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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Sharma, Amit Kumar Mehra, Ritika Kumar, Brijesh Balaji, N.V. Kumar, K. Sriram Gaikwad, V.S. Tmu Department of Physics India School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand 248007 India School of Commerece and Management Iimt University Meerut India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Karpagam Institute of Technology Department of Information Technology Coimbatore641105 India Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350357769

Using a magnetoelectric spin-orbit circuit, this study suggests a revolutionary method for developing scale and cost-effective computer. Despite semiconductors miniaturization, the leading technology of today, CMOS, has basically not evolved in terms of operational ideas throughout the years. The research investigates alternate dimensions expanded logic strategies to boost the performance of numerical control schemes and allow for the growth of novel methods like machine learning. The proposed optical logic device links the spinning energy of the electron with its mobility through spin-orbit communication and magnetism property toggle. The gadget has collaboration and senses due to the incorporation of cutting-edge linked chemicals and quasi constituents. In the areas of shifting energy, voltage, and computation efficacy, the study shows that this novel technique beats the technology known as CMOS. Additionally, it allows for minimum idle power usage and the possibility of multigenerational computation. © 2023 IEEE.

关键词： Carrier transfer CMOS technology Electricity generation Energy usage Energy-efficient computation Ferroelectricity switching Interfacial and workload component control Logic complexity Logic efficiency Microprocessor innovation Scaling Semiconductor technology Spin-flux conversion Spin-orbit transmission Strain

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Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

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computers, Materials & Continua 2023年第5期75卷 4485-4499页

作者： Fatma Taher Hamoud Alshammari Lobna Osman Mohamed Elhoseny Abdulaziz Shehab Eman Elayat College of Technological Innovation Zayed UniversityDubaiUAE Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Electronics and Communications Engineering Delta Higher Institute for Engineering&TechnologyMansouraEgypt College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates Department of Information Systems Mansoura UniversityMansoura35516Egypt Department of Teacher Preparation Faculty of Specific EducationMansoura UniversityMansouraEgypt

Cardiac diseases are one of the greatest global health *** to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent *** article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia *** fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection *** ensemble of classifiers is then applied to the fusion’s *** proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of ***,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal ***,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized *** preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection *** short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive *** proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art *** achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.

关键词： Cardiac arrhythmia preprocessing missing values classification model fusion

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MedX-Net: Hierarchical Transformer with Large Kernel Convolutions for 3D Medical Image Segmentation

MedX-Net: Hierarchical Transformer with Large Kernel Convolu...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Lin Zou, Qingzhi Key Laboratory of Computing Power Network and Information Se-curity Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Due to the exceptional performance of Transform-ers in 2D medical image segmentation, recent work has also introduced them into 3D medical segmentation tasks. For instance, Swin UNETR and other hierarchical Transformers have reintroduced prior knowledge from several convolutional networks, further enhancing the volume segmentation capabilities of models. The efficacy of these hybrid methodologies is primarily attributed to the substantial quantity of parameters and the nonlocal self-attention mechanism with a large receptive field. We argue that the behavior of these methods' large receptive fields can be simulated by employing fewer parameters through the utilization of depth-wise convolutions with large kernel. Within this manuscript, we introduce a lightweight volume segmentation model called MedX-Net, which uses convolutional network modules to simulate hierarchical Transformers for robust volume segmentation. Firstly, inspired by the hierarchical Transformer module of Swin UNETR, we investigate large-kernel depth-wise convolutions with different sizes to achieve a reduced model parameter count while maintaining a large global receptive field. Secondly, we replace the multilayer perceptron (MLP) in the hierarchical Transformer module with Inverted Bottleneck with Depthwise Convolution Enhancement(DWCE) to improve model performance with fewer activation and normalization layers, further reducing the parameter count. We validate the effectiveness and efficiency of our model for volume segmentation on three public datasets: Synapse, BTCV and ACDC. On the Synapse dataset, compared to Swin UNETR, our model achieves an improvement from 83.48% to 87.21% in Dice score. Compared to the result of 86.57% achieved by nnFormer, our model achieves superior performance while reducing the model parameter count by 64%. © 2024 IEEE.

