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检索条件"任意字段=4th International Conference on Machine Learning and Data Minining in Pattern Recognition"

共 1791 条记录，以下是1231-1240 订阅

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Real Time Direction-Sensitive Fall Detection System Using Accelerometer and learning Classifier 4

Real Time Direction-Sensitive Fall Detection System Using Ac...

引用

4th international conference on Advances in Electrical Engineering (ICAEE)

作者： Hossain, Shaikh Farhad Islam, Md. Zahurul Ali, Md. Liakot Bangladesh Univ Engn & Technol Inst Informat & Commun Technol Dhaka 1205 Bangladesh Bangladesh Univ Engn & Technol Dept Elect & Elect Engn Dhaka 1205 Bangladesh

ISBN: (纸本)9781538608692

Continuous fall recognition framework screens the day by day action of particularly elderly individuals to enroll somebody's assistance as quick as conceivable if there should be an occurrence of crisis. this paper presents a real-time fall detection using a single 3D commercial accelerometer (3DCA) and support vector machine learning algorithm (SVMLA). In past, two machine learning (ML) based calculations SVMLA and k-Nearest Neighbors (K-NN) were executed for mandate fall discovery in reproduction. Among the two strategies, SVMLA give better exhibitions which prompts 96.45% of exactness utilizing PCA mean and standard deviation highlights, surpassing the exhibitions detailed in the writing. the performances of the developed system in real time are also evaluated and they are found same accuracy, precision and recall. When applied to experimental data from 13 male subjects, the real time system discriminates between falls and activities of daily living (ADL) with same level like simulation. the system utilizes privacy preserving sensor. the system is reliable, user friendly and cost effective with less technical error rate and high classification accuracy.

关键词： ADL 3DCA SVMLA K-NN WBSN IDIL WS

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Product Surface Defect Detection Based on Deep learning

Product Surface Defect Detection Based on Deep Learning

引用

IEEE international Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Po Chun Lien Qiangfu Zhao Aizu Daigaku Aizuwakamatsu Fukushima JP Graduate School of Computer Science and Engineering The University of Aizu Aizuwakamatsu-city Fukushima Japan

Image classification is a branch of computer vision that uses a computer to acquire image data and interpret them by mimicking human biological systems. this is a very important topic in today's situation because every second a large number of image data are acquired and used for various purposes around the world. One application of image classification is to detect defects on the surfaces of industrial products. Quality inspection is usually the final stage in a production line, and so far it is mainly conducted by human experts. this can be time consuming and mistake-prone. In this study, we investigate the possibility of replacing, fully or partially, human experts with a machine learner when the product defects are visible. In this study, we investigate several methods based on deep learning. the first one is to use a deep learner directly to detect the existence of defects in a given product surface image. the second one segments suspected parts first and then uses the deep learner to classify the segmented parts. the third method employs an ensemble of deep learners. Results show that the third method can provide the best results, and can be practically useful if we introduce a proper rejection mechanism.

关键词： Support vector machines Feature extraction Inspection Kernel Convolution Training pattern recognition

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recognition of Ayurvedic Medicinal Plants from Leaves: A Computer Vision Approach 4

Recognition of Ayurvedic Medicinal Plants from Leaves: A Com...

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4th international conference on Image Information Processing (ICIIP)

作者： Sabu, Amala Sreekumar, K. Nair, Rahul R. Coll Engn Poonjar Dept Comp Sci Poonjar Kerala India Tata Consultancy Serv Robot & Cognit Syst Grp Kochi Kerala India

ISBN: (纸本)9781509067343

Plants are an indispensable part of our ecosystem and India has a long history of using plants as a source of medicines. Since the advent of modern allopathic medicine, the use of traditional medicine declined to a considerable extent. However, in recent years, traditional medicine has made a comeback for a variety of reasons like they are inexpensive, nontoxic and does not impact any side effect. Different kind of medicinal plant species are available on earth but it is very difficult to identify the plant. Considerable knowledge accumulated by the villagers and tribal on medicine from plants remains unknown to the scientists and urban people. this kind of knowledge is usually handed down through generations. Our immediate concern is to preserve this knowledge in digital form through the concepts of machine learning, pattern recognition and computer vision. A machine can identify a medicinal plant through the features extracted from the leaf images, together with a classification algorithm. this paper proposes a computer vision approach for the recognition of ayurvedic medicinal plant species found in Western Ghats of India the proposed system uses a combination of SURF and HOG features extracted from leaf images and a classification using k-NN classifier. Our experiments show results which seem to be sufficient for building apps for real life use.

