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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3245 条记录，以下是251-260 订阅

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Comparison of machine learning Algorithms and Fruit Classification using Orange data mining Tool 3

Comparison of Machine Learning Algorithms and Fruit Classifi...

引用

3rd international conference on Inventive Computation Technologies (ICICT)

作者： Vaishnav, Devashree Rao, B. Rama Univ Mumbai Dept Elect & Telecommun Mumbai Maharashtra India

ISBN: (纸本)9781538649855

With the hype about Artificial Intelligence, machine learning has become a trending topic these days. Lots of tools are available for data visualization, yet most of the model training is dependent on scripting languages. Orange data mining tool provides the flexibility with data pre-processing, visualization and model training and test in a single software. The proposed paper applies machine learning algorithms to a fruit image dataset and perform a comparative study of the algorithms to determine which algorithm has the highest Classification Accuracy and Precision score. Decision making is based on the images used to train the algorithm to learn the specific features derived from those trained images. Cross validation is performed in order to evaluate the results for Classification Accuracy. The difference between actual and predicted values is evaluated to determine the number of instances predicted correctly.

关键词： Orange data mining machine learning Supervised learning Classification Fruits dataset Cross Validation Confusion Matrix

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Classification of Power Quality Events Using Deep learning on Event Images 3

Classification of Power Quality Events Using Deep Learning o...

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3rd international conference on pattern Analysis and Image Analysis (IPRIA)

作者： Balouji, Ebrahim Salor, Ozgul Gazi Univ Dept Elect & Elect Engn Ankara Turkey

ISBN: (纸本)9781509064540

In this paper, a new method for the classification of power quality (PQ) events of the electricity networks based on deep learning approach is presented. In contrast with the existing PQ event data analysis techniques, sampled voltage data of the PQ events are not used, but image files of the three-phase PQ event data are analyzed by taking the advantage of the success of the deep leaning approach on image-file-classification. Therefore, the novelty of the proposed approach is that, image files of the voltage waveforms of the three phases of the power grid are classified. PQ events obtained from four transformer substations of the electricity transmission system for a year are used for training and testing the proposed classification method. DIGITS deep learning platform of NVIDIA is employed for the application of the deep learning algorithm on PQ event data images. It is shown that the test data can be classified with 100% accuracy. The proposed work is believed to serve the needs of the future smart grid applications, which are fast and automatic analysis of the electricity grid and taking automatic countermeasures against potential PQ events.

关键词： data classification deep learning multi-layer convolution neural network (MLCNN) power quality (PQ)

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Comparative Study of data mining Tools used for Clustering 3

Comparative Study of Data Mining Tools used for Clustering

引用

3rd international conference on Computing for Sustainable Global Development (INDIACom)

作者： Aalam, Parvej Siddiqui, Tamanna AMU Dept Comp Sci Aligarh Uttar Pradesh India

ISBN: (纸本)9789380544199

Clustering, a component of data mining is the process of grouping objects into several clusters such that objects in the same cluster have maximum similarity while the objects in different clusters has maximum dissimilarity. Clustering has been used in diverse fields including Text mining, pattern recognition, Image analysis, Bioinformatics, machine learning, Voice mining, Image processing, Web cluster engines, Whether report analysis etc. To perform the task of clustering, various data mining tools arc freely available. These tools have their own features and carry out efficiently the task of generating clusters automatically for a given set of data. This paper discusses seven such tools in detail. A comparative study of these tools has been also done on basis of various parameters as License type of the tool, Programming Language used by tool, Interface provided by the tool, Developer of the tool etc. The goal is to provide the users/researchers all the necessary details about these clustering tools so that it may help them to select an appropriate tool for their use in cluster analysis.

关键词： Algorithm Clustering Methods data mining Short Text Tools

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Interpretable machine learning with Boosting by Boolean Algorithm 8

Interpretable Machine Learning with Boosting by Boolean Algo...

