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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Kumar, Chennupati Charan Parthipan, V. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Big Data and Network Security Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

With regard to the objective of the entire study, the goal is to improve the accuracy of predicting stock price using Lasso Regression in comparison to Random Forest Regression. The Novel K-Nearest Neighbors and Support Vector Machine Algorithm are the two groups that are the emphasis of this research activity. Each of these groups is carried out ten times. For the purpose of calculating samples, the Clincal software was utilized. The alpha value was determined to be 0.05, and the confidence interval was set at 95%. We took into consideration the stock price prediction dataset that was available on *** and had a size of 12617. With a value of p=0.000 (Independent sample t-test p © 2024 IEEE.

关键词： Support vector regression

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Performance Analysis for Prediction of Trending Footwear Classification using DensNet compared with Logistic Regression model 2

Performance Analysis for Prediction of Trending Footwear Cla...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Giridhar, E.V. Parthipan, V. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Big Data and Network Security Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

The purpose of this work is to investigate the effectiveness of DensNet and Logistic Regression in terms of accurately predicting the classification of footwear *** this study, there are two groups: the Novel DensNet model and the Logistic Regression model. Each group is being iterated for a sample size of ten, which is being generated with the ClinCalc software. The alpha value is set at 0.05, and the confidence interval is set at 95%. We took into consideration the footwear prediction dataset that was available on *** and had a size of 15854. The statistical analysis reveals a significant difference between the DensNet algorithm and the Logistic Regression model, with a value of p=0.001 (independent sample t-test p © 2024 IEEE.

关键词： Logistic regression

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Prediction Of Student Education Performance Using Googlenet Comparison With Over Alexnet Algorithm 2

Prediction Of Student Education Performance Using Googlenet ...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Krishna, Kotte. Venkata Rama Parthipan, V. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Big Data and Network Security Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

An examination of the degree to which GoogleNet and Alexnet are accurate in predicting the academic success of students is the primary objective of this study. Each of the two groups that make up the program contains ten samples, and there are two groups altogether. For the purpose of determining the sample size, a threshold of 0.05%, a G-power pretest of 80%, and a confidence interval of 95% are utilized. We took into consideration the student education dataset that was available on *** and had a size of 66500. The method that was proposed was found to have an accuracy of 94.63% when compared to the existing system, which has an accuracy of only if. Furthermore, it demonstrates that there is a statistically significant difference between GoogleNet and Alexnet, with a value of p=0.001 (independent sample T test p © 2024 IEEE.

关键词： Contrastive Learning

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Mitigating Urban Heat Island Effects: A Review of Innovative Pavement Technologies and Integrated Solutions

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Structural Durability & Health Monitoring 2024年第5期18卷 525-551页

作者： S.F.Ismael A.H.Alias N.A.Haron B.B.Zaidan Abdulrahman M.Abdulghani Department of Civil Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Central Region Heavy Civil DivisionGranite ConstructionWoodlandsTexas77381USA SP Jain School of Global Management LidcombeSydneyNSW2141Australia Department of Communication Technology and Network Faculty of Computer Science and Information TechnologyUniversiti Putra MalaysiaSerdang43400Malaysia

In this review paper,we present a thorough investigation into the role of pavement technologies in advancing urban *** analysis traverses the historical evolution of these technologies,meticulously evaluating their socio-economic and environmental impacts,with a particular emphasis on their role in mitigating the urban heat island *** evaluation of pavement types and variables inﬂuencing pavement performance to be used in the multi-criteria decision-making(MCDM)framework to choose the optimal pavement application are at the heart of our *** serves to assess a spectrum of pavement options,revealing insights into the most effective and sustainable *** highlighting both the existing challenges and potential innovative solutions within theﬁeld,this paper aims to offer a directional compass for future urban planning and infrastructural *** review not only synthesizes the current state of knowledge but also aims to chart a course for future exploration,emphasizing the critical need for innovative and environmentally sensitive pavement tech-nologies in the creation of resilient and sustainable urban environments.

