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检索条件"任意字段=1st International Workshop on Machine Learning and Data Mining in Pattern Recognition"

共 585 条记录，以下是201-210 订阅

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How Effective is the Salp Swarm Algorithm in data Classification 1st

How Effective is the Salp Swarm Algorithm in Data Classifica...

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1st international Conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Panda, Nibedan Majhi, Santosh Kumar Department of Computer Science and Engineering Veer Surendra Sai University of Technology BurlaOdisha768018 India

ISBN: (纸本)9789811390418

In this paper, the Salp Swam Algorithm (SSA) is deployed in training the Multilayer Perceptron (MLP) for the task of data classification. The UCI machine learning repository standard datasets are used for evaluation of the proposed SSA based MLP. The performance of the proposed method is verified by considering various standard classification measures over the considered benchmark datasets. The result obtained by the proposed method is compared with other evolutionary algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Grey Wolf Optimization (GWO), etc. From the simulation study, it has been verified that the proposed method shows supremacy in results as compared with other evolutionary algorithm based MLP. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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NCBI: A Novel Correlation Based Imputing Technique Using Biclustering 1st

NCBI: A Novel Correlation Based Imputing Technique Using Bic...

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1st international Conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Chowdhury, Hussain A. Ahmed, Hasin A. Bhattacharyya, Dhruba Kumar Kalita, Jugal K. Computer Science and Engineering Tezpur University SonitpurAssam784028 India Department of Information and Computer Science Assam Women’s University JorhatAssam785004 India Computer Science University of Colorado Colorado SpringsCO80933-7150 United States

ISBN: (纸本)9789811390418

Presence of missing values (MV) in gene expression data is commonplace. It significantly affects the performance of statistical analysis and machine learning algorithms. Discarding objects or attributes with missing values and inappropriate estimation of MVs lead to high information loss and misleading results. So, it is necessary to have an accurate technique for missing value imputation. In this paper, we present a novel correlation based missing value imputation technique for gene expression datasets. We refer to our method as NCBI. We compare the estimation accuracy of our technique with two widely used methods such as KNNI and KMI, on four benchmark datasets by randomly knocking out data values as missing. Our technique can estimate missing values almost 20–25% more accurately than KNNI and KMI in all datasets. © 2020, Springer Nature Singapore Pte Ltd.

关键词： machine learning

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From data Collection Merit to data Connection Merit for Smart Sustainable Cities 20

From Data Collection Merit to Data Connection Merit for Smar...

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1st international workshop on Intelligence Cross-data Analytics and Retrieval, ICDAR 2020

作者： Zettsu, Koji National Institute of Information and Communications Technology Tokyo Japan

ISBN: (纸本)9781450375092

Smart data refers to the IoT data that has been processed to produce valuable data be turned into actionable information, which can be used for intelligence, planning, controlling and decision making efficiently and effectively by governments, industries and citizens. Unprecedentedly large amount and variety of sensory data can be collected to explore how these big data can become smart data and offer intelligence. Advanced data modeling and analytics, as well as data science solutions, are indispensable for transforming big data into smart data. For accelerating the utilization of smart data, NICT Real Space Information Analytics Project makes efforts to develop a cross-data analytics technology for utilizing data obtained from a variety of sensing technologies and different kinds of social big data to construct a platform that will help develop and expand smart services with a view towards smart sustainable cities. We are developing the xdata Platform on NICT's Integrated Testbed, which is a platform implementing functions of a data loader for data collection, retrieval and conversion from a variety of data sources, association mining for spatiotemporal data integration and discovery of association rules, machine learning for prediction of spatiotemporal association patterns, and creation and distribution of prediction result data in a GIS format for route search and alert notification. For accelerating open innovation using the xdata Platform, we conducted field experiments with participation of citizens. © ICDAR 2020.

关键词： data warehouses

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Sub-scene Target Detection and recognition Using Deep learning Convolution Neural Networks 1st

Sub-scene Target Detection and Recognition Using Deep Learni...

