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

共 11091 条记录，以下是941-950 订阅

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Design and Performance Evaluation of a Non-Volatile Hybrid 6T-4MTJ SRAM Bit Cell for Enhanced Power and Stability in 32-Nanometer technology 7

Design and Performance Evaluation of a Non-Volatile Hybrid 6...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Shirode, Ujwal R. Kanphade, R.D. Gaadhe, Ajjay S Ayane, Swapni S. Pimpri Chinchwad College of Engineering Department of Electronics and Telecommunications Maharashtra Pune India Jaywantrao Swant College of Engineering Maharashtra Pune India

ISBN: (纸本)9798350304268

the need for increased memory in the electronics industry and the technical scaling issues of existing memory technologies have motivated researchers to develop next-generation memory technology. Scaling down of technology can result in reduced stability, increased leakage current, and higher standby power dissipation in electronic circuits, and to address this issue, Spin-transfer torque random access memory (STT-RAM) is a viable contender because it has non-volatility, no standby leakage power, quick access times, high densities, strong read and write performance, and uses low power. this work proposes a non-volatile hybrid 6T-4MTJ SRAM bit cell based on 32-nanometer technology and shows it's comparison with a conventional 6T SRAM bit cell to evaluate the performance in terms of Power and Stability of process on design metrics. the proposed hybrid bit cell (6T-4MT J) shows a lower leakage power than a conventional CMOS-based SRAM cell. the proposed MTJ-based SRAM bit cell design improves overall stability by 15.85% while reducing overall power consumption by 90%. © 2023 IEEE.

关键词： Magnetic Tunnel Junction (MTJ) MRAM Spintronics

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Prediction on Impact of Electronic Gadgets in Students Life using Machine Learning 7

Prediction on Impact of Electronic Gadgets in Students Life ...

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Saraswathi, K. Devadharshini, B. Kavina, S. Srinidhi, S. Department of Computer Technology Kongu Engineering College Erode India

ISBN: (纸本)9781665464086

Nowadays electronic gadgets play an important role in students' life as a source of learning. the Dependency of services provided by electronic gadgets has reached a large scale. Electronic gadgets like smart phones have a major impact on people in their day-to-day life. Among all, students are the important one, as they rely on electronic gadgets for their academic activities. the major impact is that it can affect the students mental and physical health. Students are getting addicted to these electronic gadgets as it becomes inevitable. this study uses machine learning techniques to demonstrate how gadgets affect students' daily lives. To examine the addictiveness of gadgets among the students, a questionnaire has been circulated to get to know the student's necessity on electronics. the parameters include how many electronic devices they use and how long they use them for, whether the usage of electronic gadgets shows any improvement in their academic performance. Machine learning employs the preprogrammed algorithms, to predict output values for the given input data. It is considered to be an aspect of artificial intelligence. Machine learning algorithms are used in a variety of fields, such as computer vision, voice recognition, medicine etc. Where it is difficult or impractical to create conventional algorithms to perform the necessary tasks. the collected dataset is taken to analyse the performance of prediction for various Machine Learning algorithms like K-Nearest Neighbour, Random Forest, Decision Tree, Logistic regression, Support Vector Machine. As a result of this study, accuracy of several performance measures were evaluated. In the future the performance of an algorithm can be improved by using optimization techniques. © 2023 IEEE.

关键词： Students

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Design and Simulation Application of Fuzzy Controller Based on Granular computing 7

Design and Simulation Application of Fuzzy Controller Based ...

引用

7th international conference on Computational Intelligence and Applications, ICCIA 2022

作者： Li, Huiyue Yang, Jianhua Lu, Wei School of Control Science and Engineering Dalian University of Technology Dalian City China

ISBN: (纸本)9781665495844

Fuzzy control theory generates fuzzy rules based on expert experience and experimental data, so fuzzy control method can control complex and large-scale systems without precise mathematical models. But fuzzy control method still has problems: complex structure and rule explosion problem. Aiming at the above problems, this paper proposes a fuzzy control method based on granular computing. Firstly, design the fuzzy controller, the fuzzy rules are generated by the fuzzy space division method. then, using the information granulation method, each fuzzy rule is granulated into an information granule. Taking points in the information granules to fit the realization function of the granular function, using that function control the object instead of the fuzzy controller. the fuzzy reasoning process is omitted, and the number of rules has nothing to do with the accuracy, it is only related to the number of granules. therefore, the structure of the control system can be simplified under the condition of ensuring the accuracy of the system, and the problem of rule explosion can be avoided at the same time. the simulation experiment using the second-order inverted pendulum as the control object proves the feasibility and effectiveness of the fuzzy control method based on granular computing. © 2022 IEEE.

关键词： Fuzzy control

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7th international conference on Innovative computing, IC 2024

7th International Conference on Innovative Computing, IC 202...

