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Diagnosis of Parkinson disease using machine learning and data mining systems from voice dataset 3

Diagnosis of Parkinson disease using machine learning and da...

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3rd international conference on Frontiers in Intelligent Computing: Theory and Applications, FICTA-2014

作者： Sriram, Tarigoppula V.S. Venkateswara Rao, M. Satya Narayana, G.V. Kaladhar, D.S.V.G.K. Raghu Engineering College Visakhapatnam India GITAM University Rushikonda Visakhapatnam India Raghu Institute of Technology Visakhapatnam India Dept. Of Bioinformatics GITAM University Visakhapatnam India

ISBN: (纸本)9783319119328

Parkinson’s disease is one of the most painful, dangerous and non curable diseases which occurs at older ages (mostly above 50 years) in humans. The data-set for the disease is retrieved from UCI repository. A relative study on feature relevance analysis and the accuracy using different classification methods was carried out on Parkinson data-set. Sieve multigram data and Survey graph provide the statistical analysis on the voice data so that the healthy and Parkinson patients would be correctly classified. KStar and NNge present good accuracy based classification methods. Sieve multigram shows the edges between the nodes such as Fhi, Flo, Jitter, JitterAb, RAP and PPQ. KStar and NNge have connections with Shimmer and ShimmerdB . ADTree shows 21 leaves with 31 leaves and SimpleCART shows 13 leaves and 7 leaves. Most of the clusters vary with DBScan and SimpleKMeans with 25% and 38% towards Parkinson disease. © Springer international Publishing Switzerland 2015.

关键词： Sieves

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Unsupervised classification of mixed data type of attributes using genetic algorithm (Numeric, categorical, ordinal, binary, ratio-scaled) 1

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3rd international conference on Soft Computing for Problem Solving, SocProS 2013

作者： Rastogi, Rohit Agarwal, Saumya Sharma, Palak Kaul, Uarvarshi Jain, Shilpi Computer Science and Engineering Department ABES Engineering College GhaziabadUttar Pradesh India

ISBN: (数字)9788132217718

ISBN: (纸本)9788132217701

data mining discloses hidden, previously unknown, and potentially useful information from large amounts of data. As comparison to the traditional statistical and machine learning data analysis techniques, data mining emphasizes to provide a convenient and complete environment for the data analysis. data mining has become a popular technology in analyzing complex data. Clustering is one of the data mining core techniques. In the field of data mining and data clustering, it is a highly desirable task to perform cluster analysis on large data sets with mixed numeric, categorical, ordinal, and ratio-scaled with binary and nominal values. However, most already available data merging and grouping through clustering algorithms are effective for the numeric data rather than the mixed data set. For this purpose, this paper makes efforts to present a new amalgamation algorithm for these mixed data sets by modifying the common cost function, trace of the within cluster dispersion matrix. The genetic algorithm (GA) is used to optimize the new cost function to obtain valid clustering result. We can compare and analyze that the GA-based clustering algorithm is feasible for the highdimensional data sets with mixed data values that are obtained in real life results. Core Idea of Our Paper: By this paper, we try to describe a technique for estimating the cost function metrics from mixed numeric, categorical and othertype databases by using an uncertain grade-of-membership clustering model with the efficiency of Genetic Algorithm. This technique can be applied to the problem of opportunity analysis for business decision-making. This general approach could be adapted to many other applications where a decision agent needs to assess the value of items from a set of opportunities with respect to a reference set representing its business. For processing numeric attributes, instead of generalizing them, a prototype may be developed for experiments with synthetic and real data sets, and co

关键词： data mining

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Pectoral muscle detection in digital breast tomosynthesis and mammography

Pectoral muscle detection in digital breast tomosynthesis an...

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3rd international MICCAI Workshop on Medical Computer Vision, MCV 2013

作者： Ghesu, Florin C. Wels, Michael Jerebko, Anna Sühling, Michael Hornegger, Joachim Kelm, B. Michael Siemens Corporate Technology Imaging and Computer Vision Erlangen Germany Pattern Recognition Department Friedrich Alexander University Erlangen Germany Siemens AG Healthcare Erlangen Germany

ISBN: (纸本)9783319055299

Screening and diagnosis of breast cancer with Digital Breast Tomosynthesis (DBT) and Mammography are increasingly supported by algorithms for automatic post-processing. The pectoral muscle, which dorsally delineates the breast tissue towards the chest wall, is an important anatomical structure for navigation. Along with the nipple and the skin, the pectoral muscle boundary is often used for reporting the location of breast lesions. It is visible in mediolateral oblique (MLO) views where it is well approximated by a straight line. Here, we propose two machine learning-based algorithms to robustly detect the pectoral muscle in MLO views from DBT and mammography. Embedded into the Marginal Space learning framework, the algorithms involve the evaluation of multiple candidate boundaries in a hierarchical manner. To this end, we propose a novel method for candidate generation using a Hough-based approach. Experiments were performed on a set of 100 DBT volumes and 95 mammograms from different clinical cases. Our novel combined approach achieves competitive accuracy and robustness. In particular, for the DBT data, we achieve significantly lower deviation angle error and mean distance error than the standard approach. The proposed algorithms run within a few seconds. © 2014 Springer international Publishing Switzerland.

