offering a smart charging solution that is both sensitive and adaptive. This research fills in the gaps left by other studies and proposes an optimisation technique that is more dynamic and works in real-time. Its influence on the health of individual batteries and the sustainability of the grid as a whole is noteworthy. © 2024 IEEE.

关键词： Reinforcement learning

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An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications

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computers, Materials & Continua 2022年第1期70卷 1315-1334页

作者： Naglaa F.Soliman Naglaa S.Ali Mahmoud I.Aly Abeer D.Algarni Walid El-Shafai Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt International Academy for Engineering and Media Science October CityEgypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Breast cancer is themost common type of cancer,and it is the reason for cancer death toll in women in recent *** diagnosis is essential to handle breast cancer patients for treatment at the right *** with mammography is the preferred examination for breast cancer,as it is available worldwide and ***-Aided Detection(CAD)systems are used to analyze medical images to detect breast cancer,*** death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after *** of mammogram images has four main steps:pre-processing,segmentation of the region of interest,feature extraction and classification of the images into normal or abnormal *** paper presents an efficient framework for processing of mammogram images and introduces an algorithm for segmentation of the images to detect *** pre-processing step of mammogram images includes removal of digitization noise using a 2D median filter,removal of artifacts using morphological operations,and contrast enhancement using a fuzzy enhancement *** proposed fuzzy image enhancement technique is analyzed and compared with conventional techniques based on an Enhancement Measure(EME)and local contrast *** comparison shows an outstanding performance of the proposed technique from the visual and numerical *** segmentation process is performed using Otsu’smultiple thresholding *** method segments the image regions into five classes with variable intensities using four *** effectiveness is measured based on visual quality of the segmentation output,as it gives details about the image and positions of *** performance of the proposed framework is measured using Dice coefficient,Hausdorff,and Peak Signal-to-Noise Ratio(PSNR)*** segmented tumor region with the proposed segmentation method is 81%of the ground truth region provided by an ***,the proposed framew

关键词： Breast cancer mammogram images CAD contrast enhancement fuzzy logic segmentation

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Health Care DNS Tunnelling Detection Method via Spiking Neural Network 3rd

Health Care DNS Tunnelling Detection Method via Spiking Neur...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Kumar, Narendra Surendiran, R. Samuel, G. K. Jabash Bhavana, N. Shirgire, Anil Malar, A. Jasmine Gnana Qalid, Aznul Faculty of Science and Technology NIET NIMS University Rajasthan Jaipur303121 India School of Information Science Annai College of Arts and Science Tamilnadu Kumbakonam612503 India Department of Electrical and Electronics Engineering Rohini College of Engineering and Technology Kanyakumari India School of Computing and Information Technology REVA University Karnataka Bengaluru560064 India Department of Civil Engineering Dr D Y Patil Institute of Technology Maharashtra Pimpri Pune411018 India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Artificial Intelligence Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

ISBN: (纸本)9789819986453

A common DNS exploit that has been used for several decades to steal data is DNS tunneling. To avoid being detected as an intrusion from healthcare domain, the stolen health information is encoded and contained within DNS requests. Machine learning techniques for detection frequently make use of characteristics like DNS activity and healthcare network activity. The majority of features, such as time–frequency-related information, can only be obtained when data exfiltration occurs. Identifying a malicious query from a single DNS suggestion is key to preventing data exfiltration based on DNS tunneling. We can perceive DNS tunnelling before healthcare data exfiltration by not using internet traffic properties or DNS behavior information. In this paper, we describe a detection method based on deep learning that uses DNS query payloads as predictive variables in the models. Using the Spiking Neural network (SNN) in healthcare DNS Tunnelling Detection Method a DNS Tunnelling attack or not? Specificity, sensitivity, f1 score, accuracy, and Matthew's correlation coefficient (MCC) are the factors taken into account when evaluating the efficiency of the suggested model. In comparison to the Proposed Spiking neural network, the Existing Deep Neural Network (DNN), Mobile Net, and Recurrent Neural Network (RNN) technique improves the performance of a DNS tunneling detection method in a real-world network system. Results indicate that the suggested strategy is far more dependable than existing methods and achieves a 99.84% accuracy rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Recurrent neural networks

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The Goldilocks Framework: Towards Selecting the Optimal Approach to Conducting AI Projects 22

The Goldilocks Framework: Towards Selecting the Optimal Appr...