关键词： Image segmentation

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Solution to Coordination of Transmission and Distribution for Renewable Energy Integration into Power Grids:An Integrated Flexibility Market

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CSEE Journal of Power and Energy Systems 2023年第2期9卷 444-458页

作者： Tao Jiang Zhongwei Shen Xiaolong Jin Rufeng Zhang Alessandra Parisio Xue Li Xiao Kou Department of Electrical Engineering Northeast Electric Power UniversityJilin 132012China Key Laboratory of Smart Grid of Ministry of Education Key Laboratory of Smart Energy&Information Technology of Tianjin MunicipalityTianjin UniversityTianjin 300072China Department of Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN37996USA.

Nowadays,transmission system operators(TSOs)encounter the challenges of securely operating power grids with high penetration of *** this context,more flexibility is needed than ever to maintain system *** rapid development of coordinated transmission and distribution technology recently,active distribution networks(ADNs)which have abundant flexible resources,have the potential to provide flexibility to the ***,an integrated transmission-distribution flexibility market is proposed in this *** the proposed framework,the energy market is first cleared to obtain baseline generation *** the baseline generation schedules are treated as inputs to the flexibility ***,the integrated flexibility market is cleared to facilitate flexibility trading between transmission and distribution *** preserve the data privacy of different market participants,an alternating direction method of multipliers(ADMM)based method is utilized to clear the markets in a distributed *** the energy market and flexibility market-clearing model,transmission network market clearing models are linear programming(LP)models,and distribution network market clearing models are second order cone programming(SOCP)*** the proposed method,only limited information is exchanged between TSO and *** studies are conducted on a revised IEEE 30-bus transmission network with two 33-node *** results demonstrate the proposed flexibility market framework can enhance competitiveness of generation companies in the transmission network and distributed generators(DGs)in the ***,flexibility purchasing cost is reduced by 17.7%compared to traditional flexibility supplied by generators according to the case study results,and ADNs can gain additional flexibility profit by providing flexibility.

关键词： ADMM ADN energy market flexibility market TSO-DSO coordination

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Animal Intrusion Detection using Deep Learning for Agricultural Fields 5

Animal Intrusion Detection using Deep Learning for Agricultu...

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5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Thomas, Aby K. Poovizhi, P. Saravanan, M. Tharageswari, K. Alliance University Alliance College of Engineering and Design Department of Electronics and Communication Engineering Bangalore India Dr.N.G.P. Institute of Technology Department of Information Technology Coimbatore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665474672

Crop yield is severely threatened by animal invasion, which has consequences for both food security and farmer income. This suggested model details how this issue might be addressed via the integration of IoT and ML methods. As image processing and Internet of Things (IoT) sensor monitoring network technologies have advanced, sensors have undergone radical changes. Animal-human conflict is a serious issue in the agricultural sector and the forest zone, posing a threat to human life and causing significant material loss. Wireless sensors can analyze video clips from a gathered dataset to create an animal incursion detection system. This study introduces Invariant Feature Extraction (FE) using SlowFast architecture. The videos are annotated first, and then the IFE model is used to extract the spatial data. Classifying animals based on their pictures, can keep better tabs on them. Accidents involving animals and vehicles may be avoided, animals can be traced, and theft can be avoided if detection and categorization methods are used. The efficient deep learning techniques are helpful for this. © 2023 IEEE.

