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检索条件"任意字段=2019 International Conference on Applied Machine Learning and Data Science, ICAMLDS 2019"

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Odia character recognition using curvelet transform with DWT feature extraction 1

Odia character recognition using curvelet transform with DWT...

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1st international conference on applied machine learning, ICAML 2019

作者： Jena, Om Prakash Pradhan, Sateesh Kumar Biswal, Pradyut Kumar Nayak, Sradhanjali Nayak, Tushar Kanta Dept. of Computer Science Ravenshaw University Cuttack India Dept. of CSA Utkal University Bhubaneswar India Dept. of ECE IIIT BhubaneswarOdisha India

ISBN: (纸本)9781728139081

Developing of OCR for Indian scripts is an increasing area of research. This research work is an attempt to make an OCR system for Odia characters and numbers which is official language of ODISHA. In this work we proposed a new approach for Odia character recognition using digital curvelet transform. It presents characters and numbers recognition scheme for Odia characters. The curvelet transform although heavily utilized in various areas of image processing. The curvelet coefficients of a unique picture just as its morphologically adjusted renditions are utilized to KNN classifiers. The yield estimations of these classifiers are combined utilizing a straightforward dominant part casting a ballot plan to touch base at an official choice utilizing one of a kind structure of some character we have discovered better outcome as correlation with different techniques. © 2019 IEEE.

关键词： Feature extraction

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Application of improved convolutional neural network in image classification

Application of improved convolutional neural network in imag...

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2019 international conference on machine learning, Big data and Business Intelligence, MLBDBI 2019

作者： Chen, Enzhi Wu, Xiaming Wang, Chunyang Du, Yanyan School of Electronic Information Engineering Changchun University of Science and Technology Changchun China

ISBN: (纸本)9781728150949

Because the LeNet-5 convolutional neural network is not accurate in classifying images with complex texture features, in order to improve the accuracy, this paper proposes a multi-convolution neural network. First, on the basis of the LeNet-5 network, a convolutional layer and a pooling layer are added to deepen the depth of the network. Secondly, the first two pooling layers are propagated backward and connected to the last pooling layer through convolution, so as to make full use of the low-level features extracted by the network and effectively combining the low-level features and high-level features extracted by the network. Finally, use the Adam optimizer to adjust and update the parameters of the network. Experimental results on the Cats vs. Dogs, Cifar-10 and Fer2013 datasets show that the multi-convolution neural network has good classification ability on complex texture feature datasets. © 2019 IEEE.

关键词： Image classification

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RPR-BP: A Deep Reinforcement learning Method for Automatic Hyperparameter Optimization

RPR-BP: A Deep Reinforcement Learning Method for Automatic H...

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international Joint conference on Neural Networks (IJCNN)

作者： Wu, Jia Chen, SenPeng Chen, XiuYun Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Peoples R China

ISBN: (纸本)9781728119854

We introduce a new deep reinforcement learning architecture - RPR-BP to optimize hyperparameter for any machine learning model on a given data set. In this method, an agent constructed by a Long Short-Term Memory Network aims at maximizing the expected accuracy of a machine learning model on a validation set. At each iteration, it selects a set of hyperparameters and uses the accuracy of the model on the validation set as the reward signal to update its internal parameters. After multiple iterations, the agent learns how to improve its decisions. However, the computation of the reward requires significant time and leads to low sample efficiency. To speed up training, we employ a neural network to predict the reward. The training process for the agent and the prediction network is divided into three phases: Real-Predictive-Real (RPR). First, the agent and the prediction network are trained by the real experience;then, the agent is trained by the reward generated from the prediction network;finally, the agent is trained again by the real experience. In this way, we can speed up training and make the agent achieve a high accuracy. Besides, to reduce the variance, we propose a Bootstrap Pool (BP) to guide the exploration in the search space. The experiment was carried out by optimizing hyperparameters of two widely used machine learning models: Random Forest and XGBoost. Experimental results show that the proposed method outperforms random search, Bayesian optimization and Treestructured Parzen Estimator in terms of accuracy, time efficiency and stability.

