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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： M. Rajkumar Lakshi VS Karthik R Pavithra S Department of Smart Computing School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India B. Tech-Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

With the growing demand for wireless networks, network traffic has surged, particularly involving large-scale data transmission. This results in an increased number of network security threats and the emergence of various cyber-attacks. To overcome these threats, the development of an efficient intrusion detection system (IDS) has become crucial. By leveraging the recent advancements in Artificial intelligence (AI), this project introduces an optimized deep learning mechanism utilizing a prevalent deep learning model, the Deep Neural Network (DNN) algorithm. This proposed method uses feature engineering techniques like feature selection and feature reduction. Given the high dimensionality of the selected datasets, these techniques are employed to prevent over-fitting. Recursive feature elimination performs feature selection followed by Principal Component Analysis for the feature reduction process, thus enhancing the model's efficiency. The system is assessed using two benchmark datasets: KDD and UNSW-NB15. The experimental results indicate that the presented DNN model on the KDD dataset has achieved 92.45% accuracy and 91.35% accuracy on the UNSW-NB15 dataset for intrusion classification.

关键词： Deep learning Accuracy Wireless networks Computational modeling Intrusion detection Artificial neural networks Feature extraction Data models Tuning Principal component analysis

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Model-Free Reinforcement Learning Economic Dispatch Algorithms for Price-Based Residential Demand Response Management System

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2023年 1卷 123-135页

作者： Li, Jun Li, Huaqing Huang, Tingwen Zheng, Lifeng Ji, Lianghao Yin, Shen Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Texas A&M University at Qatar Doha Qatar Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Mechanical and Industrial Engineering Faculty of Engineering Norwegian University of Science and Technology Trondheim7033 Norway

It is projected that plug-in electric vehicles (PEVs) would steadily increase as household appliances. However, PEVs’ high power consumption, stochastic usage patterns, and storage capacity will surely result in a rise in the elasticity of demand response and pose significant difficulties for price-based residential demand response management (PRDRM). This artcle aims to optimize a two-tier globally shared nonconvex PRDRM problem with local constraints and PEVs, known as social welfare: maximizing retailer profits and minimizing the combined residential costs. This is done by balancing residential electricity use with retail electricity prices in an unknown market environment. The proposed online/offline model-free reinforcement learning-based economic dispatch (MFRL-ED) methods can adaptively decide on the ideal retail price sequence by integrating the daily residential-retailer behavior model with the agent-environment interaction method, providing a basic MFRL-ED solution for PRDRM without a system identification step and an accurate load-retail model. Experiments show that MFRL-ED methods provide an effective class of PRDRM solutions. © 2023 Institute of Electrical and Electronics Engineers. All rights reserved.

关键词： Reinforcement learning

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Computer Simulations of FIFZN (Fuzzy Inverse-Free Zhang Neurodynamics) with Expected Precision Adaptively Satisfied Handling TVQP (Temporally-Variant Quadratic Programming) 14

Computer Simulations of FIFZN (Fuzzy Inverse-Free Zhang Neur...

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14th International Conference on information science and technology, ICIST 2024

作者： Zhu, Sicheng Yang, Min Hu, Haifeng Zhang, Yunong Tan, Ning School of Electronics and Information Technology Sun Yat-sen University Guangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350353334

Some models and algorithms have been used to solve temporally-variant (i.e., time-dependent) quadratic programming (TVQP) in the last few years. But most of the models and algorithms contain inverse matrix and temporally-invariant sampling gap, which may lead to too much computation consumption that we do not want. In this paper, a sub-model based on inverse-free (IF) continuous Zhang neurodynamics (a.k.a., zeroing neurodynamics), IFCZN, is developed for solving the TVQP. A fuzzy system sub-model is designed and added to adaptively adjust the sampling gap under the expected precision. Aided with the fuzzy system sub-model, an inverse-free discrete algorithm, i.e., FIFZN (fuzzy inverse-free Zhang neurodynamics) discrete model, a.k.a., FIFZN algorithm, is further presented. In addition, the verification of the effectiveness and superiority of the FDZN (fuzzy discrete Zhang neurodynamics) algorithm via numerical experiments about four specific TVQP examples is conducted. © 2024 IEEE.

关键词： Fuzzy systems

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WISER: Weak Supervision and Supervised Representation Learning to Improve Drug Response Prediction in Cancer 41

WISER: Weak Supervision and Supervised Representation Learni...

