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共 117 条记录，以下是21-30 订阅

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EEG-based Confusion recognition Using Different machine learning Methods 2

EEG-based Confusion Recognition Using Different Machine Lear...

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2nd international conference on Artificial Intelligence and Computer Engineering, ICAICE 2021

作者： He, Shuwei Xu, Yanran Zhong, Lanyi School of Electronic Engineering Xidian University Xian China College of Medicine and Biological Information Engineering Northeastern University Shenyang China International School Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665421867

Massive Open Online Course (MOOC) has emerged as a key trend. As a way of teaching online, the main shortcoming of MOOC is lacking feedback because there is a distance in both time and space between teachers and students. This study proposes the confusion recognition system based on Electroencephalography(EEG). We apply machine learning methods, including Naive Bayes, KNN, Random Forest, XGBoost, and also a deep learning method, LSTM, on the EEG data set respectively to detect whether a student feel confused. We find that LSTM shows better performance than any machine learning methods we use. The average accuracy of LSTM classifier is 78.1%. This study shows the significance of detecting confusion through EEG and helping students in improving learning efficiency. © 2021 ieee.

关键词： Brain computer interface

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Analyses of the Technological Accumulation over the 2nd and the 3rd AI Boom and the Issues Related to AI Adoption by Firms

Analyses of the Technological Accumulation over the 2nd and ...

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Portland international conference on Management of Engineering and Technology (PICMET) - Managing Technological Entrepreneurship - The Engine for Economic Growth

作者： Miyazaki, Kumiko Sato, Ryusuke Tokyo Inst Technol Dept Technol & Innovat Management Tokyo Japan IBM Corp Global Business Serv Watson Delivery 2 Tokyo Japan

ISBN: (纸本)9781890843373

Although AI was proposed by *** back in 1956, 60 years later, we are beginning to witness a surge of interest in the practical applications of AI in many sectors. However, the actual adoption rate of AI in businesses has been quite low. The current 3rd AI boom follows the 2nd AI boom in the 1980s which focused mainly on expert systems. In this paper, an attempt is made to analyze the technological accumulation process from the 2nd to the 3rd AI boom and to identify forthcoming key application areas. The methodology is based on a 3 step approach. In the first step, a bibliometric analyses of Artificial Intelligence was carried out to analyze the technological accumulation during the 2nd and the 3rd AI boom. Having done the desk work and interviewed several experts in AI, we decided that 2013 was the year which marked the boundary between the two AI booms. A bibliometric analyses based on countries and institutions over the 2 periods was followed by co-occurrence analyses of the author keywords in the 2 periods, before and after 2013. In the third stage, interviews were carried out with some corporate members to do a qualitative analysis on the possible application areas of AI and issues to be solved for adopting AI were identified. The results showed that in the 3rd AI boom, machine learning, deep learning, genetic algorithm have been identified as the key technologies. Furthermore, prediction, forecasting, data mining, fault diagnosis, pattern recognition were identified as important areas in the 3rd AI boom. It was also revealed that firms' R&D has been changing to focus more on AI applications.

关键词： machine learning Bibliometrics Companies Neural networks Market research Expert systems

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Hybrid PCA-SVM method for pattern recognition of chatter gestation

Hybrid PCA-SVM method for pattern recognition of chatter ges...

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2011 2nd international conference on Digital Manufacturing and Automation, ICDMA 2011

作者： Shao, Qiang Feng, Chang-Jian Li, Wenlong University of Dalian Nationalities Dalian Lianning 116600 China

ISBN: (纸本)9780769544557

To distinguish chatter gestation, chatter recognition method based on hybrid PCA(Principal Compenent Analysis) and SVM(Support Vector machine) is proposed for dynamic patterns of chatter gestation in cutting process. At first, FFT features are extracted from the vibration signal of cutting process, then FFT vectors are presorted and introduced to PCA-SVM for machine learning and classification. Finally the results of chatter gestation recognition and chatter prediction experiments are presented and show that the method proposed is effective. © 2011 ieee.

关键词： Support vector machines

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Energy Consumption Forecasting using a Deep learning Energy-Level Based Prediction 2

Energy Consumption Forecasting using a Deep Learning Energy-...