关键词： Pavement sustainability urban heat island environmental impact of pavements innovative pavement solutions economic and social implications sustainable development in urban areas

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Smoothing Neural network for Non-Lipschitz Optimization with Linear Inequality Constraints

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Chinese Journal of Electronics 2021年第4期30卷 634-643页

作者： YU Xin WU Lingzhen XIE Mian WANG Yanlin XU Liuming LU Huixia XU Chenhua Department of Computer and Electronic Information Guangxi University The Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Science and Engineering Guilin University of Aerospace Technology Department of Electrical Engineering Guangxi University

This paper presents a smoothing neural network to solve a class of non-Lipschitz optimization problem with linear inequality constraints. The proposed neural network is modelled with a differential inclusion equation, which introduces the smoothing approximate techniques. Under certain conditions, we prove that the trajectory of neural network reaches the feasible region in finite time and stays there thereafter, and that any accumulation point of the solution is a stationary point of the original optimization problem. Furthermore, if all stationary points of the optimization problem are isolated,then the trajectory converges to a stationary point of the optimization problem. Two typical numerical examples are given to verify the effectiveness of the proposed neural network.

关键词： Non-Lipschitz optimization Neural networks Stationary points Convergence

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Cyber Deception System based on Monte Carlo Simulation in the Mobile Edge Computing (MEC)

Cyber Deception System based on Monte Carlo Simulation in th...

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2023 IEEE International Conference on computer, Information, and Telecommunication Systems, CITS 2023

作者： Kabdjou, Joelle Tagne, Elie Fute Rawat, Danda B. Acosta, Jaime Kamhoua, Charles University of Dschang Department of Mathematics and Computer Science Dschang Cameroon University of Buea Department of Computer Engineering Dschang Buea Cameroon Howard University Department of Electrical Engineering and Computer Science Washington United States Network Security Branch Devcom Army Research Laboratory Adelphi United States

ISBN: (纸本)9798350336092

The frequency of malicious activities by cyber attackers is on the rise, posing a significant challenge to counter cyber attacks in the Mobile Edge Computing (MEC) environment. The vulnerability of wireless networks, which serve as the primary communication media for MEC servers and IoT applications, is due to their open nature of communication. This work proposes a MEC architecture that utilizes network Function Virtualization (NFV) and Service Function Chain (SFC) to combat cyber adversaries. Additionally, we evaluate the performance of a cyber deception system, which leverages a software-defined networking (SDN) controller and decision- maker to continuously monitor incoming MEC traffic and direct cyber adversaries to a deception MEC server. The deception MEC server collects information about cyber adversaries' intent, capabilities, and potential damage to the system, thus securing legitimate users from cyber attacks. The proposed approach was evaluated using Monte Carlo simulations, and numerous results were obtained which demonstrate the effectiveness of cyber deception in MEC. © 2023 IEEE.

关键词： network function virtualization

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Total Transfer Capacity of the North Greece Border: Where Are We Now? 14

Total Transfer Capacity of the North Greece Border: Where Ar...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Dourbois, Grigoris A. Gisis, Alexandros Peitsinis, Petros Panopoulou, Chrysi Koumis, Andronikos Kampouris, Yannis Kontis, Eleftherios O. Southeast Electricity Network Coordination Center Thessaloniki Greece Independent Power Transmission Operator Athens Greece Department of Electrical and Computer Engineering University of Thessaly Volos Greece

ISBN: (纸本)9781837242689

Nowadays, Regional Coordination Centers (RCCs) promote the coupling of the European electricity markets by performing on a daily basis the Coordinated Capacity Calculation (CCC), that aims to compute the maximum Total Transfer Capacity (TTC) on a secure and reliable way. In this paper, the impact of several factors on the TTC of the North Greece (NGR) border is quantified and presented. More specifically, during 2023 two crucial changes have been applied on the power system of the NGR border. The first change is the energization of the Maritza East - Nea Santa tie-line, which is a second tie-line between Greece and Bulgaria. The second change is the modification of the protection relay settings of the tie-lines between North Macedonia and Greece. These tie-lines are the Bitola - Meliti and Dubrovo - Thessaloniki. The analysis reveals that both changes had a positive impact, increasing considerably the transmission capability of the NGR border. © The Institution of engineering & Technology 2024.