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1st International Conference on AI engineering - Software engineering for AI, CAIN 2022

作者： Dzhusupova, Rimman Bosch, Jan Olsson, Helena Holmstrom Control & Safety Systems McDermott Dept. of Electrical & Instrumentation The Hague Netherlands Chalmers University of Technology Department of Computer Science and Engineering Gothenburg Sweden Malmö University Department of Computer Science and Media Technology Malmö Sweden

ISBN: (纸本)9781450392754

Artificial intelligence is increasingly becoming important to businesses since many companies have realized the benefits of applying Machine Learning (ML) and Deep Learning (DL) into their operations. Nevertheless, ML/DL technologies' industrial development and deployment examples are still rare and generally confined within a small cluster of large international companies who are struggling to apply ML more broadly and deploy their use cases at a large scale. Meanwhile, current AI market has started offering various solutions and services. Thus, organizations must understand how to acquire AI technology based on their business strategy and available resources. This paper discusses the industrial experience of developing and deploying ML/DL use cases to support organizations in their transformation towards AI. We identify how various factors, like cost, schedule, and intellectual property, can be affected by the choice of approach towards ML/DL project development and deployment within large international engineering corporations. As a research result, we present a framework that covers the trade-offs between those various factors and can support engineering companies to choose the best approach based on their long-term business strategies and, therefore, would help to accomplish their ML/DL project deployment successfully. © 2022 ACM.

关键词： Commerce

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Optical Data Center Implementation within Data Center Networks Context Based on Mininet 2

Optical Data Center Implementation within Data Center Networ...

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2nd International engineering Conference on electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Abdulmahdi, Esraa Ali Ali, Adnan Hussein Ibrahim, Raed Khalid Al-Askery, Ali Alhamdane, Hayder Jasim Mahmood, Amal Ibrahim Electrical Engineering College Middle Technical University Student at Dept. of Computer Engineering Techniques Baghdad Iraq Institute of Technology Middle Technical University Electrical Techniques Department Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad Iraq Electrical Engineering College Middle Technical University Department of Control and Automation Technical Engineering Baghdad Iraq College of Applied Arts Middle Technical University Baghdad Iraq Electrical Engineering College Middle Technical University Dept. of Computer Engineering Techniques Baghdad Iraq

ISBN: (纸本)9798350373363

The technology of reconfigurable optical networks (RONs) is growing to be a promising solution to effectively cater to the rapidly increasing traffic generated by the digital society. Optical technology emerges as a viable alternative because to its capacity to offer a substantial bandwidth, little latency, and enhanced overall performance. Nevertheless, it is imperative to effectively manage the physical resources comprising a data center. This study provides an experimental demonstration of the RONs, which is enhanced by a control plane. The implementation relies on the monitoring of real-time network statistics. This enables the execution of various activities, including as network slice provisioning and reconfiguration, assignment of packet priority classes, and dynamic operations. These actions are performed to ensure the attainment of the desired Quality of Service level. The experimental results show the successfully implementation of RONs using Mininet Emulator and sFlow traffic monitoring technology. The results approve that optical datacenter has minimum delay with low OSNR even with long distance and fast switching time. © 2023 IEEE.

关键词： Optical switches

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Cloud Computing Based POC Diagnostic Device: Rapid Infectious Disease Testing by Data Analytics 1

Cloud Computing Based POC Diagnostic Device: Rapid Infectiou...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Basha, H. Anwer Ramalingam, L. Hasan, Md Ashfaqul Ananda, M.H. Velmurugan, S. Maniraj, S.P. Saveetha College of Liberal Arts & Sciences Simats University Department of Computer Science Tamil Nadu Chennai India Loyola Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Reva University School of Electrical and Electronics Engineering Karnataka Bangalore India Tjs Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350330915

To meet the growing need for affordable, efficient, and accurate diagnostic tools, the system has developed a Raspberry Pi-based Point-of-Care (POC) gadget for speedy and reliable detection of infectious illnesses. This groundbreaking gadget, built on the flexible and powerful Raspberry Pi computer, ushers in a new era of state-of-the-art POC diagnostics by combining molecular and image analysis methods. Complex testing for pathogens like COVID-19 and malaria may be carried out using this state-of-the-art system, which integrates a camera module and custom biosensors. The device's software parses sensor data, improves diagnostic precision using machine learning techniques, and presents a straightforward interface for clinicians and patients alike. This study takes a holistic approach by considering all relevant factors, including adherence to regulations, user education, upkeep, and data management. Our POC diagnostic gadget driven by a Raspberry Pi provides a low-cost, high-throughput method for diagnosing illness, which may have far-reaching implications for the future of healthcare delivery in both under-resourced and well-endowed regions. © 2023 IEEE.

关键词： Healthcare Infectious Disease Internet of Things, Cloud Computing Point-of-Care Diagnostics

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