关键词： Hyperparameter optimization Deep learning Reinforcement learning LSTM network

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Solder Joint Defect Detection Based on Image Segmentation and Deep learning

Solder Joint Defect Detection Based on Image Segmentation an...

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2019 IEEE international conference on Signal, Information and data Processing, ICSIDP 2019

作者： Gao, Shijia Zhang, Hui Mi, Hanguang National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing China Beijing Aerospace Automatic Control Institute 50# Yongding Road Beijing China

ISBN: (纸本)9781728123455

Image feature extraction and machine learning methods are used to detect and identify PCB solder joints. The normal/abnormal classification of solder joints are realized. What is more, the maximal class variance method (OTSU) is used to distinguish solder joints from background, and the Morphology was used to extract the suspected solder joints. The positive and negative samples were labeled and then the convolution neural network (CNN) was used to classify the test samples. Compared with the support vector machine (SVM) method, which extracts HOG features, and the fisher discriminant method based on the circular degree feature, the neural network has better classification effect. © 2019 IEEE.

关键词： Feature extraction

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Application of Deep learning Models to MicroRNA Transcription Start Site Identification 7

Application of Deep Learning Models to MicroRNA Transcriptio...

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IEEE 7th international conference on Bioinformatics and Computational Biology (ICBCB)

作者： Barham, Clayton Cha, Mingyu Li, Xiaoman Hu, Haiyan Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA

ISBN: (纸本)9781728106410

MicroRNAs (miRNA) are similar to 22 base pair long RNAs that play important roles in regulating gene expression. Understanding the transcriptional regulation of miRNA is critical to gene regulation. However, it is often difficult to precisely identify miRNA transcription start sites (TSSs) due to miRNA-specific biogenesis. Existing computational methods cannot effectively predict miRNA TSSs. Here, we employed deep learning architectures incorporating Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) techniques to detect miRNA TSSs in regions of accessible chromatin. By testing on benchmark experimental data, we demonstrated that deep learning models outperform support vector machine and can accurately distinguish miRNA TSSs from both flanking regions and intergenic regions.

关键词： bioinformatics transcriptional regulation transcription start sites neural networks deep learning

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Interpretable Prediction of Protein-Ligand Interaction by Convolutional Neural Network

Interpretable Prediction of Protein-Ligand Interaction by Co...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Hu, Fan Jiang, Jiaxin Yin, Peng Chinese Acad Sci Joint Engn Res Ctr Hlth Big Data Intelligent Anal Shenzhen Inst Adv Technol Shenzhen Peoples R China Univ Sci & Technol China Sch Software Engn Hefei Peoples R China

ISBN: (纸本)9781728118673

Evaluation of protein-ligand interaction is a crucial step in the process of drug discovery. Recently, several methods based on deep learning have gained impressive binary classification performance on protein-ligand binding prediction. However, lack of three-dimensional complex data still limits the accuracy and robustness of evaluation of protein-ligand binding affinity, as well as the prediction of their binding sites. In this paper, we propose a novel convolutional neural network based method for estimating the binding affinity between protein and ligand using only 1D sequence data. Even with the same amount of sample size, this model outperforms other structure-dependent traditional and machine learning based methods in terms of both binary classification and regression task. Furthermore, we use this model to identify the key amino acid residues of protein that are vital for binding interaction, which provides biological interpretation.

