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检索条件"主题词=Classification Algorithms"

共 26354 条记录，以下是4821-4830 订阅

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classification of Cognitive Activities EEGs via Singular Spectrum Analysis and Joint Approximate Diagonalization of Eigen-Matrices Algorithm with Ensemble Classifier

Classification of Cognitive Activities EEGs via Singular Spe...

引用

IEEE Symposium on Product Compliance Engineering - Asia (ISPCE-CN)

作者： Xiangyu Luo Bingo Wing-Kuen Ling Faculty of Information Engineering Guangdong University of Technology Guangzhou China

ISBN: (数字)9798331523008

ISBN: (纸本)9798331523015

Cognition involves the recognition and perception of external objects to solve creative problems and adapt to the environment, forming the foundation of human intelligence. However, neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) lead to cognitive impairment. Transcutaneous electrical nerve stimulation (TENS) is a recognized therapy for enhancing cognitive performance. This paper proposes using singular spectrum analysis (SSA) and the joint approximate diagonalization of eigen-matrices (JADE) algorithm with an ensemble classifier to classify electroencephalograms (EEGs) before and after TENS application during various cognitive activities. Initially, subjects wear a headband to acquire single-channel EEGs before and after TENS is applied to their radial nerves. Empirical mode decomposition (EMD) is then used to suppress underlying EEG trends. Subsequently, SSA and the JADE algorithm perform blind source separation (BSS) of EEGs from electromyograms (EMGs) and electrocardiograms (ECGs). JADE is a fourth-order based independent component analysis (ICA) algorithm. The tunable Q-factor wavelet transform (TQWT) decomposes EEGs into subbands from which features are extracted. Finally, an ensemble classifier, combining random forest and least squares support vector machine (LS-SVM), classifies the data. The computer numerical simulation results demonstrate that our proposed method achieves higher classification accuracy compared to existing methods.

关键词： Support vector machines Q-factor Accuracy Approximation algorithms Feature extraction Electroencephalography Blind source separation classification algorithms Random forests Spectral analysis

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An Empirical Study of Sentiment Analysis on Movie Review using Machine Learning based classification Approach

An Empirical Study of Sentiment Analysis on Movie Review usi...

引用

International Conference on Information Systems and Computer Networks (ISCON)

作者： Vikram Rajpoot Ruchi Agrawal Ankur Chaturvedi Kunal Goyal GLA University Mathura

ISBN: (纸本)9781665447874

Sentiment analysis is the investigation of feelings and conclusions from any type of text. It is additionally named as assessment. This is extremely helpful to communicate the assessment of the gathering person. The strategy is utilized to discover the estimation of the individual as for a given wellspring of substance. The web-based platform consists of gigantic measure information as tweets, websites. In this paper, we have presented the sentiment analysis approaches on Movie review dataset using different procedures like Naïve Bayes, K-Nearest Neighbour and Random Forest.

关键词： Sentiment analysis Machine learning algorithms Films Estimation Motion pictures Computer networks classification algorithms

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Soft set and Fuzzy Rules Enabled SVM Approach for Heart Attack Risk classification Among Adolescents

Soft set and Fuzzy Rules Enabled SVM Approach for Heart Atta...

引用

International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： C. M Chethan Malode K. Bhargavi B. G Gunasheela G. Kavana R. Sushmitha Department of Information Science and Engineering Siddaganga Institute of Technology Tumkur Karnataka India

ISBN: (纸本)9781538652589;9781538652572

Nowadays heart attack is becoming more common among adolescents due to several reasons like heavy work pressure, lack of physical activities, irregular food habits, obesity, eating high fat food, smoking, sedentary life style, heavy drinking and so on. It has been reported that above ten lakh adolescents die every year due to heart related diseases. In literature maximum number of works have been carried out to address heart attack problem among old age people, however there are very less works concentrating on the heart attack detection focusing only adolescents. Hence in this paper the focus is towards adolescents' heart attack risk detection and classification. This is achieved by proposing a novel architecture which detects chances of heart attack in early stages using soft set theory and fuzzy rules. The performance of the proposed architecture is found to be good with respect to several parameters like accuracy, delay and efficiency.

关键词： Cardiac arrest Support vector machines Heart Computer architecture classification algorithms Electrocardiography

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Performance analysis of artificial neural network and K Nearest neighbors image classification techniques with wavelet features

Performance analysis of artificial neural network and K Near...

