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检索条件"任意字段=7th International Conference on Computing in Engineering and Technology, ICCET 2022"

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An Efficient Approach for EEG Seizure Detection using CNN with Feature Extraction 7

An Efficient Approach for EEG Seizure Detection using CNN wi...

引用

7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Joseph, Liya Izudheen, Sminu Narayanana, Uma Nair, Abhinav R Ashok Gopal, K.A. Rajagiri School of Engineering & Technology Department of Computer Science and Engineering Ernakulam India CVV Institute of Science and Technology Department of Computer Science and Engineering Ernakulam India

ISBN: (纸本)9798350372816

Epileptic seizures are a significant agony for those who suffer from them. Epileptic studies primarily focus on understanding the abnormal behavior of brain signals. Detecting seizures in EEG signals manually is a very chaotic and challenging task. therefore, we propose an automatic seizure detection system that drastically reduces the manual effort required for seizure detection while providing accurate results. the proposed method involves extracting twelve statistical features from segmented data and adjusting labels to facilitate binary classification using a Convolutional Neural Network (CNN). the dataset is normalized and balanced using SMOTE to address class imbalance. Evaluation metrics such as accuracy, precision and recall are computed on the test set with results visualized through accuracy and loss curves. User input allows for predictions on specific test data, determining the presence of seizures based on model predictions. this structured approach ensures reliable seizure detection through the use of advanced deep learning techniques and comprehensive model evaluation. Furthermore, a comparative study was conducted to assess the effectiveness of the proposed model against conventional machine learning algorithms such as Logistic Regression, Support Vector Machine (SVM), Gradient Boosting and Decision Tree. Our analysis reveals that CNN consistently demonstrates superior performance compared to these methods. © 2024 IEEE.

关键词： Convolutional neural networks

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TLM-NoC: Two Level Mesh Network-on-Chip for Performance Improvement 23

TLM-NoC: Two Level Mesh Network-on-Chip for Performance Impr...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Gagan, N. Bhowmik, Biswajit National Institute of Technology Karnataka Department of Computer Science and Engineering Mangalore575025 India

ISBN: (纸本)9781665494571

As the number of cores integrated into a system-on-chip (SoC) increases, the role played by this on-chip communication system becomes more and more critical due to communication bottleneck and scalability. the network-on-chip (NoC) design paradigm is today recognized as the most viable communication infrastructure over SoCs. However, the former architecture imposes latency concerns while the network size increases resulting in significant performance degradation. this paper finds an alternative solution for improving mesh NoCs, especially when long-distance communication takes place. the proposed solution suggests a variant of the 2D mesh topology named 'two-level mesh - network-on-chip (TLM-NoC)' in which a baseline mesh includes sparse mesh interconnection at the upper layer. the upper layer primarily establishes long-distance communications between nodes which in turn reduces latency and improves other metrics. Experimental results show adequate performance improvements by the TLM-NoC over traditional meshes. Evaluation of TLM-NoC shows latency improvement by 17.97%, resulting in 33.33% packet flit loss reduction and 26.80 % increased throughput. Further, the proposed architecture consumes 23.69% less energy for delivering traffic. © 2021 IEEE.

关键词： Performance evaluation Smart cities Scalability Network-on-chip Computer architecture Traffic control throughput

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A Novel Power Divider With Tunable Power Ratios and Reconfigurable Output Phase Differences 7

A Novel Power Divider With Tunable Power Ratios and Reconfig...

引用

7th IEEE international conference on Electronic Information and Communication technology, ICEICT 2024

作者： Huang, Jialin Guo, Xulong Xue, Shanshan Xu, Chang Huang, Yong Mao Wang, Xiaolong Jilin University College of Electronic Science and Engineering International Center of Future Science Changchun China Coordination Center of China Inner Mongolia Branch of National Computer Network Emergency Response Technical Team Hohhot China Xihua University School of Electrical and Electronic Information Chengdu China

ISBN: (纸本)9798350384437

In this letter, a novel power divider with a wide tunable range of power ratios and reconfigurable output phase differences is proposed. the tunable power ratio is implemented by replacing the branch transmission line of the conventional Gysel power divider. the branch transmission line equivalent circuit consists of three short-circuited varactors cascaded through two sections of transmission lines. Moreover, by selecting appropriate capacitances of the varactors, the proposed novel power divider can operate either in an in-phase mode or out-of-phase mode. Finally, the electromagnetic (EM) simulation of six examples was conducted and one experimental circuit of the proposed novel power divider was fabricated. the simulation and measurement results verified the feasibility of the proposed novel power divider. © 2024 IEEE.

关键词： Power transmission lines

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False Data Injection Attack Detection Based on Wavelet Packet Decomposition and Random Forest in Smart Grid 23

False Data Injection Attack Detection Based on Wavelet Packe...

