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Resilience Week (RWS)

作者： Moyan Lyu Junaid Farooq Department of Electrical & Computer Engineering College of Engineering and Computer Science University of Michigan-Dearborn Dearborn MI USA

ISBN: (数字)9798350388985

ISBN: (纸本)9798350388992

The deployment of fifth-generation (5G) networks across various industry verticals is poised to transform communication and data exchange, promising unparalleled speed and capacity. However, the security concerns related to the widespread adoption of 5G, particularly in mission-critical sectors, present significant challenges. This article investigates the potential of a Zero Trust (ZT) security philosophy as a viable countermeasure to these concerns. It delves into the practicalities of implementing ZT principles within 5G networks, with a specific focus on harnessing AI/ML technologies for proactive security measures, dynamic policy adaptations, and advanced risk assessments. Further, the article underscores the importance of developing a tailored ZT maturity model for 5G networks. Furthermore, the paper outlines key future research directions aimed at improving the ZT maturity of 5G deployments, contributing to the safe and secure integration of 5G technology in various sectors.

关键词： Industries Adaptation models Philosophical considerations 5G mobile communication Refining Mission critical systems Transforms Zero Trust Risk management computer security

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Optimal Placement of Solar Power Plant by Using AVOA Algorithm

Optimal Placement of Solar Power Plant by Using AVOA Algorit...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Klington, A. George Karthikeyan, K. St. Mother Theresa Engineering College Department of Computer Science Engineering Tuticorin India St. Mother Theresa Engineering College Department of Electrical and Electronics Engineering Tuticorin India

ISBN: (纸本)9798331521394

Infrastructure development and its design are more complex because of its constraint on budget and efficiency. By remembering these difficulties, a modified electrical structure is necessary for the nation. The power system structure should not depend on the conventional system. This main criterion is being in our engineer's mind. But, strengthening loads and their necessaries do not accept them to sleep. To face complex structures and constraints-based infrastructure facilities, optimal places for renewable systems are necessary. Here, the optimal placement of solar power plant is taken as the primary concern and its related analysis is made. Even though many algorithms are coming from all over the world. The part of those algorithms is not analyzed in many environments. In this paper, an AVOA algorithm is used for our analysis. This part of our research makes us excited with its results. The IEEE 57 system is used for the analysis. © 2025 IEEE.

关键词： African vultures optimization algorithm CPF MATPOWER PSO PV cell voltage stability

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Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

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computers, Materials & Continua 2023年第6期75卷 5271-5286页

作者： Saeed Mohsen Sherif S.M.Ghoneim Mohammed S.Alzaidi Abdullah Alzahrani Ashraf Mohamed Ali Hassan Electronics and Communications Engineering Department Al-Madinah Higher Institute for Engineering and TechnologyGiza12947Egypt Department of Artificial Intelligence Engineering Faculty of Computer Science and EngineeringKing Salman International University(KSIU)South Sinai46511Egypt Department of Electrical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications Engineering October High Institute for Engineering and Technology6th of October CityGiza12596Egypt

Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)***,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic *** this perspective,an automated AI technique with a digital processing method can be used to improve these *** paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG *** classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and *** addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the ***,Hadamard coefficients of the EEG signals are obtained via the ***,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG ***,a k-fold cross-validation technique is applied to validate the performance of the proposed *** LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,*** computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,*** results show that the LSTM classifier provides better performance than SVM in the classification of EEG ***,the proposed classifiers provide high classification accuracy compared to previously published classifiers.

关键词： Electroencephalogram LSTM SVM fast Walsh-Hadamard transform seizure accuracy sensitivity specificity

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Bayesian Network Modeling and Prediction of Transitions Within the Homelessness System 48

Bayesian Network Modeling and Prediction of Transitions With...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Rahman, Khandker Sadia Zois, Daphney-Stavroula Chelmis, Charalampos University at Albany State University of New York Department of Computer Science AlbanyNY United States University at Albany State University of New York Department of Electrical and Computer Engineering AlbanyNY United States

ISBN: (纸本)9781728163277

Administrative data collected by homeless service providers offer a unique opportunity to understand how homeless individuals navigate the homeless system towards securing stable housing. However, the literature on predictive models in the context of homeless service provision has neglected the sequential nature of services that an individual receives over time. Our work addresses this gap by learning, from administrative data, a Bayesian network, which in turn can be used to accurately predict whether an individual will exit the system, or alternatively, the service she would be assigned to the next time she experiences homelessness. Experimental evaluation shows that the proposed approach outperforms prior art not only at predicting exit, but also the less frequent services (and thus more challenging to predict). © 2023 IEEE.

关键词： Bayesian networks

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SEU Reliability Assessment Framework for COTS Many-core Processors

SEU Reliability Assessment Framework for COTS Many-core Proc...