关键词： Animals

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Identification of Anomaly Scenes in Videos Using Graph Neural Networks

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computers, Materials & Continua 2023年第3期74卷 5417-5430页

作者： Khalid Masood Mahmoud M.Al-Sakhnini Waqas Nawaz Tauqeer Faiz Abdul Salam Mohammad Hamza Kashif Department of Software Engineering Lahore Garrison UniversityLahore54000Pakistan School of Business Skyline University CollegeSharjahUAE Al-Madinah International University–Faculty of Computer and Information Technology Kuala LumpurMalaysia College of Computer and Information Systems Islamic University of MadinahMadinahKingdom of Saudi Arabia School of IT Skyline University CollegeSharjahUAE Faculty of Engineering Computing and Science(FECS)-Swinburne University of TechnologySarawak Department of General Education Skyline University CollegeUniversity City of SharjahUAE Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan

Generally,conventional methods for anomaly detection rely on clustering,proximity,or *** themassive growth in surveillance videos,outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques *** research explores the structure of Graph neural networks(GNNs)that generalize deep learning frameworks to graph-structured *** node in the graph structure is labeled and anomalies,represented by unlabeled nodes,are predicted by performing random walks on the node-based graph *** to their strong learning abilities,GNNs gained popularity in various domains such as natural language processing,social network analytics and *** detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of *** Graph-based deep learning networks are designed to predict unknown objects and *** our case,they detect unusual objects in the form of malicious *** edges between nodes represent a relationship of nodes among each *** case of anomaly,such as the bike rider in Pedestrians data,the rider node has a negative value for the edge and it is identified as an *** encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible *** show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities,which shows a huge potential in automatically monitoring surveillance *** autonomous monitoring of CCTV,crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public *** suggested GNN model improves accuracy by 4%for the Pedestrian 2 dataset and 12%for the Pedestrian 1 dataset compared to a few state-of the-art techniq

关键词： Graph neural network deep learning anomaly detection auto encoders

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Securing Multi-Tenant Cloud Environments with Fully Homomorphically Encrypted Secure Multiparty Computation

Securing Multi-Tenant Cloud Environments with Fully Homomorp...

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2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Jagannathan, J. R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India

ISBN: (纸本)9798350388602

The aim of this work is to examine the application of FHE-SMPC as a viable security model for protecting the tenant isolation in multi-tenant cloud model. In multi-tenant environments, where various customers use the same cloud system, controlling the data leakage and protecting privacy is a major issue. Actual safeguards fail to meet the needs of data protection during processing, therefore, the introduction of highly elaborate encryption processes must happen. This research work focuses on the use of FHE technique and SMPC such that data can undergo processing while the plaintext remains unknown to any party. A kind of approach is based on the acquisition of synthetic and real data sets, system-level architecture, and cryptographic algorithms using open-source libraries with cloud service providers. The results of such implementation indicate that despite the fact that the FHE-SMPC system has a considerable computational overhead in comparison with classical schemes it is very secure and protected against different types of cryptographic attacks. The problem of scalability is still an open issue, but the complex is quite efficient with various data types that is why it can be used in various cloud applications. This paper offers a pertinent solution for security of data that is housed in multitenant environment and will go along way in creating fertile ground for research and innovation in secure clouding. © 2024 IEEE.

关键词： Cloud security

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Reduced Carbon Foot Print Based Localized Energy Demand Supply Using Micro Grids 4

Reduced Carbon Foot Print Based Localized Energy Demand Supp...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Mariappan, Umasree Balakrishnan, D. Ponraj, Anitha Maheswari, S. Balaji, Rayeni Sri P.S.R. Engineering College Department of Information Technology Tamil Nadu Sivakasi626140 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnakoil626126 India Sri Vidya College of Engineering & Technology Department of Electronics and Communication Engineering Tamil Nadu Virudhunagar626005 India

ISBN: (纸本)9798350393354

Microgrid systems are the most used devices which can support the storage of a higher amount of energy with increased reliability and flexibility. In this study, a grid-connected microgrid (MG) integrated with solar photovoltaic (PV), wind turbine (WT), fuel cell (FC), and Battery Energy Storage System (BESS) is introduced as a test system for optimizing costs and battery charging/discharging strategies. The framework developed represents a multi-objective function along with constraints, suitable for addressing this stochastic multi-objective problem. The performance of these physics-based optimization techniques is compared based on their effectiveness in achieving cost savings and optimizing battery charging. Upon analysis, it was found that Black Hole Optimization (BHO) exhibited significant cost savings overall, while Lightning Search Algorithm (LSA) proved more effective in optimizing battery charging strategies. This evaluation and comparison of different optimization techniques provide insights into their respective strengths and effectiveness when applied to the proposed MG test system. Specifically, BHO excelled in cost savings, whereas LSA showed superior performance in optimizing battery charging. These findings can guide researchers and practitioners in selecting appropriate optimization techniques based on the specific objectives and constraints of similar MG systems. © 2024 IEEE.