关键词： Plant recognition Leaf recognition HOG SURF Computer Vision machine learning

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Proceedings - 2017 5th international conference on Applied Computing and Information Technology, 2017 4th international conference on Computational Science/Intelligence and Applied Informatics and 2017 1st international conference on Big data, Cloud Computing, data Science and Engineering, ACIT-CSII-BCD 2017

Proceedings - 2017 5th International Conference on Applied C...

引用

5th international conference on Applied Computing and Information Technology, 2017 4th international conference on Computational Science/Intelligence and Applied Informatics and 2017 1st international conference on Big data, Cloud Computing, data Science and Engineering, ACIT-CSII-BCD 2017

ISBN: (纸本)9781538633021

the proceedings contain 44 papers. the topics discussed include: a learning material for physics experiment with high-accuracy using computer vision technique;automatic classification of remarks in werewolf BBS;a rapid incremental frequent pattern mining algorithm for uncertain data;global and local bursts detection in streaming data;two-mode three-way dominance points model for periodic dissimilarity;an intelligent noninvasive taste detection app for watermelons;automated risk identification of CMMI project planning using ontology;and depth recognition in 3D translucent stereoscopic imaging of medical volumes by means of a glasses-free 3d display.

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IEEE 4th international conference on Soft Computing and machine Intelligence, ISCMI 2017

IEEE 4th International Conference on Soft Computing and Mach...

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4th IEEE international conference on Soft Computing and machine Intelligence, ISCMI 2017

ISBN: (纸本)9781538613146

the proceedings contain 40 papers. the topics discussed include: quality assessment of large scale dimensionality reduction methods;integrating data science and R programming at an early stage;improvement algorithms of perceptually important point identification for time series data mining;a large-scale filter method for feature selection based on Spark;applying machine learning to big data streams-an overview of challenges;use of reliability engineering concepts in machine learning for classification;interactive concepts for shaping generative models of spatial behavior;an enhanced clustering analysis based on glowworm swarm optimization;change reaction strategies for DNSGA-II solving dynamic multi-objective optimisation problems;variation of ant colony optimization parameters for solving the travelling salesman problem;playing the game of snake with limited knowledge: unsupervised neuro-controllers trained using particle swarm optimization;feature selection for an SVM based webpage classifier;accessibility and search engine optimization on scalable vector graphics;emotional journey for an emotion provoking cycling exergame;high-speed human detection in top-view images by feature scaling for informed filters;fast algorithm for isolated words recognition based on hidden Markov model stationary distribution;automatic identification of wake vortex traverse by transport aircraft using fuzzy logic;and study on automatic tracking algorithm of neurons in most atlas of mouse brain based on open curve snake.

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Opinion Mining Using Support Vector machine with Web Based Diverse data 7th

Opinion Mining Using Support Vector Machine with Web Based D...

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7th international conference on pattern recognition and machine Intelligence (PReMI)

作者： Sabuj, Mir Shahriar Afrin, Zakia Hasan, K. M. Azharul Khulna Univ Engn & Technol Dept Comp Sci & Engn Khulna Bangladesh Northern Univ Bangladesh Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9783319699004;9783319698991

Opinions of other people always carry a very important source of information that has a major impact on the entire decision making process. With the emerging availability and popularity of online reviews, opinions, feedback and suggestions, people now actively employ these views for better decision making. Opinion mining is a natural language processing and information extraction task that aims to examine people's opinions, sentiments, emotions and attitudes about a product. this paper presents an opinion classifier based on Support Vector machines (SVM) algorithm that can be used to analyze data for classifying opinions. We design a classifier to determine opinion from Bangla text data. We evaluate the performance and analyze comparative results.

关键词： machine learning Supervised learning Opinion mining Support vector machine Sentiment detection Opinion strength Text classification

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Improving the Performance of Deep learning Based Speech Enhancement System Using Fuzzy Restricted Boltzmann machine 7th

Improving the Performance of Deep Learning Based Speech Enha...