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7th international conference on Innovative computing, IC 2024

ISBN: (纸本)9789819741205

the proceedings contain 157 papers. the special focus in this conference is on Innovative computing. the topics include: Proactive Auto Scaling Based on Marginal Request Change Analysis for Reducing Tail Latency in Kubernetes Cluster;Integration of Generative Adversarial Networks (GAN) and AI Drawing in Criminal Sketches—Applied in Crime Scene Investigation by Law Enforcement;implementation of a High Quality Cardiopulmonary Resuscitation Teaching Intelligence Training and Assessment System;Predicting Twitter Posts from Fake Accounts Using XGBoost Model;the Interplay Between Self-regulated Learning and Programming Learning Achievement;a Study on the Continued Viewing Intention of YouTuber Videos: Experiential Marketing, Social Capital, and Habit Perspectives;an Exploratory Study on the Design of Human-Computer Interaction Interfaces for Augmented Reality Integrated Museum Exhibitions;communication-Aware Optimization of Microservice Scheduling on Edge computing;Design of Computer Model for Raw Material Supplier Selection Based on Entropy Weight TOPSIS Method;exploring How Cybersecurity Competitions Can Help Improve Students’ Practical Abilities in Cybersecurity;Adversarially Residual U2Net for COVID-19 Lung Infection Segmentation from CT Images;partnerships Analysis Based on Measurements of Common Research Interest;System Design of Operating Costs of Intelligent Healthcare Services for the AHP Algorithm;a Multi-party Private Set Union Protocol Against Malicious Adversary;analysis of the Current Status of Research on Unmanned Collaborative Equipment Above and Below Water;data Mining Analysis of New Energy Vehicles Based on Cluster Analysis technology;Two-Layer Minimum Variance FIR Filter-Based DPLL Design;fuzzy Proximity Two-Dimension Inductance Gesture Recognition System Analysis and Implement;proximity Capacitive Gesture Recognition for Recursive Neighbor Memory Neural Network;application of Genetic Algorithms in Automated Mechanical Design.

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Generating Adversarial Examples in Audio Classification with Generative Adversarial Network 7

Generating Adversarial Examples in Audio Classification with...

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7th international conference on Image, Vision and computing, ICIVC 2022

作者： Zhang, Qiang Yang, Jibin Zhang, Xiongwei Cao, Tieyong Army Engineering University Graduate School Nanjing China Army Engineering University Command and Control Engineering College Nanjing China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

To improve the performance of acoustic adversarial examples, this paper proposes an adversarial generation model based on Generative Adversarial Network (GAN) for audio classification. By introducing the classification model into GAN, this paper proposes a general GAN framework to execute adversarial attacks for audio classification. then we propose a Short-time Synthesis GAN-based (SSGAN) attack method, which can reduce the complexity of audio adversarial example generation, and further improve the generality and performance of the GAN-based audio adversarial example generation. Experiments on audio classification datasets such as UrbanSound8k and ESC50 show that compared with existing audio adversarial example generation methods, the proposed method generates adversarial examples with lower perceptibility, and has a higher attack success rate and attack efficiency for typical audio classification models. © 2022 IEEE.

关键词： Generative adversarial networks

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Drone Assisted Deep Learning based Wildfire Detection System 7

Drone Assisted Deep Learning based Wildfire Detection System

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7th international conference on Parallel, Distributed and Grid computing, PDGC 2022

作者： Gupta, Vaasu Roy, Sutirtha Jaiswal, Vaibhav Bhardwaj, Kartik Rana, Prashant Singh Thapar Institute of Enginnering and Technology Electronics and Communication Engineering Department Punjab Patiala147004 India Thapar Institute of Enginnering and Technology Electrical and Instrumentation Engineering Department Punjab Patiala147004 India Thapar Institute of Enginnering and Technology Computer Science and Engineering Department Punjab Patiala147004 India

ISBN: (纸本)9781665454018

Wildfires are one of the major threats to forest sustainability. therefore, there is an urgent need for an early prevention system for wildfires. the flight controller comprises a Node Micro-Controller Unit board and is controlled by an ESP8266-based Wireless setup. To address the problem of wildfire monitoring in forests, this paper proposes a wildfire monitoring system based on drones and deep learning techniques. In the present study, after pre-processing the images collected from a drone camera, deep learning models are used to classify them as containing fire or non-fire. Among the approaches discussed, the customized Inception model trained over the combined data-set from six different sources outperforms the previously presented works, achieving an overall validation accuracy of 96.04% and performing well on fire-like images and from bright day to dim light images also. © 2022 IEEE.

关键词： Drones

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Regression and Classification-Based Model Predictive Maintenance of Aircrafts Using Machine Learning 7

Regression and Classification-Based Model Predictive Mainten...