关键词： Stochastic systems

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Optimizing User Data Privacy and Confidentiality in Cloud Storage Systems Through Advanced Obfuscrypt Encryption Methods for Enhanced Security and Efficient Data Protection

Optimizing User Data Privacy and Confidentiality in Cloud St...

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2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Ramesh, P.S. Sudha, I. Jagannathan, J. Pandiarajan, Rajkumar Ponmalar, A. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Kalasalingam Academy Of Research and Education School of Computing Department of Information and Technology Krishnankoil Virudhunagar India R.M.K. Engineering College Tamilnadu Chennai India

ISBN: (纸本)9798350388602

With a steady increase in the adoption of cloud storage systems, the client's data are at risk of being leaked. There are several methods of encrypting data which are quite useful but they lack some of the features of today's sophisticated threats. The methodology used in this research propose an encryption framework known as Obfuscrypt to improve the issues of data privacy as well as confidentiality of data stored in cloud storage systems. Obfuscrypt uses conventional encryption strategies incorporated with obfuscation and post-quantum cryptographic algorithms to provide a complex security plan immune to classic and quantum approaches. Some of the proposed other innovations include the decentralised key management with multi-factor authentication and RBAC-ABE as the fine-grained access control techniques. These features make it possible for only the authorized personnel to be in a position to be in a position of decrypting and retrieving the stored data thereby minimizing the possibility of the data being accessed by unauthorized people. The work also considers the issue of performance efficiency of the cloud storage systems. Similar to other symmetric encryption programs, Obfuscrypt uses light encryption algorithms and brings in to play the hardware acceleration so that it may infuse lesser computational overhead in the process of encryption and decryption. As can be clearly seen from the results, the actual work proves that the proposed framework not only offers a higher level of security but does not affect the system performance to a great extent. Also, the study addresses the post-quantum cryptographic algorithm for making the system resistant to an attack that can be posed by quantum computing systems. With the help of these improved algorithms, Obfuscrypt guarantees the permanent data security in the world of constantly emerging cyber threats. © 2024 IEEE.

关键词： Data obfuscation

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Source-Free Unsupervised Domain Adaptation with Sample Transport Learning

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Journal of computer Science & technology 2021年第3期36卷 606-616页

作者： Qing Tian Chuang Ma Feng-Yuan Zhang Shun Peng Hui Xue School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing 210044China School of Computer Science and Engineering Southeast UniversityNanjing 211189China

Unsupervised domain adaptation(UDA)has achieved great success in handling cross-domain machine learning *** typically benefits the model training of unlabeled target domain by leveraging knowledge from labeled source *** this purpose,the minimization of the marginal distribution divergence and conditional distribution divergence between the source and the target domain is widely adopted in existing ***,for the sake of privacy preservation,the source domain is usually not provided with training data but trained predictor(e.g.,classifier).This incurs the above studies infeasible because the marginal and conditional distributions of the source domain are *** this end,this article proposes a source-free UDA which jointly models domain adaptation and sample transport learning,namely Sample Transport Domain Adaptation(STDA).Specifically,STDA constructs the pseudo source domain according to the aggregated decision boundaries of multiple source classifiers made on the target ***,it refines the pseudo source domain by augmenting it through transporting those target samples with high confidence,and consequently generates labels for the target *** train the STDA model by performing domain adaptation with sample transport between the above steps in alternating manner,and eventually achieve knowledge adaptation to the target domain and attain confident labels for ***,evaluation results have validated effectiveness and superiority of the proposed method.

关键词： unsupervised domain adaptation domain shift sample transport pseudo source domain

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