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7th international conference on pattern recognition and machine Intelligence (PReMI)

作者： Samui, Suman Chakrabarti, Indrajit Ghosh, Soumya K. Indian Inst Technol Kharagpur Kharagpur 721302 W Bengal India

ISBN: (纸本)9783319699004;9783319698991

Supervised speech enhancement based on machine learning is a new paradigm for segregating clean speech from background noise. the current work represents a supervised speech enhancement system based on a robust deep learning method where the pre-training phase of deep belief network (DBN) has been conducted by employing fuzzy restricted Boltzmann machines (FRBM) instead of regular RBM. It has been observed that the performance of FRBM model is superior to that of RBM model particularly when the training data is noisy. Our experimental results on various noise scenarios have shown that the proposed approach outperforms the conventional DNN-based speech enhancement methods which use regular RBM for unsupervised pre-training.

关键词： Speech enhancement Deep learning Deep neural network Restricted Boltzmann machine (RBM) Fuzzy restricted Boltzmann machine (FRBM) Unsupervised pre-training Speech quality Speech intelligibility

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Formulation of Two Stage Multiple Kernel learning Using Regression Framework 7th

Formulation of Two Stage Multiple Kernel Learning Using Regr...

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7th international conference on pattern recognition and machine Intelligence (PReMI)

作者： Shiju, S. S. Salim, Asif Sumitra, S. Indian Inst Space Sci & Technol Dept Math Thiruvananthapuram Kerala India

ISBN: (纸本)9783319699004;9783319698991

Multiple kernel learning (MKL) is an approach to find the optimal kernel for kernel methods. We formulated MKL as a regression problem for analyzing the regression data and hence the data modeling problem involves the computation of two functions, namely, the optimal kernel function which is related with MKL and the optimal regression function which generates the data. As such a formulation demands more space requirements supervised pre-clustering technique has been used for selecting the vital data points. We used two stage optimization for finding the models, in which, the optimal kernel function is found in the first stage and the optimal regression function in the second stage. Using kernel ridge regression the proposed method had been applied on real world problems and the experimental results were found to be promising.

关键词： Multiple kernel learning Regression Kernel ridge regression

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A Matrix Factorization & Clustering Based Approach for Transfer learning 7th

A Matrix Factorization & Clustering Based Approach for Trans...

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7th international conference on pattern recognition and machine Intelligence (PReMI)

作者： Devi, V. Sowmini Padmanabhan, Vineet Pujari, Arun K. Univ Hyderabad Sch Comp & Informat Sci Hyderabad India

ISBN: (纸本)9783319699004;9783319698991

Recommender systems that make use of collaborative filtering tend to suffer from data sparsity as the number of items rated by the users are very small as compared to the very large item space. In order to alleviate it, recently transfer learning (TL) methods have seen a growing interest wherein data is considered from multiple domains so that ratings from the first (source) domain can be used to improve the prediction accuracy in the second (target) domain. In this paper, we propose a model for transfer learning in collaborative filtering wherein the latent factor model for the source domain is obtained through Matrix Factorization (MF). User and Item matrices are combined in a novel way to generate cluster level rating pattern and a Code Book Transfer (CBT) is used for transfer of information from source to the target domain. Results from experiments using benchmark datasets show that our model approximates the target matrix well.

关键词： Collaborative filtering

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Tree-Based Structural Twin Support Tensor Clustering with Square Loss Function 7th

Tree-Based Structural Twin Support Tensor Clustering with Sq...

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7th international conference on pattern recognition and machine Intelligence (PReMI)

作者： Rastogi, Reshma Sharma, Sweta South Asian Univ Fac Math & Comp Sci Dept Comp Sci New Delhi India

ISBN: (纸本)9783319699004;9783319698991

Most of the real-life applications involving images, videos etc. deals with matrix data (second order tensor space). Tensor based clustering models can be utilized for identifying patterns in matrix data as they take advantage of structural information in multi-dimensional framework and reduce computational overheads as well. Despite such numerous advantages, tensor clustering has still remained relatively unexplored research area. In this paper, we propose a novel clustering technique, termed as Treebased Structural Least Squares Twin Support Tensor Clustering (Tree-SLSTWSTC), that builds a cluster model as a binary tree, where each node comprises of proposed Structural Least Squares Twin Support Tensor machine (S-LSTWSTM) classifier that considers the structural risk minimization of data alongside a symmetrical L2-norm loss function. the proposed approach results in time-efficient learning. Initialization framework based on tensor k-means has been proposed and implemented in order to overcome the instability disseminated by random initialization. To validate the efficacy of the proposed framework, computational experiments have been performed with relevant tensor based models on face recognition and optical digit recognition datasets.

关键词： Twin Support Tensor machine Unsupervised learning Tree-based clustering Tensor space

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