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1st international Conference on data Science, machine learning and Applications, 2019

作者： Merugu, Suresh Jain, Kamal Mittal, Anshul Raman, Balasubramanian R&D Centre CMR College of Engineering & Technology Hyderabad India Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9789811514197

Sub-scene recognition algorithm based on super resolution along with scene dependent neural network model and sub-scene dependent target detection for automating object information extraction is proposed. This work deals with large number of challenges possessed by classification problems. Some of the challenges in this problem are the low resolution satellite images, diverse pattern of each sub-scene causing the low level learning for classification and plethora of distinct object classes present in each sub-scene causes low accuracy of object detection. Objective of this paper presents an image super resolution technique for rectifying problems posed by low resolution images with color density variations of chromaticity coordinates. To eliminate the problem of diverse patterns, have divided various land cover types into separate groups based on maximum mixed fraction among these groups and corresponding sub-scene recognition disparate model parameters are used to recognize various scenes. To increase the accuracy for object detection has developed a sub-scene dependent Neural Network model for extracting the target/anomaly of object information. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Detection of Diseases in Potato Leaves Using Transfer learning 1st

Detection of Diseases in Potato Leaves Using Transfer Learni...

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1st international Conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Dasgupta, Soumik Ranjan Rakshit, Somnath Mondal, Dhiman Kole, Dipak Kumar Jalpaiguri Government Engineering College Jalpaiguri735102 India Cyware Labs Bangalore560102 India

ISBN: (纸本)9789811390418

The problem of food shortage has grown rampant in the recent times in developing countries. In a tropical country like India, potato is one of the major staple food that is eaten throughout the year. Recently the production of potato is falling short due to various diseases like Early Blight and Late Blight which cause a huge loss of the cropped plants. This also leads to a major loss in the national economy as well. The emergence of deep learning has affected many fields of machine learning research. Since it is not required in deep learning to develop hand-crafted features, it has found widespread adoption in the scientific community. To tackle the need for a huge amount of data for deep learning, another heavily implemented technique is used, namely, transfer learning, to make the training process faster and more accurate with a relatively small dataset at hand. The performance of the model is demonstrated both quantitatively by computing the accuracy metric as well as visually. The model is lightweight and robust and thus can be added to an application in a handheld device like smartphone so that crop growers could spot the disease affected crops on the go and save them from getting ruined. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Image classification

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PRPD data analysis with Auto-Encoder Network 1

PRPD data analysis with Auto-Encoder Network

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1st international Symposium on Water Resource and Environmental Management (WREM)

作者： Li, Songyuan Man, Yuyan Zhang, Chi Fang, Qiong Li, Suya Deng, Min State Grid Tianjin Elect Power Co Elect Power Res Inst Tianjin 300384 Peoples R China Red Phase INC Xiamen 361005 Fujian Peoples R China

Gas Insulated Switchgear (GIS) is related to the stable operation of power equipment. The traditional partial discharge pattern recognition method relies on expert experience to carry out feature engineering design artificial features, which has strong subjectivity and large blindness. To address the problem, we introduce an encoding-decoding network to reconstruct the input data and then treat the encoded network output as a partial discharge signal feature. The adaptive feature mining ability of the Auto-Encoder Network is effectively utilized, and the traditional classifier is connected to realize the effective combination of the deep learning method and the traditional machine learning method. The results show that the features extracted based on this method have better recognition than artificial features, which can effectively improve the recognition accuracy of partial discharge.

关键词： Partial discharges

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Application of data mining Methods for pattern recognition in Negotiation Support Systems 19th

Application of Data Mining Methods for Pattern Recognition i...

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1st Joint Meeting of the 19th Annual international Conference on Group Decision and Negotiation (GDN) / Behavioral OR Conference

作者： Kaya, Muhammed-Fatih Schoop, Mareike Univ Hohenheim D-70599 Stuttgart Germany

ISBN: (纸本)9783030217112;9783030217105

data mining methods have long been used to support organisational decision making by analysing organisational data from large databases. The present paper follows this tradition by discussing two different data mining techniques that are being implemented for pattern recognition in Negotiation Support Systems (NSSs), thereby providing process assistance to human negotiators. To this end, data from several international negotiation experiments via NSS Negoisst is used. Consequently, a suitable data representation of the underlying utility data and communication data has to be created for the applicability of data mining Each generated data type needs individual processing treatments and almost all data mining methods lose their feasibility without a correct data representation as consequence. Once a correct data representation is found, the potential for pattern recognition in electronic negotiation data can be evaluated using descriptive and predictive methods. Whilst Association Rule Discovery is used as a descriptive technique to generate essential sets of strategic association patterns, the Decision Tree is applied as a supervised learning technique for the prediction of classification patterns. The extent to which reliable as well as valuable patterns can be derived from the electronic negotiation data and valuable predictions can be generated is examined in this paper.