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2022 International Conference on Microelectronics, ICM 2022

作者： Dammak, Chifa Mohamed, Otmane Ait Boukadoum, Mounir Concordia University Department of Electrical and Computer Engineering MontrealQC Canada University of Quebec at Montreal Department of Computer Science MontrealQC Canada

ISBN: (纸本)9781665493246

The high level of performance intrinsic to many-core architectures has made them the obvious successor to single-core processors in scenarios that require high computation. However, the increase in compute units also magnifies the vulnerability to soft errors, which may lead to system failure. This work proposes a portable and scalable fault injection engine for open-source many-core processor architectures. The tool performs automated simulation-based fault injection campaigns on the RTL design to provide insight into the impact of soft errors on each processor core and to evaluate their propagation across cores. This platform was validated on the OpenPiton open-source many-core processor, with a reliability assessment performed to evaluate SEU impact on the targeted architecture. This study was made on a 2-core and a 3-core system with 6 classes of faults, but the approach is scalable to more cores and can be easily extended to cover more fault injection targets. Our results show that 38.5% and 51.1% of the injected faults have led to an erroneous output result of the target core and 13.7% and 2.5% to a system crash for 2-core and 3-core systems, respectively. © 2022 IEEE.

关键词： Reliability analysis

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Analysis of Discovering Fraud in Master Card Based on Bidirectional GRU and CNN Based Model

Analysis of Discovering Fraud in Master Card Based on Bidire...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Kakkar, Sushma Subha, B. Reddy, L. Chandra Sekhar Pal, Subharun Dimri, Sushil Chandra Nishant, Neerav Sharda School of Engineering and Technology Sharda University Department of Electrical Electronics and Communication Engineering Greater Noida India Kristu Jayanti College Department of Management Bangalore India Telangana Medchal India Indian Institute of Technology Jammu Department of Computer Science and Engineering Jammu Jammu & Kashmir Jagti India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India School of Engineering Babu Banarasi das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350300857

Detecting and confirming MasterCard fraud (also known as 'master card fraud detection') is a popular course topic amongst students due to the usefulness of data processing and machine learning strategies in combating cybercrime. The three main parts of the proposed method are preprocessing, feature extraction, and model training. Preprocessing techniques are used to mine crucial information for identifying MasterCard fraud. Only by using the most advanced and efficient algorithms, the future transactions can be predicted with any degree of certainty. By employing these techniques, the proposed approach may confirm a variety of facts, including the correctness of authentication and the regularity of dealings. To accomplish feature extraction, the proposed approach used the PCA and SKM algorithms. In order to train the models following feature extraction, the proposed approach uses CNN, BiGRU, and BiGRU-CNN. Both the CNN and the BiGRU models are outperformed by the proposed model. With an approximate 98.87 percent accuracy, the proposed method is superior to other methods such as CNN and BiGRU. © 2023 IEEE.

关键词： Feature extraction

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Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1105-1121页

作者： Yue Zhou Xin Luo MengChu Zhou the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China the School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

关键词： Big data analysis cryptocurrency transaction network embedding(CTNE) dynamic network network embedding network representation static network

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The Application of a Synonyms Dictionary Using Bi-LSTM in Bangla for Better Approximation 2

The Application of a Synonyms Dictionary Using Bi-LSTM in Ba...

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2nd International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2023

作者： Hossain, M Iffat Saha, Utsha Billa, Arefa Software Engineer BJIT Ltd. Dhaka Bangladesh North Dakota State University Department of Computer Science Fargo United States Florida State University Department of Electrical and Computer Engineering Tallahassee United States

ISBN: (纸本)9798350358667

With the increasing internet usage, the volume of computerized data in Bangla is also growing exponentially. This vast repository of unstructured web data has various potential applications in Natural Language Processing (NLP). However, research in Bangla NLP faces significant challenges, including language complexity and limited availability of tools and datasets. These factors result in a lower accuracy rate when traditional methods used for English NLP are applied to Bangla. Specifically, the Bangla language has a rich lexicon of root words and characters, leading to an increase in out-of-vocabulary words when dealing with unseen texts. This paper proposes a novel method that employs a synonym dictionary to mitigate this problem. By converting unseen words into their synonyms that are recognized by the model, the method significantly reduces out-of-vocabulary errors. Our approach achieved an accuracy rate of 97.4%, compared to 95.3% without using the synonym dictionary, and had a loss value of 0.96. This methodology holds promise as an effective tool for solving classification problems in Bangla NLP. © 2023 IEEE.

关键词： Long short-term memory

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Glacier: Glass-Box Transformer for Interpretable Dynamic Neuroimaging 48

Glacier: Glass-Box Transformer for Interpretable Dynamic Neu...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Mahmood, Usman Fu, Zening Calhoun, Vince Plis, Sergey Emory University AtlantaGA United States Georgia State University Department of Computer Science AtlantaGA United States Georgia Institute of Technology Department of Electrical and Computer Engineering AtlantaGA United States

ISBN: (纸本)9781728163277

Deep learning models can perform as well or better than humans in many tasks, especially vision related. Almost exclusively, these models are used to perform classification or prediction. However, deep learning models are usually of black-box nature, and it is often difficult to interpret the model or the features. The lack of interpretability causes a restrain from applying deep learning to fields such as neuroimaging, where the results must be transparent, and interpretable. Therefore, we present a 'glass-box' deep learning model and apply it to the field of neuroimaging. Our model mixes spatial and temporal dimensions in succession to estimate dynamic connectivity between the brain's intrinsic networks. The interpretable connectivity matrices produced by our model result in beating state-of-the-art models on many tasks using multiple functional MRI datasets. More importantly, our model estimates task-based flexible connectivity matrices, unlike static methods such as Pearson's correlation coefficients. © 2023 IEEE.

关键词： Neuroimaging

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A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

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Frontiers of Structural and Civil engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC Duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data

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