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7th international conference on Parallel, Distributed and Grid computing, PDGC 2022

作者： Banday, Aamir Shafi Chohan, Jasgurpreet Singh Kumar, Raman Chandigarh University Department of Mechanical Engineering Punjab Mohali India

ISBN: (纸本)9781665454018

One of the main hurdles of Industry 4.0 is to predict Aircraft failures Since downtime costs money and reduces productivity, the ability to prevent failures is essential. Knowing how many cycles or RULs remain before a breakdown occurs is crucial for Aircraftry. Where possible under the same circumstances, the RUL estimations should be based on prior observations. the development of technologies that track equipment's present state is at the heart of Evaluate the influence research.. Despite the enormous research on this subject, no one universal strategy exists. the lack of a universal method is what drives this notion, which uses recurrent neural networks (RNN) for the monitoring of the proposed approach. © 2022 IEEE.

关键词： Recurrent neural networks

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In-Depth Investigation of Artificial Intelligence - Blockchain Integration Strategies for Security Enhancement 7

In-Depth Investigation of Artificial Intelligence - Blockcha...

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

作者： Jothy, C.R. Judith, J.E. Noorul Islam Centre for Higher Education Computer Science and Engineering Tamilnadu Kanyakumari India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372816

Blockchain being a distributed ledger preserves data in a highly secured environment which cannot be modified even by the user who created the data. this security aspect makes the blockchain technology popular in recent years and was adopted by many organizations to secure their data. the other technology which is very popular recent times is the Artificial Intelligence (AI). Nowadays, day to day life activities are tied up with the use of AI. Knowingly or unknowingly we are using AI in various circumstances. As the world becomes digitalized almost in all aspects, security threats are also has been growing in different dimensions. this paper makes a deep survey on various research articles which integrates AI and blockchain technology to improve the overall security. this paper also analyses the various methodologies and the machine learning algorithms used by different researchers to secure data on a blockchain so that the highly secured data can be automated with the integration of AI. If proper integration techniques were implemented with appropriate Machine Learning algorithms the security will be much improved. this paper concludes by providing a path way to the upcoming researchers to move their research based on the problems identified by comparing various research articles. © 2024 IEEE.

关键词： Adversarial machine learning

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Student in-class behaviors detection and analysis system based on CBAM-YOLOv5 7

Student in-class behaviors detection and analysis system bas...

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

作者： Yang, Sen-Quan Chen, Yong-Hao Zhang, Zhen-Yu Chen, Jing-Hua Shaoguan University School of Intelligent Engineering Shaoguan China

ISBN: (纸本)9781665478571

Analyzing the students' in-class behaviors is able to intuitively reflect their learning states, which are important for teaching evaluations and for designing instruction. In order to automatically detect and recognize students' behaviors through surveillance video, we design a CBAM-YOLOv5 detection and analysis system. By fusing Convolutional Block Attention Module (CBAM), CBAM-YOLOv5 could eliminate the effect of background information and effectively extract robust feature of student behaviors. A new dataset including six in-class behaviors, such as drinking, study, trance, writing, hand_up and using_phone, is built to training and validating the proposed CBAM-YOLOv5. Experiments demonstrate that the effectiveness and robust feature extracting capability of the proposed CBAMYOLOv5. the detected students' in-class behaviors are classified and summarized, and the teaching quality report is timely fed back through Wechat applet. © 2022 IEEE.

关键词： Students

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Safety helmet detection system of smart construction site based on YOLOv5S 7

Safety helmet detection system of smart construction site ba...

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

作者： Zhou, Minghang Zhu, Jiuyu Li, Xinyu TongJi University College of Electronic and Information Engineering China Xi'An JiaoTong University College of Physics Xi'An China Hunan University of Technology College of Artificial Intelligence Hunan China

ISBN: (纸本)9781665478571

Because of the high risk of construction work, the safety of construction personnel have been widely paid attention from all walks of life. Some construction personnel does not obey the rules and regulations of the construction site, such as often do not wear safety helmets, which greatly increases the potential safety hazard (on the construction site). However, since the construction site environment is relatively complex and usually of the small target, with relatively backward hardware facilities and low economic investment, the model real-time detection performance is not ideal and has low robustness. To settle these problems, this paper proposes a YOLOv5 model to judge the real-time proportion of work-site construction workers wearing safety helmets and build the helmet detection for the complex environment based on YOLOv5S. Experimental evaluation shows that the verification of the given safety helmet wear test data set reached the average accuracy of 93.8%. Compared with the previous YOLOv5 algorithm, the accuracy of helmet-wearing detection is improved by 1.1%, which meets the helmet detection requirements in complex construction scenes. © 2022 IEEE.

关键词： Safety devices

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