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2nd ieee international Power and Renewable Energy conference (IPRECON)

作者： Rane, Rahul Desai, Maitraya Pandey, Abhishek Kazi, Faruk VJTI Elect Engn Dept Ctr Excellence CoE Complex & Nonlinear Dynam Syst Mumbai Maharashtra India

ISBN: (纸本)9781665401371

Smart grids benefit the consumers via enabling informed participation. As a result, customers have more choices, which drives a range of buying trends and actions. One such important application is accurate forecasts of energy demands (loads) at individual sites, but load forecasting is still a challenge. Most load forecasting research has used traditional numerical value estimation, which is a regression style prediction;however, energy consumers will not know if the forecasted load is low or high. Level prediction can be performed by performing regression first and then classifying the values into energy levels, or by using a classification model to predict the levels directly. The Random Forest (RF) classification algorithm is shown to have outperformed other machine learning algorithms for level predictions. This paper provides a deep learning-based classification approach. The results of this deep learning-based classification were compared to those of the RF classification algorithm, it was observed that the deep learning method outperformed the RF method, particularly for high numbers of energy levels.

关键词： Convolutional Neural Networks (CNN) Deep Neural Networks (DNN) Energy consumption prediction Energy level classification Load forecasting machine learning Ordinal binning pattern recognition Random Forest (RF) Smart grid Smart meter

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Research on Technology of Eye State recognition Based on Gabor Wavelet Transform and pattern Classification 2

Research on Technology of Eye State Recognition Based on Gab...

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2021 2nd international conference on machine learning and Computer Application, ICMLCA 2021

作者： Feng, Yanjun Liu, Jun Song, Kai School of Information Science and Engineering Shenyang Ligong University Shenyang China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (纸本)9783800757398

Fatigue driving can be detected through human eye state recognition. A methodology was presented in this paper for recognizing the state of human eyes by using Gabor Wavelet Transform (GWT) and pattern recognition. Firstly, human face was detected by skin color Gaussian model. Then the position of eyes was located by combining Gabor filtering and integral projection. After that, the feature vector was constructed by extracting the amplitudes of key points in the Gabor amplitude image. Finally, prognostic classification was implemented with a Support Vector machine (SVM) classifier. The experimental results indicated that this eye state recognition method makes full use of direction and scale characteristics of GWT and reduces the dimension of feature vector, which not only simplifies the algorithm, but also has a high rate of recognition. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Support vector machines

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Assessment of System Vulnerability for Smart Grid Applications 2

Assessment of System Vulnerability for Smart Grid Applicatio...

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2nd ieee international conference on Engineering and Technology (ICETECH)

作者： Parate, Madhumita Tajane, Sachin Indi, Bhagyashri VJTI Dept Elect Engn Mumbai Maharashtra India

ISBN: (纸本)9781467399166

The smart grid is an electrical grid that has a duplex communication. This communication is between the utility and the consumer. Digital system, automation system, computers and control are the various systems of Smart Grid. It finds applications in a wide variety of systems. Some of its applications have been designed to reduce the risk of power system blackout. Dynamic vulnerability assessment is done to identify, quantify, and prioritize the vulnerabilities in a system. This paper presents a novel approach for classifying the data into one of the two classes called vulnerable or non-vulnerable by carrying out Dynamic Vulnerability Assessment (DVA) based on some data mining techniques such as Multichannel Singular Spectrum Analysis (MSSA), and Principal Component Analysis (PCA), and a machine learning tool such as Support Vector machine Classifier (SVM-C) with learning algorithms that can analyze data. The developed methodology is tested in the ieee 57 bus, where the cause of vulnerability is transient instability. The results show that data mining tools can effectively analyze the patterns of the electric signals, and SVM-C can use those patterns for analyzing the system data as vulnerable or nonvulnerable and determines System Vulnerability Status.

关键词： Data Mining DVA ICT machine learning MSSA pattern recognition PCA Power System Blackout Security Smart Grids SVM TSA

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Individual Identification of QPSK Modulation Communication Radiator Based on ResNet-18 2

Individual Identification of QPSK Modulation Communication R...