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41st International Conference on Machine Learning, ICML 2024

作者： Shubham, Kumar Jayagopal, Aishwarya Danish, Syed Mohammed Prathosh, A.P. Rajan, Vaibhav Department of Electrical Communication Engineering Indian Institute of Science Karnataka Bengaluru India Department of Information Systems and Analytics School of Computing National University of Singapore Singapore Indian Institute of Technology Bihar Patna India

Cancer, a leading cause of death globally, occurs due to genomic changes and manifests heterogeneously across patients. To advance research on personalized treatment strategies, the effectiveness of various drugs on cells derived from cancers (‘cell lines') is experimentally determined in laboratory settings. Nevertheless, variations in the distribution of genomic data and drug responses between cell lines and humans arise due to biological and environmental differences. Moreover, while genomic profiles of many cancer patients are readily available, the scarcity of corresponding drug response data limits the ability to train machine learning models that can predict drug response in patients effectively. Recent cancer drug response prediction methods have largely followed the paradigm of unsupervised domain-invariant representation learning followed by a downstream drug response classification step. Introducing supervision in both stages is challenging due to heterogeneous patient response to drugs and limited drug response data. This paper addresses these challenges through a novel representation learning method in the first phase and weak supervision in the second. Experimental results on real patient data demonstrate the efficacy of our method WISER (Weak supervISion and supErvised Representation learning) over state-of-the-art alternatives on predicting personalized drug response. Our implementation is available at https://***/kyrs/WISER. Copyright 2024 by the author(s)

关键词： Self-supervised learning

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An Accuracy Analysis Of Classical And Quantum Machine Learning Algorithms Using Different Datasets

An Accuracy Analysis Of Classical And Quantum Machine Learni...

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Networking and Communications (ICNWC), International Conference on

作者： P. Bhaskaran S. Prasanna School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

Classical machine learning has practical significant advancements and extensive acceptance across various domains, enabling the growth of precise predictive models. The purpose of this work is to examine the accuracy gained by various machine learning algorithms, both classical and quantum. In addition, the study delves into the fundamental ideas and principles that form the backbone of quantum machine learning algorithms, such as quantum data representation, quantum feature mapping, and quantum optimization strategies. Experiments use classical machine learning models trained and tested on traditional hardware to measure evaluation metrics like classification accuracy, precision, recall, and F1-score for quantum machine learning models executed on quantum computing platforms or simulators. In quantum SVM, the seed dataset obtained 100% accuracy, and the supermarket dataset obtained 69% accuracy when compared to classical SVM. The findings imply that, under some conditions, quantum machine learning algorithms show the capacity to beat their conventional counterparts in terms of accuracy.

关键词： Support vector machines Training Machine learning algorithms Quantum algorithm Quantum entanglement Computational modeling Machine learning

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Discounted Iterative Adaptive Critic Designs With Novel Stability Analysis for Tracking Control

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1262-1272页

作者： Mingming Ha Ding Wang Derong Liu School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Faculty of Information Technology the Beijing Key Laboratory of Computational Intelligence and Intelligent Systemthe Beijing Laboratory of Smart Environmental Protectionand the Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA IEEE

The core task of tracking control is to make the controlled plant track a desired *** traditional performance index used in previous studies cannot eliminate completely the tracking error as the number of time steps *** this paper,a new cost function is introduced to develop the value-iteration-based adaptive critic framework to solve the tracking control *** the regulator problem,the iterative value function of tracking control problem cannot be regarded as a Lyapunov function.A novel stability analysis method is developed to guarantee that the tracking error converges to *** discounted iterative scheme under the new cost function for the special case of linear systems is ***,the tracking performance of the present scheme is demonstrated by numerical results and compared with those of the traditional approaches.

关键词： Adaptive critic design adaptive dynamic programming(ADP) approximate dynamic programming discrete-time nonlinear systems reinforcement learning stability analysis tracking control value iteration(VI)

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Enhancing Alzheimer’s Disease Detection with Chaotic Moth Flame Optimization Algorithm: A Feature Selection Approach

Enhancing Alzheimer’s Disease Detection with Chaotic Moth F...

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Emerging Technologies in Computer science for Interdisciplinary Applications (ICETCS), International Conference on

作者： S Sabari Vasan P Jayalakshmi School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

Advances in medical science have led to a better understanding of Alzheimer’s disease (AD), as shown by the clear description of AD using numerical, medical & MRI architectural imagery details. Early diagnosis of AD is possible using clinical data in addition its presence can be identified with huge correctness. Still, managing the massive and broadening AD data record is seemly an issue and utilising the unrefined data record unless removing redundant and duplicate data can significantly impact prediction or classification accuracy. Therefore, this research proposes an improved memetic metaheuristic behavioural founded feature selection framework called chaotic moth flame optimization algorithm (CMFOA). The suggested framework of MFO incorporates chaotic mapping to boost the search process, distinguishing it from the traditional form. CMFOA outperforms Ant colony optimization (ACO) and Firefly algorithm (FFA) in terms of ease, variable reduction, optimal efficiency, quick implementation, and rapid computing, resulting in improved subsets of characteristics for AD prediction. The study utilized the Alzheimer’s disease neuroimaging initiative (ADNI) dataset to ascertain potential characteristics implicated in the diagnosis of Alzheimer’s disease. The performance of the three feature subset selection approaches was validated using a deep neural network (DNN).The results demonstrated that CMFOA outperformed the other models in terms of accuracy.