关键词： Negotiation Support Systems data mining Text mining Association Rule Discovery Decision Tree

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Algorithmic approach to the recognition of cells in the sensorimotor cortex from microphotographs 1

Algorithmic approach to the recognition of cells in the sens...

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1st workshop on Information Systems for the Radiation Biology Tasks, ISRB 2020

作者： Kolesnikova, I.A. Budennaya, N.N. Severyukhin, Yu.S. Lalkovicova, M.G. Laboratory of Radiation Biology Joint Institute for Nuclear Research Dubna Russia Federal State-Funded Educational Institution of Higher Education of Moscow Region "Dubna University" Dubna Russia Slovak Academy of Sciences Institute of Experimental Physics Kosice Slovakia

The aim of the work is to study the effect of ionizing radiation on the central nervous system of laboratory animals. The Sector of Radiation Physiology of LRB JINR studies the effects of radiation on the cellular and organismal levels. Experimental animals are irradiated with protons or gamma rays in the JINR Medico-Technical Complex. The collection of biological material, its fixation, slicing, staining, microscopy and the creation of photographic images of the samples take two weeks or more, and the morphological analysis of the histological preparations takes approximately six months. Achievements in the application of artificial neural networks in the biomedical field indicate the possibility of automating the stage of analysis of histological preparations. The purpose of this work is to show the effectiveness of modern computer vision algorithms and machine learning methods for automating the stages of the morphological analysis of micropreparations of the brain of experimental animals after exposure to ionizing radiation. Thereby the speed of obtaining qualitative results increases, and the subjectivity of the approach to processing experimental data decreases. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： data handling

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Forecasting Network Intrusions from Security Logs Using LstMs 1st

Forecasting Network Intrusions from Security Logs Using LSTM...

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1st international workshop on Deployable machine learning for Security Defense, MLHat 2020, collocated with the 25th ACM SIGKDD Conference on Knowledge Discovery and data mining, KDD 2020

作者： Mueller, W. Graham Memory, Alex Bartrem, Kyle Leidos Inc. ArlingtonVA22203 United States

ISBN: (纸本)9783030596200

Computer network intrusions are of increasing concern to governments, companies, and other institutions. While technologies such as Intrusion Detection Systems (IDS) are growing in sophistication and adoption, early warning of intrusion attempts could help cybersecurity practitioners put defenses in place early and mitigate the effects of cyberattacks. It is widely known that cyberattacks progress through stages, which suggests that forecasting network intrusions may be possible if we are able to identify certain precursors. Despite this potential, forecasting intrusions remains a difficult problem. By leveraging the rapidly growing and widely varying data available from network monitoring and Security Information and Event Management (SIEM) systems, as well as recent advances in deep learning, we introduce a novel intrusion forecasting application. Using six months of data from a real, large organization, we demonstrate that this provides improved intrusion forecasting accuracy compared to existing methods. © 2020, Springer Nature Switzerland AG.

关键词： Deep learning

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VIPERC 2019: Visual pattern extraction and recognition for cultural heritage understanding 1

VIPERC 2019: Visual pattern extraction and recognition for c...

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1st international workshop on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2019

作者： Amelio, Alessia Ćosović, Marijana Draganov, Ivo Rumenov Janković, Radmila Kabassi, Katerina Tanikić, Dejan University of Calabria DIMES Rende87036 Italy University of East Sarajevo Faculty of Electrical Engineering Lukavica East Sarajevo71126 Bosnia and Herzegovina Technical University of Sofia Faculty of Telecommunications Sofia1756 Bulgaria Mathematical Institute of the Serbian Academy of Sciences and Arts Belgrade11000 Serbia Ionian University Kerkira491 00 Greece University of Belgrade Technical Faculty in Bor Bor19210 Serbia

Nowadays, the cultural heritage understanding has become invaluable for a better analysis of history and traditions worldwide. In the last years, machine learning, intelligent systems and statistical analysis played a key role in the extraction, analysis and identification of patterns from visual data in different real-life aspects. In this context, the main goal of the 1st international workshop on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2019) was presenting the advancement in visual pattern extraction, analysis and recognition aiming to preserve the cultural heritage. The event was organised as an international research forum by the University of Calabria, the University of East Sarajevo, the Technical University of Sofia, the Mathematical Institute of the Serbian Academy of Sciences and Arts, the Ionian University and the University of Belgrade. © 2019 CEUR-WS. All rights reserved.

关键词： Intelligent systems

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