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作者： Chen, Zhenyu Yuan, Shuai Wu, Longfei Guan, Zhitao Du, Xiaojiang School of Control and Computer Engineering North China Electric Power University China Brock University Department of Finance Operations and Information Systems ON Canada Fayetteville State University Department of Mathematics and Computer Science Fayetteville United States Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9781665494571

As one of the critical infrastructures, the safety and reliability of the smart grid are directly associated with the development and stability of society. However, studies have shown that the power grid is at risk when the parameters are manipulated and cyber-attacks are generated against the state estimation, i.e., under false data injection attack (FDIA). Currently, a rich body of literature has studied on the FDIA defense methods, but most of them focus on the direct current (DC) scenario. this paper proposes a novel detection model that combines the wavelet packet decomposition (WPD) technique with the random forest (RF) algorithm. the WPD is able to capture the deviation of parameters from the normal conditions, whereas the RF is developed to classify these features and effectively identify the malicious data. the proposed model is also evaluated using real-world data on IEEE 118-bus power system. the results demonstrate excellent performance on precision rate and recall rate under varying scenarios. © 2021 IEEE.

关键词： Radio frequency Training data Interference Power system stability Feature extraction Data models Wavelet packets

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A Text Structure-based Extractive And Abstractive Summarization Method 7

A Text Structure-based Extractive And Abstractive Summarizat...

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7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Yan, Jing Zhou, Shihua Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian China

ISBN: (纸本)9781665478571

Extraction summarization and abstraction summarization have advantages and disadvantages, so how to better combine these two ways has become a difficult problem. To address this challenge, this paper proposed a new fusion method. the major novelty lies in the design of the new method. Wherein our approach, first, use the text structure information and the idea of the K-means algorithm to divide the text into regions, and then the main part and the non-main parts are determined according to the distribution of the subject words. Next, apply information extraction on the main part and text generation on the non-main parts. Finally, the two parts of the summarization are merged according to the sequence of the text. Experimental results show that the quality of the summarization is better than that of extraction summarization and abstraction summarization. In addition, to make the method more targeted in Chinese text processing, the Cw2vec model based on Chinese stroke information is used in the encoding process, and the experiment proves that the quality of summarization can be further improved. © 2022 IEEE.

关键词： Text processing

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EEG and Forehead EOG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks 23

EEG and Forehead EOG-Based Driver Fatigue Classification Usi...

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作者： Zhang, Jie Xiao, Nanfeng Guangzhou Xinhua University Guangzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9781665494571

In this study, we fuse electroencephalography (EEG) signals and forehead electrooculogram (EOG) signals to classify the driver's fatigue and alert states. Based on time-varying power spectral density (PSD) and Hilbert-Huang spectrum entropy extracted from EEG, 36 eye movement features from EOG, we develop novel sparse-deep belief networks (Sparse-DBN) for detecting driver drowsiness. PSD, four modalities, and support vector machine (SVM) classifier are used for comparison. the experimental results show that EEG and forehead EOG feature fusion can improve performance compared with two single modalities. Furthermore, the results show that the classification performance achieves an improved classification performance using two feature fusions and Sparse-DBN classifier with the sensitivity, specificity, accuracy,and area under the curve of 97.99% /96.72%, 94.73% /95.51%, 97.93% /97.91%, and 0.96273/ 0.96376, respectively. Additionally, EEG and forehead EOG features can be collected simultaneously using only four shared forehead sites and achieve comparative performance compared with traditional sites, which is the preferred approach for practical applications. © 2021 IEEE.

关键词： Support vector machines Electrooculography Forehead Sensitivity Fuses Fatigue Feature extraction

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Ternary SRAM Cell Designs for Next-Generation: A Comprehensive Investigation of 6T, 8T, and 10T Designs 7

Ternary SRAM Cell Designs for Next-Generation: A Comprehensi...

引用

7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Bale, Ajay Sudhir Vura, Swetha Arulananth, T.S. Sai Nithin Reddy, Daggula Reddy, B. Rithvik Nithin Kumar Reddy, Nalobannagari New Horizon College of Engineering Department of Electronics and Communication Engineering Bengaluru India Impact College of Engineering and Applied Sciences Department of Automation and Robotics Bengaluru India MLR Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad500043 India

ISBN: (纸本)9798350372816

Ternary logic is popular where it uses logic levels of 0, 1 and 2 instead of using the old binary logic structure. Due to this, it has grown to be very popular method in many applications. It is used in applications which focuses on high density data processing and also to obtain lower power consumption. the design of this SRAMs poses lot of challenges and there is a high demand for robust and optimized memory units. this work concentrates on overcoming the problems of higher power consumption that is found in traditional SRAM cells. It focusses on the comprehensive study if 6T, 8T and 10T SRAM cells. through this study, the performance metrics are analyzed for power consumption and R/W efficiency. the results obtained shows the lower consumption of power and improved R/W efficiency. © 2024 IEEE.