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International Conference on Computer and Communications Security (ICCCS)

作者： Dharmendra Patidar Nitin Jain Ashish Parikh Electronics and commununication (M.tech Pursuing) Mandsaur institute of Technology Mandsaur India Electronics and commununication (Asst. Prof.) Mandsaur institute of Technology Mandsaur India Electronics and commununication (H.O.D.) Mandsaur institute of Technology Mandsaur India

In present day classification of multi class image play an important role in engineering and computer vision application like image processing in biomedicai, retrieval of content based image. From some past years researchers and scientists have made a lot of efforts in implementation of an advanced image classification approaches [5, 6, 7, 8, 9, and 10]. The classification of images is a challenging and important task nowadays. In this propose method our objective is to successfully classify an image from given large image data base. Image features which contained most important information for successful classification is extract by using Haar wavelet and Daubechies wavelet (db4) wavelet discrete Mayer wavelet (demy). In this proposed method received image features are first used with ANN for training and testing and then used same image features of different wavelet transform for KNN training testing. Finally we evaluate the performance of both ANN and KNN classifier with different wavelet Features. Highest classification efficiency is received with Dmey based ANN classifier. Proposed work shows an new application and its directly contributes towards image classification. The complete work is experimented in Mat lab 201 1b using real world dataset.

关键词： Wavelet transforms Artificial neural networks Image classification Training classification algorithms Wavelet analysis

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classification and recognition of ballistic microcephalus based on deep neural network

Classification and recognition of ballistic microcephalus ba...

引用

International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Tiantian Cai Yijun Sheng Zi He Wenfeng Sun School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China National Key Laboratory of Science and Technology on space Microware Xian China

ISBN: (数字)9780996007894

ISBN: (纸本)9781728146331

To solve the problem of indistinguishable noncooperative targets and different types of micromotion in the space, the electromagnetic scattering cross-sectional area (RCS) sequence of the target to be detected was obtained by radar measurement, and the classification and identification of multiple targets with same shapes and different types of fretting form was conducted by deep neural network, and the recognition rate is higher. Firstly, a series of RCS characteristics such as mean value and period are extracted according to the target electromagnetic scattering cross-sectional area (RCS) sequence. These features are used as input of neural network classifier, and BP neural network algorithm is used to classify these targets. The experimental results show that the method based on multi-layer neural network has higher recognition rate than single-layer network, and it can improve the recognition rate of warheads with different types of fretting greatly.

关键词： Feature extraction Target recognition Biological neural networks Radar cross-sections classification algorithms

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A Road map for Age and Gender classification using EL-Methods

A Road map for Age and Gender Classification using EL-Method...

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Recent Advances in Information Technology for Sustainable Development (ICRAIS), International Conference on

作者： K. Prakash M. Sudharsan G Meenakashi Margaret Marry T Department of Computer Science Krist Jayanti College Autonomous Bengaluru Karnataka Department of Computer Science Kristu Jayanti College Autonomous Bengaluru Karnataka Department of Computer Science Justice Basheer Ahmed Sayeed College for Women Chennai Tamilnadu

ISBN: (数字)9798350354461

ISBN: (纸本)9798350354522

Age and Gender analysis is very crucial to prediction for blind people. This variety of predictions is particularly used for blind people. Gender and age classification relies on distinct attributes for their determination. The gender classification with the help of facial expression features, height, and body shape. Similarly, age classification is performed with the help of wrinkles, skin texture, and facial features. Different ML method are used to predict gender and age classification. In this work put forward, an EL-based machine learning algorithm is in use to anticipate and age classification. The SVM algorithm is used for the grouping of gender and age grouping. Based on the boosting ensemble learning with the help of SVM, performed better classification of age and genderprediction. This novelty method has been incorporated into ensemble learning with implemented using 1056 images with different pixel intensity values after prepossessing all images get into the same, and based on that, training and testing are evaluated at 80% and 20%. The proposed ensemble method produced better accuracy of age (97%) and gender (96%) grouping.

