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An efficient density-based clustering for multi-dimensional database

An efficient density-based clustering for multi-dimensional ...

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4th international conference on Information, Cybernetics and Computational Social Systems (ICCSS)

作者： Zhang, Lieliang Li, Zhiyang Liu, Weijiang Qu, Wenyu Wu, Yinan Dalian Maritime Univ Coll Informat Sci & Technol Dalian Peoples R China Tianjin Univ Sch Comp Software Tianjin Peoples R China 91550 PLA Unit 87 Dalian Peoples R China

ISBN: (纸本)9781538632574

Cluster analysis aims at classifying data elements into different categories according to their similarity. It is a common task in data mining and useful in various field including pattern recognition, machine learning, information retrieval and so on. As an extensive studied area, many clustering methods are proposed in literature. Among them, some methods are focused on mining clusters with arbitrary shapes. However, when dealing with large-scale and multi-dimensional data, there is still a need for an efficient and versatile clustering method to identify these arbitrary shapes that may be embedded in these multidimensional space. In this paper, we propose a density-based clustering algorithm that adopts a divide-and-conquer strategy. To handle large-scale and multi-dimensional data, we first divide the data by grid cells. It is very efficient in large-scale cases where other algorithms often fail. Moreover, rather than tuning the grid cell width, we present a way to automatically determine the grid cell width. then, we propose a flood-filling like algorithm to identify the clusters with arbitrary shapes over these grid cells. Finally, extensive experiments are conducted in both synthetic databases and real-world databases, showing that the proposed algorithm efficiently finds accurate clusters in both low-dimensional and multi-dimensional databases.

关键词： Clustering Grid KNN Graph Large-scale data

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Facial Emotion recognition using Deep Convolutional Networks 4

Facial Emotion Recognition using Deep Convolutional Networks

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IEEE 4th international conference on Knowledge-Based Engineering and Innovation (KBEI)

作者： Mohammadpour, Mostafa Khaliliardali, Hossein Hashemi, Seyyed Mohammad R. AlyanNezhadi, Mohammad M. Islamic Azad Univ Qazvin Branch Dept Comp Engn Qazvin Iran Semnan Univ Elect & Comp Engn Dept Semnan Iran Shahrood Univ Technol Image Proc & Data Min Lab Shahrood Iran

ISBN: (纸本)9781538626405

Facial emotion recognition is an emerging field which use in many nowadays application including social robots, neuromarketing and games. Non-verbal communication methods like facial expressions, eye movement and gestures are used in many application of human computer interaction, which among them facial emotion is widely used because it convey the emotional states and feelings of persons. the emotion recognition is not an easy task because there is no landmark distinction between the emotions on the face and also there are a lot of complexity and variability. In the traditional machine learning algorithm some important extracted features used for modeling the face, so, it can not achieve high accuracy rate for recognition of emotion because the features are hand-engineered and depend on prior knowledge. Convolutional neural networks (CNN) have developed in this work for recognition facial emotion expression and classify them into seven basic categories. Instead of calculating hand-engineered features, CNN calculates features by learning automatically. the novelty of the proposed method is using facial action units (AUs) of the face which first these units are recognized by CNN and incorporate to recognizing the seven basic emotion states. To evaluated the proposed model, Cohn-Kanade database is used so that the model achieves the best accuracy rate 97.01 by incorporating AU while other works in the literature used a direct CNN and achieve accuracy rate 95.75.

关键词： Emotion Classification Convolutional neural networks Action Unit

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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...

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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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Incremental learning of Non-stationary Temporal Causal Networks for Telecommunication Domain 7th

Incremental Learning of Non-stationary Temporal Causal Netwo...

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

作者： Mohan, Ram Chaudhury, Santanu Lall, Brejesh Flytxt R&D Dept Thiruvananthapuram Kerala India Indian Inst Technol Dept EE Delhi India

ISBN: (纸本)9783319699004;9783319698991

In today's competitive telecommunication industry understanding the causes that influence the revenue is of importance. In a continuously evolving business environment, the causes that influence the revenue keeps changing. To understand and quantify the effect of different factors we model it as a non-stationary temporal causal network. To handle the massive volume of data, we propose a novel framework as part of which we define rules to identify the concept drift and propose an incremental algorithm for learning non-stationary temporal causal structure from streaming data. We apply the framework on a telecommunication operator's data and the framework detects the concept drift related to changes in revenue associated with data usage and the incremental causal network learning algorithm updates the knowledge accordingly.

关键词： Causes of effects Incremental learning Non-stationary causal networks

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Label Correlation Propagation for Semi-supervised Multi-label learning 7th

Label Correlation Propagation for Semi-supervised Multi-labe...

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

作者： Ghosh, Aritra Sekhar, C. Chandra Indian Inst Technol Madras Dept Comp Sci & Engn Madras Tamil Nadu India

ISBN: (纸本)9783319699004;9783319698991

Many real world machine learning tasks suffer from the problem of scarce labeled data. In multi-label learning, each instance is associated with more than one label as in semantic scene understanding, text categorization and bio-informatics. Semi-supervised multi-label learning has attracted recent interest as gathering labeled data is both expensive and requires manual effort. Further, many of the labels have semantic correlation which manifests as co-occurrence and this information can be used to build effective classifiers in the multi-label scenario. In this paper, we propose two different graph based transductive methods, namely, the label correlation propagation and the k-nearest neighbors based label correlation propagation. Extensive experimentation on real-world datasets demonstrates the efficacy of the proposed methods and the importance of using the label correlation information in semi-supervised multi-label learning.

关键词： Semi-supervised learning Multi-label learning Graph based learning

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Handwriting text/non-text classification on mobile device 4

Handwriting text/non-text classification on mobile device

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4th international conference on Artificial Intelligence and pattern recognition, AIPR 2017

作者： Khomenko, Viacheslav Volkoviy, Andriy Degtyarenko, Illya Radyvonenko, Olga 57 Lva Tolstogo Str. Kyiv01032 Ukraine

ISBN: (纸本)9781941968437

this paper is dedicated to classification of hand-written/drawn input made on screen of mobile devices into two classes: Text and Non-Text. A deep-learning solution using gated recurrent and feed-forward artificial neural networks has been proposed. Two approaches have been compared: A real-time approach, designed to process data at input time with preliminary strokes grouping, and a batch processing approach, designed for analysis of completed handwriting documents having access to document contexts and performing text line grouping after classification. the presented solutions have been validated using the benchmark IAMonDo dataset [1] and specially collected Samsung Mobile HandWriting Document (MHWD) dataset, containing about 10 000 free-form documents combining unconstrained handwriting in seven different languages and different heterogeneous elements. the obtained precision by text class is 98.09% and recall by text class is 99.07% for the proposed batch processing approach. the results of the research have become the basis for development of Document Structure Analysis Engine focused on mobile platform and included in Samsung Handwriting recognition Solution. © 2017 SDIWC.

关键词： Batch data processing

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Product Surface Defect Detection Based on Deep learning

Product Surface Defect Detection Based on Deep Learning

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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...

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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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