关键词： Memetics Metaheuristics Fires Artificial neural networks Prediction algorithms Feature extraction Classification algorithms

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Loss and Vibration Analysis of Flywheel Energy Storage Motor for UPS System

Loss and Vibration Analysis of Flywheel Energy Storage Motor...

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2024 IEEE China International Youth Conference on Electrical engineering, CIYCEE 2024

作者： Yao, Kexin Ye, Caiyong Liu, Yi Yang, Mohan Zhao, Yunfeng School of Electrical and Electronic Engineering Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Engineering and Technology Wuhan430074 China School of Electrical and Information Engineering Tianjin University Tianjin300072 China National Center for Quality Inspection and Test of Motors Jingxian242500 China

ISBN: (纸本)9798331530174

When the flywheel energy storage motor for UPS system is running at high speed through standby, its motor loss and electromagnetic vibration will increase. In order to improve system reliability, the motor structure needs to be reasonably designed. This paper firstly analyses the working state of flywheel energy storage motor for UPS system. Secondly, the theoretical basis of loss calculation and electromagnetic vibration is summarized in detail. In addition, the influence of structural parameters such as air gap length on stator iron loss and the influence of structural parameters such as stator slot opening on electromagnetic vibration are analyzed respectively. Through the comprehensive analysis of loss and vibration, this flywheel energy storage motor can achieve the performance requirements of low loss and low vibration. © 2024 IEEE.

关键词： Flywheels

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Investigation on the Degradation Mechanism of AlGaN/GaN HEMTs under the Effect of Microwave Pulses at Different Powers 14

Investigation on the Degradation Mechanism of AlGaN/GaN HEMT...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Yang, Fanfeng Liao, Min Zeng, Binjian Yang, Peiliang Chen, Yiqiang College of Materials Science and Engineering Xiangtan University Xiangtan China The No.5 Electronics Research Institute of the Ministry of Industry and Information Technology The Science and Technology on Reliability Physics and Application of Electronic Component Laboratory Guangzhou China

ISBN: (纸本)9798350301359

We have investigated the effect of microwave pulse stress of different powers on AlGaN/GaN HEMTs. The dc characteristics of the devices were first scrutinized. The results indicate that the electrical characteristics of the devices are degraded after stress. The saturation drain-source current decreases from 681.8 mA to 246.5 mA, a decrease of 63.8 %, at the power 40 dBm. And at the power 45 dBm, the saturation drain-source current drops even more, from 662.3 mA to 176.7 mA, a drop of 73.3 %. The stress results in significant positive drift of threshold voltage and decrease of gate leakage current. And the degradation is more obvious with increasing microwave pulses power. Then, the internal defects of the device are characterized by low-frequency noise method, and the mechanism of device degradation is discussed. At the power of 45 dBm, the internal defect density of the device doubles, resulting in more electrons being captured at the channel. This is useful for the reliability level of future devices and practical applications. © 2023 IEEE.

关键词： AlGaN/GaN HEMTs degradation low-frequency noise microwave pulses power

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Smart Contract-Based Advanced Financial Security System: A Literature Review

Smart Contract-Based Advanced Financial Security System: A L...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Prabanand, S.C. Thanabal, M.S. Mahalakshmi, P. Solairaj, A. Durai Murugan, A. Gowsika Sree, S.M. Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Tamil Nadu Sathyamanagalam India Department of Computer Science and Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul India Department of Information Technology Kamaraj College of Engineering and Technology Tamil Nadu Virudhunagar India Department of Computer Science and Engineering Nadar Saraswathi College of Engineering and Technology Tamil Nadu Theni India Department of Computer Science and Business Systems M Kumarasamy College of Engineering Tamil Nadu Karur India

ISBN: (纸本)9798350305708

The current research provides a literature evaluation of works from sources such as IEEE and science Direct that explore testing and analysis methodologies and approaches for smart contracts. Smart contracts built on the blockchain have attracted a lot of attention from academics because of their unique properties. These include the immutability of transaction records, the automatic execution of contract instructions, and the decentralized building of trust. No one can deny blockchain's revolutionary potential across a number of sectors, but there are still technological hurdles to overcome before smart contracts gain widespread use. Solutions to these problems, as well as investigations into potential new uses for smart contracts outside of their present financial and logistics-related contexts, should guide future research efforts. In general, smart contracts built on blockchain technology have the power to revolutionize a number of sectors and open the door to a future that is more decentralized. © 2023 IEEE.

关键词： Blockchain concurrency Ethereum IoT scalability security smart contracts

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