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

共 11094 条记录，以下是1071-1080 订阅

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A Text Structure-based Extractive And Abstractive Summarization Method 7

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

引用

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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A Medical Model Built on Machine Learning to Evaluating the Relationship between the Depression and Living Standards 7

A Medical Model Built on Machine Learning to Evaluating the ...

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

作者： PragnaSri, Suggala Nallagattla, Venkata Ramana Gupta Ajajunnisa, Shaik Amarendra, K. Phanindra, Pinninti Bhanu Parvathaneni, Sriram Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of Technology Vijayawada India Department of Computer Science and Engineering Koneru Lakshmaiah Foundation Andhra Pradesh Vaddeswaram522502 India

ISBN: (纸本)9781665464086

Future healthcare reforms are being influenced by new technological developments. Finding the elements that contribute to depression may inspire fresh research and therapeutic approaches. Depression is a difficulty that many people experience, because depression is a condition that is increasingly posing a major community health threat. To manage and analyze the diverse data and comprehend the relationship between depression and life satisfaction, this paper uses machine learning techniques. the assessment study is afterwards divided principally in to the two parts. A data consolidation procedure is introduced in the first section. Data relationships are established and each relationship in data is uniquely identified using the Secure Hash Algorithm idea. A model that incorporated was proposed in the second segment. Unsupervised machine learning technique. K-Means is used for clustering. Supervised machine learning algorithms like Naïve Bayes, Multi Class Support Vector Machine with Posterior Probability (MCSVMPP), KNN and Voting classifier is usedto find the depression according to different factors. © 2023 IEEE.

关键词： Support vector machines

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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...

引用

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： 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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Multi-Facial Automated Attendance System using Haar Cascade, LBPH, and OpenCV-Based Face Detection and Recognition 7

Multi-Facial Automated Attendance System using Haar Cascade,...

引用

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

作者： Rathi, Snehal Kapadnis, Snehal Sonar, Gayatri Sonar, Gaurav Vispute, Sushma R. Vishwakarma Institute of Information Technology Pune India Vishwakarma Institute of Technology Pune India Aress Software Nashik India Pimpri Chinchwad College of Engineering Pune India

ISBN: (纸本)9798350304268

In the 21st century, everything around us is dependent upon technology to make our lives much easier. Daily tasks are increasingly being computerized. Nowadays more people prefer to do their work electronically. To the extent of our knowledge, students' attendance at some universities is manual. the attendance management system computerizes the traditional attendance process. Utilizing OpenCV (Open-source Computer Vision) and some face detection and recognition algorithms, we propose a system to measure student attendance in classes. By using face detection algorithms and face recognition algorithms, we will keep track of how many people are present in the class and attendance of each one. this project intends to provide a system that automates and streamlines the process of collecting and monitoring student attendance using facial recognition technology. To find, identify, and validate collected faces, preprocessing methods like the Haar Cascade approach will be used. To make the attendance process quicker and more accurate, we aim to offer a method. © 2023 IEEE.

关键词： attendance management system computer vision face detection face recognition Haar Cascade approach

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A Parallelism-Based Earliest Finish Time (PBEFT) Algorithm for Workflow Scheduling in Clouds 7

A Parallelism-Based Earliest Finish Time (PBEFT) Algorithm f...

引用

7th international conference on Cloud computing and Big Data Analytics, ICCCBDA 2022

作者： Wu, Xiaojun Cheng, Sijia Yuan, Sheng Wang, Ziyi School of Software Engineering Xi'An Jiaotong University Xi'an710049 China

ISBN: (纸本)9781665487115

For those public-cloud-based application providers, workflow scheduling in clouds must not only meet traditional performance optimization goals, but also minimize financial costs. this paper focuses on budget-constrained workflow scheduling issue on heterogeneous cloud resources, and tries to minimize the end-to-end delay of the workflow while meeting the given budget. A heuristic algorithm is proposed, which introduces a resource leasing mechanism to maximize the usage of the budget. this paper also defines a scheduling factor parameter consisting of two factors (parallelism and urgency) to determine the scheduled tasks. A scheduling algorithm based on task duplication is designed, which reduces the finish time of workflow tasks by using the idle time slots in existing servers. Finally, compared with contrast algorithms, the experimental results show that the proposed algorithm is superior in both meeting budget constraints and minimizing end-to-end delay. © 2022 IEEE.

关键词： Cloud computing Costs Scheduling algorithms Parallel processing Scheduling Virtual machining Delays

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GNAS: Generalized Neural Attentive Similarity Model for Recommendation 23

GNAS: Generalized Neural Attentive Similarity Model for Reco...