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Joint 8th international conference on Informatics, Electronics and Vision (ICIEV) / 3rd international conference on Imaging, Vision and pattern recognition (icIVPR) / international conference on Activity and Behavior Computing (ABC)

作者： Neuhaus, Nathan Kovalerchuk, Boris Cent Washington Univ Dept Comp Sci Ellensburg WA 98926 USA

ISBN: (纸本)9781728107882

Much research has been conducted for developing efficient, automated, interpretable, and highly accurate machine learning algorithms. However, the primary challenge remains-developing algorithms successful in all of these aspects simultaneously. For applications, where human lives are at stake, having to choose between accuracy and interpretability is not acceptable. To address this challenge, previously the Dominance Classifier and Predictor (DCP) algorithm, capable of discovering of the human-understandable models in simple and visualizable terms was proposed. On the benchmark Wisconsin Breast Cancer (WBC) dataset, it achieved greater accuracy than other interpretable algorithms, reducing the gap between the prediction accuracy of interpretable and non-interpretable algorithms on these data. This paper proposes a new interpretable algorithm RPPR, to bridge this gap more by boosting DCP via discovering properties of misclassified cases. Experiments with RPPR on WBC and two other benchmark datasets using 10-fold cross validation, achieved accuracies greater than 99%. Thus, the accuracy of non-interpretable algorithms is reachable without sacrificing interpretability.

关键词： machine learning interpretability boosting classifier dominant intervals human-understandable models

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data mining for security applications

Data mining for security applications

引用

3rd international conference on machine learning and Applications

作者： Thuraisingham, B Univ Texas Richardson TX USA

ISBN: (纸本)0780388232

The importance of data mining to detect and to prevent cyber attacks and its applications for cyber security and national security including intrusion detection and biometrics are described. data mining is the process of posing queries and extracting patterns. It can be used for analyzing web logs and also in analyzing the audit trails. The tool also helps to determine whether any unauthorized intrusions have occured and whether any unauthorized queries have been posed.

关键词： data mining

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Issues on Critical Objects in mining Algorithms 3

Issues on Critical Objects in Mining Algorithms

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3rd international conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Yazdani, H. Kwasnicka, H. Univ Wroclaw PL-50138 Wroclaw Poland

ISBN: (纸本)9781467391870

data objects are considered as fundamental keys in learning methods that without the objects the mining algorithms are meaningless. data objects basically direct the accuracy of the selected algorithm in case if they are extracted from inappropriate groups. Knowing the exact type of data object leads the miner to provide a suitable environment for learning algorithms. Supervised and unsupervised learning methods propose some membership functions that perform with respect to behaviour of each data category to classify data objects and solutions. The paper explores different type of data objects by categorizing them based on their behaviour with respect to learning methods. We also introduce some critical objects that play the main role in each data set. Issues on critical objects in mining algorithms are fully discussed in this paper. The accuracy and behaviour of these critical objects are compared by running fuzzy, probabilistic, and possibilistic algorithms on some data sets presented in this paper. The results prove that some methods are able to provide a suitable environment for critical objects and some are not. The comparison results also show that most of the learning methods have difficulties dealing with critical objects. Lack of ability to deal with these objects may cause irreparable consequences.

关键词： data Objects data Type Critical Object Fuzzy Possibilistic Method Supervised Classification Unsupervised Clustering Membership Function

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Spatio-Temporal Convolutional Neural Network for Frame Rate Up-Conversion 2019

Spatio-Temporal Convolutional Neural Network for Frame Rate ...