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2nd international conference on machine learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023

作者： Peng, Runlong Gao, Hang Long, Han College of Electronic Science National University of Defense Technology Changsha China College of Science National University of Defense Technology Changsha China

ISBN: (纸本)9798350328530

With the increasing number of wireless communication devices, the electromagnetic spectrum is becoming increasingly crowded. In order to enhance the ability in monitoring the electromagnetic spectrum and improve the efficiency of spectrum utilization, it is necessary to strengthen the research on the individual identification technology of the communication radiation source. To solve the problem of individual identification of QPSK modulated communication radiators, a deep learning method is introduced. The input of residual neural network is signal spectrum and time domain sampling, and the individual number of radiators is used as label for individual identification of radiators. © 2023 ieee.

关键词： pattern recognition

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Neural Network Based Iris pattern recognition System Using Discrete Walsh Hadamard Transform Features

Neural Network Based Iris Pattern Recognition System Using D...

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2nd international conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Khedkar, Mrunal M. Ladhake, S. A. Sipna Coll Engg & Tech Amravati 444605 Maharashtra India

ISBN: (纸本)9781479924325;9781479926596

An iris pattern recognition system is developed for CASIA database using discrete 2D Walsh Hadamard (WH) transform as a tool for feature extraction. Experimental prototype pattern recognition (PR) system is designed in which iris images of ten different persons are given as input to the system. After localizing region of interest (ROI), features are extracted with respect to image statistics, texture and 2D WH transform domain. Based upon these features, an optimal feature vector comprising of only 23 features is selected and it is given as input to neural network based pattern recognition (PR) system. Two different neural network configurations including Multi Layer Perceptron (MLP), Radial Basis Function (RBF) and a different learning machine, known as Support Vector machine (SVM) are investigated for their suitability as a PR system. It is observed that MLP neural network based PR system comprising of only one hidden layer containing 20 processing elements (PEs) outperforms others in respect of performance measures on cross-validation (CV) dataset.

关键词： Multi layer Perceptron pattern recognition system Radial Basis Function Support vector machine Walsh Hadamard Transform

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Predicting the Price Direction of Bitcoin Using Twitter Data and machine learning 2

Predicting the Price Direction of Bitcoin Using Twitter Data...

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2nd ieee international conference on Data Science and Computer Application, ICDSCA 2022

作者： Kanji, Abdul Mannan Chaudhary, Ishita Shankar, Rithika Lakshmi Srinivasa, Gowri Pes University Pes Center for Pattern Recognition Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

Bitcoin is a decentralized digital currency that was intro- duced in 2009 and since then, has become increasingly popular as one of the most known and highly valued currencies. Contributing factors to its rise include crypto Twitter influencers. An engaged audience on Twitter seems to have an influence on the cryptocurrency market. In this paper, we analyze the impact that tweets have on the price of Bitcoin. Using word-clouds and candlestick plots, we gain insight into the factors that affect Bitcoin prices. We also use various machine learning techniques to automatically classify the sentiment in Tweets related to cryptocurrencies. We incorporate these and other relevant features to build and compare the performance of multiple machine learning models to predict the direction (increase or decrease) of the price of Bitcoin. © 2022 ieee.

关键词： Forecasting

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Research on Teaching Reform of 'Motor and Drive'Based on Matlab Simulation 2

Research on Teaching Reform of 'Motor and Drive'Based on Mat...

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2nd international conference on Education, Knowledge and Information Management, ICEKIM 2021

作者： Chengwei, Zhang Yihua, Liang Liu, Chao Yang, Shengyi Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province Guizhou Minzu University Gui Yang China

ISBN: (纸本)9781728168340

'Motor and Drive' is a professional basic course with strong theory and wide application in practical engineering. Its theory is abstract and formula is complex, which is considered by teachers and automation students as one of the professional courses difficult to teach and learn. In order to better teaching and learning, teaching reform is provided. The teaching content is optimized, the teaching methods, experimental methods and assessment methods are reformed based on matlab simulation. It is helpful to improve the teaching quality, stimulate students' learning enthusiasm and solve practical problems. The teaching reform foundation of 'motor and drive' is provided in this paper. © 2021 ieee.

关键词： Teaching

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