关键词： drug discovery representation learning binding sites prediction interpretability occlusion

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Prediction of Student Success Through Analysis of Moodle Logs: Case Study 1

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11th international conference on ICT Innovations (ICT Innovations)

作者： Ademi, Neslihan Loshkovska, Suzana Kalajdziski, Slobodan Int Balkan Univ Skopje North Macedonia Ss Cyril & Methodius Univ Skopje North Macedonia

ISBN: (数字)9783030331108

ISBN: (纸本)9783030331108;9783030331092

data mining together with learning analytics are emerging topics because of the huge amount of educational data coming from learning management systems. This paper presents a case study about students' grade prediction by using data mining methods. data obtained from Moodle log files are explored to understand the trends and effects of students' activities on Moodle learning management system. Correlations of system activities with the student success are found. data is classified and modeled by using decision tree, Bayesian Network and Support Vector machine algorithms. After training the model with a one-year course activity data, next years' grades are predicted. We found that Decision tree classification gives the best accuracy on the test data for the prediction.

关键词： data Mining learning Analytics LMS E-learning Prediction

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Prediction car prices using quantify qualitative data and knowledge-based system 11

Prediction car prices using quantify qualitative data and kn...

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11th international conference on Knowledge and Systems Engineering (KSE)

作者： Doan Van Thai Luong Ngoc Son Pham Vu Tien Nguyen Nhat Anh Nguyen Thi Ngoc Anh Hanoi Univ Sci & Technol CMC Inst Sci & Technol Hanoi Vietnam

ISBN: (纸本)9781728130033

Car pricing using machine learning has a strong relationship with the process of knowledge acquisition for expert systems. Recently, the primary technique for knowledge acquisition has been the time-consuming process of recommendation, posting for car buying or selling on internet market websites. After discovering the data, we can divide that into two types: structured and unstructured that require knowledge-based analysis. This paper will involve the techniques for extraction of meaning, data inference, and rules for qualitative data. The main purpose of the current research is to explore different data types of car data and the objective is to create an automated technique to predict car prices.

关键词： Prediction Car Pricing Entity Embedding Quantify Qualitative data Knowledge-based System

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Feature Selection using Random Forest Classifier for Predicting Prostate Cancer 9

Feature Selection using Random Forest Classifier for Predict...

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9th Annual Basic science international conference (BaSIC) - Recent Advances in Basic sciences Toward 4.0 Industrial Revolution

作者： Huljanah, Mia Rustam, Zuherman Utama, Suarsih Siswantining, Titin Univ Indonesia Dept Math Depok 16424 Indonesia

Prostate cancer is cancer that attacks the prostate gland, usually affecting men over 50 years. Prostate cancer is a disease that develops slowly. Based on this, rapid and precise detection is needed so that the disease can be treated immediately. This study focuses on the application Feature Selection using the Random Forest Classifier to detect prostate cancer. The Random Forest Classifier is a method of classifying data by determining the decision tree. The use of more trees will affect the accuracy to be obtained for the better. The Random Forest Classifier can classify data that has incomplete attributes and can be used to handle large sample data. Selection of features is an important process because it can affect the accuracy of classification. This method increases accuracy by about 87%. Thus, the selection of features can improve accuracy in the detection of prostate cancer.

关键词： Random Forest Classifier Prostate Cancer machine learning Feature Selection

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A survey on game theoretic approaches for privacy preservation in data mining and network security 16

A survey on game theoretic approaches for privacy preservati...

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16th international conference on Mobile Systems and Pervasive Computing (MobiSPC) / 14th international conference on Future Networks and Communications (FNC) / 9th international conference on Sustainable Energy Information Technology

作者： Shah, Hitarth Kakkad, Vishruti Patel, Reema Doshi, Nishant Pandit Deendayal Petr Univ Gandhinagar India

Game theory has found widespread use in various fields like Economics, Biology, Political science, etc. and forms an aegis for logical decision making in these areas. In computer science, due to advancing technologies, there has been a pressing need to use game theory in various problems due to the lack of scalability of traditional solutions. There has been ongoing research in various fields of computer science like security, machine learning, cloud computing, etc. where game theoretic approaches are extensively used. In this paper, we present a review on game theoretical approaches to various fields in computer science such as privacy preservation, network security and intrusion detection and resource optimization In the end, this paper provides a comparative study of various game models used in different applications in a tabular format. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Game Theory Privacy Preservation Network Security Intrusion Detection and Resource Optimization

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