关键词： Static random access storage

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Problems and Issues of Emotional computing and Artificial Emotional Intelligence: A Review of Rosalind W. Picard's thesis 'Affective computing: Challenges' 7

Problems and Issues of Emotional Computing and Artificial Em...

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7th IEEE/ACIS international conference on Big Data, Cloud computing, and Data Science, BCD 2022

作者： Lee, Jina Lee, Saeyeon Kim, Dongcheol Soongfill Univ Dept of Business Administration Seoul Korea Republic of SoongSill Univ Dept of IT Policy and Management Seoul Korea Republic of

ISBN: (数字)9781665465823

ISBN: (纸本)9781665465823

Affective computing not only includes interactions with machines such as artificial intelligence, robots, and computers that recognize, respond to, and influence human emotions, but also spans a wide range of research fields such as psychology, engineering, sociology, mathematics, computer science, education, and linguistics. Artificial Emotional Intelligence is developing beyond the stage of recognizing and interpreting human emotions to simulating emotions, stimulating human emotions, and interacting emotionally. As the market for emotions related technology grows larger, it is very important to look at the problems, issues, and current status of this field. this paper will look into the limitations, problems, and issues of emotional computing and artificial emotional intelligence raised in a paper 'Affective computing: Challenges' by Rosalind W. Picard (Published in 2003) and discuss how many of these problems have been solved and what new current issues have been raised. © 2022 IEEE.

关键词： Emotion Recognition

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IoT and AI Synergy in Society: Robotics and Automation Perspectives 7

IoT and AI Synergy in Society: Robotics and Automation Persp...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Geetha, B.T. Jothi, J. Salomi Backia Pal, Shaunak Islam, Imam Taqi Deen Raghuram, K.S. Soni, Mukesh Simats Saveetha University Saveetha School of Engineering Department of Ece Tamil Nadu India Srm Institute of Science and Technology Faculty of Science and Humanities Department of Commerce Ramapuram campus Chennai600089 India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India University of Electrical Science and Technology of China Sichuan Chengdu China Department of Mechanical Engineering Visakhapatnam India Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science& Technology Tathawade Pune India

ISBN: (纸本)9798350350067

the Internet of things, or IoT, is a relatively new Internet revolution that is being successfully adopted by the corporate, industrial, healthcare, and economic sectors as well as other areas of the modern information society. Specifically, IoT with artificial intelligence (AI) support improves a great deal the performance in a wide range of daily applications with a focus on automation systems, robotics, transportation, and business. Smart automation in the manufacturing sector is now possible thanks to the development of Industry 4.0. the BRDT algorithm is used in this work to track how much energy appliances and machinery use, forecast behaviour, and identifies unusual conduct. When the suggested system's efficiency is compared to current approaches, it provides an efficiency of 85%. the application of this technology raises a number of standardisation, security, resource planning, legal, and paradigm-shifting business challenge problems. the whole supply chain and all of its actors, from producers to end users, are crucial to the success or failure of Industry 4.0 deployment. © 2024 IEEE.

关键词： Supply chains

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Efficient Drinking Water Quality Analysis using Machine Learning Model with Hyper-Parameter Tuning 7

Efficient Drinking Water Quality Analysis using Machine Lear...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Maheswari, M Uma Sudharsanan, R. Arthy, M. Jenefer, Anne Oormila, L. Samuthira Pandi, V. RMD Engineering College Department of Electrical and Electronics Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Information Technology Tamil Nadu Ramapuram India SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu India Agni College of Technology Department of Computer Science Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Electronics Ad Communication Engineering Tamilnadu Ramapuram India

ISBN: (纸本)9798350397253

the conventional approaches to determining the quality of water include costly and lengthy statistical and laboratory testing;hence, the concept of real-time monitoring is no longer applicable in the modern day. the disastrous implications of poor water quality demand that there be a remedy that is both more expedient and more realistic. this research study examines a variety of supervised machine learning algorithms in order to identify the quality of the water. A variety of variables, such as the pH value, the hardness, the solids, the chloramines, the sulfates, the conductivity, the organic carbon, the trihalomethanes, the turbidity, and the potability of the water, are essential for determining the quality of the water. Random Forest (RF) and Decision Tree (DT) are used in order to ascertain the caliber of the water that is suitable for human consumption. the standard laboratory approach for testing the quality of water is one that takes a lot of time and may be somewhat expensive at times. In a very short length of time, the algorithms that are proposed in this study are able to provide an estimation of the quality of drinking water. DT has a height accuracy F1 score of 99%, whereas RF has a score of 87.86% and a precision of 82.36%. the difference in these two scores is due to the fact that DT's precision is lower. the suggested method demonstrates its potential for usage in real-time water quality monitoring systems by achieving appropriate accuracy with a minimal set of parameters. To prove the usefulness of this program, this is essential. © 2023 IEEE.

关键词： Water quality

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