关键词： learning algorithms

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Enhanced Tissue-Specific Molecular pattern Extraction Via Recursive Maximum Margin Criterion

Enhanced Tissue-Specific Molecular Pattern Extraction Via Re...

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3rd international conference on Civil and Architecture Engineering (ICCAE’2014);3rd international conference on Automation, Robotics and Mechanical Engineering (ICAMR’2014);3rd international conference on machine learning and Computer Science (IMLCS’2014);3rd international conference on Management, Humanity and Economics (ICMHE’2014);3rd international conference on Power, Control and VLSI Engineering (ICPCVE’2014)

作者： Kirill A.Veselkov Reza Mirnezami Zoltan Takats the Section of Computational and Systems Medicine at Imperial College London Sir Alexander Fleming Building the section of Biosurgery and Surgical Technology at Imperial College London 10 th Floor QEQM BuildingSt Mary's Hospital

Mass Spectrometry Imaging (MSI) is a rapidly advancing bioanalyitical approach that enables the simultaneous measurement of thousands of molecular species from intact tissue sections in a spatially resolved manner. It provides the opportunity to develop a fully automated chemical histology approach for accurate tissue classification. Such "machine-learned" tissue identification platforms require efficient and robust extraction of tissue-specific discriminating molecular ion patterns. Here, we present a comparative analysis of several supervised feature extraction methods and propose the use of a Recursive Maximum Margin Criterion (RMMC) method for enhanced extraction of tissue-specific discriminating molecular patterns.

关键词： Biomedical computing, dimensionality reduction, imaging mass spectrometry, pattern recognition, supervised feature extraction.

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An Unsupervised Emotional Scene Retrieval Framework for Lifelog Videos

An Unsupervised Emotional Scene Retrieval Framework for Life...

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Hiroki Nomiya Atsushi Morikuni Teruhisa Hochin Kyoto Institute of Technology Kyoto Japan

In order to promote the utilization of lifelog videos, an effective retrieval framework of the emotional scenes, which are considered to be important scenes, is proposed in this paper. The proposed method is based on facial expression recognition since the emotional scenes can be detected by taking the facial expressions into consideration. Most of conventional facial expression recognition methods require a large amount of training data to construct a recognition model. Adopting such methods for large-scale video databases is unrealistic because preparing sufficient training data requires considerable human efforts. We introduce an unsupervised machine learning framework to solve this issue by making it possible to construct a facial expression recognition model without any training data set. The proposed method is evaluated through an emotional scene detection experiment. A prototype of the emotional scene retrieval system based on the proposed emotional scene detection method is implemented.

关键词： Facial features Videos Face recognition Clustering algorithms Accuracy Equations Training data

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mining the Big data: The Critical Feature Dimension Problem

Mining the Big Data: The Critical Feature Dimension Problem

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Qingzhong Liu Bernardete Ribeiro Andrew H. Sung Divya Suryakumar Department of Computer Science Sam Houston State University Huntsville TX USA School of Computing The University of Southern Mississippi Hattiesburg MS USA Department of Informatics Engineering University of Coimbra Coimbra Portugal Applied Machine Learning Wipro Technologies Ltd. Mountain View California USA

In mining massive datasets, often two of the most important and immediate problems are sampling and feature selection. Proper sampling and feature selection contributes to reducing the size of the dataset while obtaining satisfactory results in model building. Theoretically, therefore, it is interesting to investigate whether a given dataset possesses a critical feature dimension, or the minimum number of features that is required for a given learning machine to achieve "satisfactory" performance. (Likewise, the critical sampling size problem concerns whether, for a given dataset, there is a minimum number of data points that must be included in any sample for a learning machine to achieve satisfactory performance.) Here the specific meaning of "satisfactory" performance is to be defined by the user. This paper addresses the complexity of both problems in one general theoretical setting and shows that they have the same complexity and are highly intractable. Next, an empirical method is applied in an attempt to find the approximate critical feature dimension of datasets. It is demonstrated that, under generally reasonable assumptions pertaining to feature ranking algorithms, the critical feature dimension are successfully discovered by the empirical method for a number of datasets of various sizes. The results are encouraging in achieving significant feature size reduction and point to a promising way in dealing with big data. The significance of the existence of crucial dimension in datasets is also explained.