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作者： Cheng, Zhilin Lyu, Yanxia Liu, Jiemin Wang, Cong School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China

ISBN: (纸本)9781665494571

Item-based collaborative filtering captures user preferences by using the historical interactive items of users and generates a personalized recommendation list. Recently, the state-of-the-art collaborative filtering method is neural attentive item similarity model (NAIS). However, the exploration of ensemble models has obtained less investigation. In this work, we present a generalized neural attentive similarity (GNAS) model to perform recommendation based on implicit feedback. the key of our design is to integrate the generalized matrix factorization model and neural attentive item similarity model, which allows the model to have both the memorization ability from matrix factorization model and the generalization ability from neural model. To improve the user interest expression ability of the model, we add an ensemble kernel to the attention mechanism, learning the attention weight of historical items that users have interacted with before. Experimental results show that the recommended accuracy of our model is improved compared to the latest recommended models. In addition, GNAS also has the advantage of less time consumption and interpretability of recommendation. © 2021 IEEE.

关键词： Weight measurement Smart cities Collaborative filtering Diversity reception Decision making Collaboration Reinforcement learning

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ANN based Harmonic Extraction in DFIG Fed DSTATCOM System 7

ANN based Harmonic Extraction in DFIG Fed DSTATCOM System

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

作者： Vijayasarathi, N. Senthilkumar, Masan Ragul, S. Pandi, P. Vasanthprakash, M. Karthikeyan, D. Chettinad College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Karur India SRM Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350372816

the evolution of the renewable energy is the best solution for environment to reduce the pollution. Due to the fastest development of the Wind power penetration in the power system many countries offer the stable operations of power system. the most widely installed machine in the wind farm is Doubly Fed Induction Generator (DFIG).the operation of a wind energy conversion system (WECS) is based on a DFIG which is Sensitive to unbalanced non-linear load. In the proposed system the AC source is given to the non-linear load, to reduce the fluctuation the control function Distribution Static Synchronous Compensator (DSTATCOM) is used where the Input power is fed to the DFIG, it converts the AC voltage to DC and fed to Voltage Source Invertor (VSI). the reference and actual current get combined and passes to the Proportional Integral (PI) controller which is used to adjust reference and actual values and then fed to the Pulse Width Modulation (PWM) generator. the source voltage and source current are given to the DQ theory which produce the reference current and then fed to Artificial Neural Network (ANN) classifier which is used to reduce the harmonics. the PWM generator which improve the power quality and generate pulses which is applied to VSI and thereby, the generation of an opposing current is injected to the Point of Common Coupling (PCC). the design process is accessed by the MATLAB simulation. © 2024 IEEE.

关键词： MATLAB

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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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High Speed True Random Number Generator Controlled by Logistic Map 23

High Speed True Random Number Generator Controlled by Logist...

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作者： Luo, Yuling Han, Shuming Zhang, Shunsheng Wang, Yanhu Liu, Junxiu School of Electronic Engineering Guangxi Normal University Guilin China

ISBN: (纸本)9781665494571

the integrated circuit technology has occupied a dominant position in electronic equipment. Traditional True Random Number Generators (TRNGs) are mostly designed based on analog circuits, which cannot satisfy current needs. the ring oscillators are easy to produce phase jitter and D flip-flop can easily enter the metastable state at higher input signal frequency. In this paper, a TRNG is designed by using the ring oscillator and D flip-flop as a source of randomness. Moreover, the Logistic map is used to realize the frequency conversion between the high-frequency output of the system and the low-frequency sampling of the random source to make the random source accumulate more jitter. the design has been implemented on the field-programmable gate array device, and the generated random sequence can successfully pass NIST SP800-22 and TestU01 tests. the TRNG designed in this paper first introduces the Logistic map as the control module and has a high output frequency. © 2021 IEEE.

关键词： Ring oscillators Jitter NIST Frequency conversion Generators Frequency control Field programmable gate arrays

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Computer Vision and Its Algorithmic Evolution in Image Understanding 7

Computer Vision and Its Algorithmic Evolution in Image Under...

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

作者： Smitha, B.S. Jain, Alok Sreenivasulu, D. Sharma, Saurabh Reddy, B. Ravali Al-Rubaye, Taqi Mohammed Khattab New Horizon College of Engineering Department of Automobile Engineering Bangalore India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201314 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India The Islamic University College of Medical Technologies Department of Medical Laboratory Technology Najaf Iraq

ISBN: (纸本)9798350350067

As computer vision systems have improved, mostly through machine vision progress, people's views on pictures have changed a lot. In response to this change, the Dynamic AdaptNet project is making an advanced system for analysing pictures that uses data from the surroundings. the manuscripts goals is to offer a full range of features, such as dynamic convolutional neural networks (CNNs), adaptable segmentation, and temporal attention. Standard, Baseline, Enhanced, Advanced, Optimised, and Specialised are some of the choices for the system. Standard is the easiest and most important to use. this study compares and contrasts different methods by checking how well they work based on several factors, such as success, adaptability, and resilience. Independent reviews and comparisons show that Dynamic AdaptNet is a complete tool for real-time picture analysis. this shows that it has the ability to make computer vision better. © 2024 IEEE.

关键词： Convolutional neural networks

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