关键词： Power markets

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Enriching molecular graph representation via substructure learning

Enriching molecular graph representation via substructure le...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, HongHao Zhang, Acong Li, Ping Southwest Petroleum University School of Computer Science and Software Engineering Chengdu China China Mobile Jiangxi Co. Ltd. Network Department Ganzhou China

ISBN: (纸本)9798350386226

Continuous molecular graph representations are highly useful for effective molecule property predictions. However, learning graph-specific structure information remains challenging. Current graph neural network models typically aggregate all node embeddings of a graph into a single vector, which, while being an efficient compressor, may not capture the high-level function-related substructure information, e.g., motifs. Moreover, the nodes that are loosely relevant to the graph representation may lead to the sub-optimal representation learning. Towards these limitations, in this paper we propose a new approach to enrich molecular graph representation, where the most relevant motif are selected to represent the graph at a high level and the key-node spanned subgraph is constructed to filter out irrelevant nodes. By comparing to recently proposed unimodal graph neural network models and multi-modal methods, we show that our method achieves state-of-the-art performance on molecular property prediction task. © 2024 IEEE.

关键词： Molecular docking

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Impact of Cloud Computing on Agricultural Advancement using Data Mining Algorithms 2

Impact of Cloud Computing on Agricultural Advancement using ...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Saravanan, S.K. Nisha, F. Robin Rohit, V. Lenin, J. Selvam, P.D. Rajmohan, M. Department of BigData and Network Security Saveetha School of Engineering SIMATS Tamil Nadu Chennai India School of Engineering and Technology Computer Science & Engineering CMR University Karnataka Bengaluru India Computer Science & Engineering School of Computer Science & Engineering and Information Science Presidency University Karnataka Bengaluru India Department: Computer Science & Engineering Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India Department of Electronics and Communication Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai India

ISBN: (纸本)9798350340235

Incorporating Internet of Things (IoT) algorithms, Ensemble Learning, and Explainable AI (XAI) into the Reinforcement Learning framework provides a comprehensive solution. The framework's dynamic paradigm for precision agriculture increases output while decreasing resource consumption. The system adapts to its environment in real-time based on what it has learned via its previous experiences, thanks to Reinforcement Learning. By incorporating IoT algorithms, agricultural machinery may more easily gather and exchange data with cloud servers, improving precision and flexibility. By merging many models to provide reliable results, Ensemble Learning allows us to significantly enhance our predictive abilities. The usage of XAI, which provides context and justification for computations, has the potential to increase public confidence in AI-powered agricultural systems. It demystifies decision-making and arms participants with relevant information. In order to take use of the scalability and processing capacity of cloud resources, agricultural algorithms may now be readily installed and controlled with the help of this framework. Data mining algorithms have the potential to change agriculture by fostering sustainable practices and addressing challenges of global food security, as this research elucidates. © 2023 IEEE.

关键词： Agricultural Innovation Cloud Computing Data Mining Algorithms Ensemble Learning Explainable AI (XAI) Internet of Things (IoT) Precision Agriculture Reinforcement Learning Resource Optimization Sustainable Practices

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Improving Location-based Thermal Emission Side-Channel Analysis Using Iterative Transfer Learning

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IEEE Access 2025年 13卷 87672-87692页

作者： Zhang, Kai Lou, Tun-Chieh Wang, Chung-Che Jang, Jyh-Shing Roger Li, Henian Lin, Lang Chang, Norman Yiwu Industrial and Commercial College Zhejiang Yiwu322000 China National Taiwan University Graduate Institute of Network and Multimedia Taipei106 Taiwan National Taiwan University Department of Computer Science & Information Engineering Taipei106 Taiwan University of Florida Department of Electrical and Computer Engineering GainesvilleFL32611 United States Ansys Inc CanonsburgPA15317 United States

This study applies machine learning to side-channel attacks and proposes an iterative transfer learning method for deep learning models. This study leverages the similarity in training patterns across bytes by first training on a single byte and then using the resulting model as a pretrained foundation for the remaining bytes. This approach enables effective model training with smaller amounts of data while reducing the measurement-to-disclosure (MTD, i.e., the minimum number of traces needed for successful key recovery) in the attack phase. With sufficient data, iterative transfer learning reduces MTD from 55 to 54 using MLP and from 125 to 83 using CNN. Even under limited data conditions, it successfully breaks AES-128 while reducing training samples from 13,600 to 2,000, achieving an average MTD of 635, whereas traditional methods fail. Experimental results demonstrate that the iterative transfer learning approach addresses the persistent data scarcity challenge in deep learning, significantly expanding the applicability of deep learning methods in side-channel attack scenarios. © 2013 IEEE.

关键词： Deep learning

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