关键词： Accuracy Electromagnetic interference data mining Classification algorithms Complexity theory Educational institutions Vectors

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Multi-party Conversation Summarization Based on Sentence Selection Using Verbal and Nonverbal Information

Multi-party Conversation Summarization Based on Sentence Sel...

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Yo Tokunaga Kazutaka Shimada Department of Artificial Intelligence Kyushu Institute of Technology Iizuka Fukuoka Japan

In this paper, we propose a method for conversation summarization. For the method, we combine two approaches, a scoring method and a machine learning technique (SVMs). First we compare important utterance extraction by the scoring method and SVMs. In the machine learning technique, we introduce verbal features, such as relations between utterances and anaphora features, and nonverbal features. Next we generate a summary from the outputs of the scoring method and SVMs. In our approach, a basic summary consists of utterances with high confidence extracted from the scoring method. Utterances from SVMs are used as supplementary information. In the experiment, we compare a combination method and a method with only SVMs. The output of our method was suitable in terms of readability and correctness as a summary of original conversation.

关键词： Feature extraction Accuracy Support vector machines Noise Context data mining Electronic mail

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Universalization of narrow methods: Case study on autoencoders

Universalization of narrow methods: Case study on autoencode...

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IEEE international conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Alexey Potapov Vita Batishcheva Shuchao Pang Mechanics and Optics National Research University of Information Technology St. Petersburg Russia Petersburg State University St. Petersburg Russia College of Computer Science and Technology Jilin University Changchun China

The problem of bridging the gap between efficient but narrow methods of machine learning, and universal but inefficient methods was considered. Our main claim, which is methodologically important to the field of Artificial General Intelligence (AGI), is that neither narrow nor basic universal methods are sufficient for AGI. This claim was illustrated on example of pattern recognition task using stacked autoencoders and their two extensions with more exhaustive search and richer solution space. These three types of classifiers were evaluated on the base of a criterion that account for both error rate and training time. Depending on the urgency of the task to be solved, less or more universal methods appeared to be better. Thus, AGI might start with narrow methods, but should be able to perform their “universalization” (i.e. extension of the model space possibly up to Turing-complete space if it is appropriate in a certain situation).

关键词： Training Simulated annealing Error analysis Computational modeling Logistics pattern recognition Artificial intelligence

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Comments data mining for Evaluating Student's Performance

Comments Data Mining for Evaluating Student's Performance

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Shaymaa E. Sorour Tsunenori Mine Kazumasa Godaz Sachio Hirokawax Fac. of Specific Educ. Kafr Elsheik Univ. KafrElsheikh Egypt Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Kyushu Joho Daigaku Dazaifu Fukuoka JP Kyushu Daigaku Fukuoka JP

The present study proposes prediction approaches of student's grade based on their comments data. Students describe their learning attitudes, tendencies and behaviors by writing their comments freely after each lesson. The main difficulty of this research is to predict students' performance by separately using two class data in each lesson. Although students learn the same subject, there exist differences between the comments in the two classes. The proposed methods basically employ latent semantic analysis (LSA) and two types of machine learning technique: SVM (support vector machine) and ANN (artificial neural network) for predicting students' final results in four grades of S, A, B and C. Moreover, an overlap method was proposed to improve the accuracy prediction results, the method allows to accept two grades for one mark to get the correct relation between LSA results and students' grades. The proposed methods achieve 50.7% and 48.7% prediction accuracy of students' grades by SVM and ANN, respectively. To this end, the results of this study reported models of students' academic performance predictors that are valuable sources of understanding students' behavior and giving feedback to them so that we can improve their learning activities.

关键词： Support vector machines Artificial neural networks Accuracy Predictive models Vectors Semantics data mining

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Using hybrid Neural Network to address Chinese Named Entity recognition

Using hybrid Neural Network to address Chinese Named Entity ...

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IEEE international conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Guoyu Wang Yongquan Cai Fujiang Ge Beijing University of Technology Beijing China Lenovo Cloud/Big Data and Intelligent Computing Lab China

Many machine learning methods have been applied on Named Entity recognition (NER). Such methods generally build on a large manually-annotated training set. However, the training set is usually limited as human labeling is costly and time consuming. Compare to the training set, the unlabeled corpus is usually much bigger and contains rich information about language. In this paper, a hybrid Deep Neural Network (DNN) is proposed to take advantage of the implicit information embedded in the un-labeled corpus. The experiments show that F1-score is improved from 85% to 90% (person name), from 75% to 81% (location name), and from 74% to 78% (organization name), compared with Conditional Random Fields (CRFs).

关键词： Training Neurons Neural networks Semantics Computational modeling Hidden Markov models Dictionaries

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