关键词： Training Support vector machines Biometrics Machine learning algorithms Accuracy Computational modeling Prediction algorithms classification algorithms Ensemble learning Testing

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Driver's cognitive state classification toward brain computer interface via using a generalized and supervised technology

Driver's cognitive state classification toward brain compute...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chun-Hsiang Chuang Pei-Chen Lai Li-Wei Ko Bor-Chen Kuo Chin-Teng Lin Institute of Electrical Control Engineering and Brain Research Center National Chiao Tung University Hsinchu Taiwan Graduate Institute of Educational Measurement and Statistics National Taichung University Taichung Taiwan Department of Biological Science and Technology and Brain Research Center National Chiao Tung University Hsinchu Taiwan

Growing numbers of traffic accidents had become a serious social safety problem in recent years. The main factor of the high fatalities was the obvious decline of the driver's cognitive state in their perception, recognition and vehicle control abilities while being sleepy. The key to avoid the terrible consequents is to build a detecting system for ongoing assessment of driver's cognitive state. A quickly growing research, brain-computer interface (BCI), offers a solution offering great assistance to those who require alternative communicatory and control mechanisms. In this study, we propose an alertness/drowsiness classification system based on investigating electroencephalographic (EEG) brain dynamics in lane-keeping driving experiments in a virtual reality (VR) driving environment with a motion platform. The core of the classification system is composed of dimension reduction technique and classifier learning algorithm. In order to find the suitable method for better describing the data structure, we explore the performances using different feature extraction and feature selection methods with different classifiers. Experiment results show that the accuracy is over 80% in most combinations and even near 90% under Principal Component Analysis (PCA) and Nonparametric Weighted Feature Extraction (NWFE) going with Gaussian Maximum Likelihood classifier (ML) and k-Nearest-Neighbor classifier (kNN), respectively. In addition, this developed classification system can also solve the individual brain dynamic differences caused from different subjects and overcome the subject dependent limitation. The optimized solution with better accuracy performance out of all combinations can be considered to implement in the kernel brain-computer interface.

关键词： Feature extraction Driver circuits Electroencephalography classification algorithms Brain modeling Principal component analysis Support vector machines

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Plant foliage Recognition based on classification using Artificial Neural Network

Plant foliage Recognition based on Classification using Arti...

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International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)

作者： Prashanth G K Virupakshaiah H K Bharathi P T Asha Gowda Karegowda Tejaswini K M Kiran K Department of MCA SIT Tumkur Karnataka India Department of Mathematics SSIT Tumkur Karnataka India Department of MCA SIT Tumkut Karnataka India

The state-of-the-art method to find the pictographic four types of foliage (flower, fruit, medical and tree) identification is proposed. Foliage is represented by a boundary of local feature using edge detection, followed by applying convex hull algorithm. In the second phase, ANN has been applied for simulating the system using the features identified in first phase. The proposed work resulted in an average identification rate of 96.75% and 94% with training and test data.

关键词： Training Computer science Image edge detection Artificial neural networks Feature extraction classification algorithms Biomedical imaging

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Simultaneous feature selection and feature weighting with K selection for KNN classification using BBO algorithm

Simultaneous feature selection and feature weighting with K ...

引用

Conference on Information and Knowledge Technology

作者： Ahmad A. Kardan Atena Kavian Amir Esmaeili Department of Computer Engineering and Information Technology Amirkabir University of Technology Tehran Iran

K nearest neighbor algorithm (K-NN) is considered as one of the machine learning algorithms for data classification. This algorithm suffers of some disadvantages such as sensitivity to the distance function, K value selection and high computational complexity (time and spatial). In this paper, a novel hybrid approach is proposed for simultaneous feature selection and feature weighting with k value selection of K-NN rule based on Biogeography based optimization (BBO). The 6 evolutionary algorithms and 14 non-evolutionary algorithms are used to compare and evaluate with the novel proposed algorithm (BBO-KNN). The experimental results signify that the BBO-KNN has higher efficiency compared to other methods and is led to higher classification rate as well as effective data dimension reduction.

关键词： classification algorithms Accuracy Algorithm design and analysis Machine learning algorithms Biological cells Sensitivity Data models

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Associative classification over Data Streams

Associative Classification over Data Streams

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International Conference on Information Engineering and Computer Science (ICIECS)

作者： Zhen-Hui Song Yi Li Department of Computer ShiJiaZhuang Vocational Technology Institute Shijiazhuang China

Based on association rules, Associative classification (AC) has shown great promise over many other classification techniques on static dataset. However, the increasing prominence of data streams arising in a wide range of advanced application has posed a new challenge for it. This paper describes and evaluates AC-DS, a new associative classification algorithm for data streams which is based on the estimation mechanism of the Lossy Counting (LC) and landmark window model. We apply AC-DS to mining several datasets obtained from the UCI Machine Learning Repository and the result show that the algorithm is effective and efficient.

关键词： classification algorithms Itemsets Association rules Algorithm design and analysis Machine learning algorithms Accuracy

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