引用

3rd international conference on Intelligent Systems, Metaheuristics and Swarm Intelligence (ISMSI)

作者： Tanaka, Yusuke Omori, Toshiaki Kobe Univ Dept Elect & Elect Engn Grad Sch Engn Nada Ku 1-1 Rokkodai Cho Kobe Hyogo Japan

ISBN: (纸本)9781450372114

The visual quality of the video is improved by realizing higher resolution and higher frame rate. In order to realize higher frame rate, we propose new frame rate up-conversion method using spatio-temporal convolutional neural network. In recent years, with the development of machine learning techniques such as convolutional neural networks, clearer interpolation frame estimation has been realized. However, with the conventional convolutional neural network method, it is difficult to estimate an accurate interpolation frames for video including complex motion. In order to deal with this problem, we adopted spatio-temporal convolution rather than conventional spatial convolution. Spatio-temporal convolution is thought to be effective for nonlinear motion because it can capture the time change of the motion of the object. We verified the effectiveness of the proposed method by using video data including complex motions such as rotational motion and scaling.

关键词： Neural networks machine learning Frame rate up-conversion High-dimensional time-series data mining Spatio-temporal pattern recognition

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SCHEMATA: 3D Classification and Categorization of Ancient Terracotta Figurines 12

SCHEMATA: 3D Classification and Categorization of Ancient Te...

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12th international conference on Agents and Artificial Intelligence (ICAART)

作者： Zeckey, Alexander Langner, Martin Georg August Univ Gottingen Inst Digital Humanities Heinrich Duker Weg 12 Gottingen Germany

ISBN: (纸本)9789897583957

The goal of the starting case-study is not only to develop procedures for automatically generating corpora using 3D pattern recognition, but also to reflect on the associated schematizations and how they can be applied in computer science and visual sciences. For this purpose, methods of object mining in 3D data are to be developed. We chose an object group which is defined by its complexity in shape and the similarity between the objects: In 4th and 3rd century BC ancient Greece small terracotta figurines used to be an art form that was quite common. Based on 200 of those terracottas, a classification system will be elaborated with digital methods, which is able to meet the complexity of the artefacts. In close cooperation between computer science and archaeology, this experimental process leads to a fundamental examination of the concept of pattern recognition as a humanities category. The discussion of the various concepts and methods will be carried out in two complementary dissertations.

关键词： Object mining Image Exploration Greek Terracotta Figurines machine learning Typology

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Analysis of Complete-Link Clustering for Identifying Multi-attributes Software Quality data 3rd

Analysis of Complete-Link Clustering for Identifying Multi-a...

引用

3rd international conference on Advanced machine learning Technologies and Applications (AMLTA)

作者： Pal, Jaya Bhattacherjee, Vandana Birla Inst Technol Dept CS & Engn Ranchi Bihar India

ISBN: (纸本)9783319746906;9783319746890

Clustering is a robust technique in the area of data mining research for extracting useful information from a set of data. It classifies the data into several clusters based on similarity of the pattern. The quality of clustering can be presented based on a metric of dissimilarity of objects, compared for various types of data. This paper presents one of the agglomerative approaches of hierarchical clustering techniques i.e. complete-link clustering by considering Euclidian distance metric for the quality estimation for students' projects data.

关键词： Clustering Hierarchical clustering data mining Agglomerative Complete link clustering Cluster generation Software quality

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An approach to leak detection in pipe networks using analysis of monitored pressure values by support vector machine

An approach to leak detection in pipe networks using analysi...

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3rd international conference on Network and System Security

作者： Mashford, John De Silva, Dhammika Marney, Donavan Burn, Stewart Commonwealth Sci & Ind Res Org Highett Vic 3190 Australia

ISBN: (纸本)9781424450879

This paper presents a method of mining the data obtained by a collection of pressure sensors monitoring a pipe network to obtain information about the location and size of leaks in the network This inverse engineering problem is effected by support vector machines (SVMs) which act as pattern recognisers. In this study the SVMs are trained and tested on data obtained from the EPANET hydraulic modelling system. Performance assessment of the SVM showed that leak size and location are both predicted with a reasonable degree of accuracy. The information obtained from this SVM analysis would be invaluable to water authorities in overcoming the ongoing problem of leak detection.

关键词： leak detection pipe networks pattern recognition support vector machines

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