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检索条件"任意字段=4th International Conference on Machine Learning and Data Minining in Pattern Recognition"

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A Survey on machine learning Techniques in Network Intrusion Detection System

A Survey on Machine Learning Techniques in Network Intrusion...

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international conference on Computing, Communication & Automation (ICCCA)

作者： B.Ida Seraphim Shreya Palit Kaustubh Srivastava E Poovammal Computer Science and Engineering SRM IST Kattankulathur Tamil Nadu India

ISBN: (纸本)9781538669488

Huge amounts of data is being generated every second due to technological development and innovations coming out in the market. Social Networking and the cloud computing boom in this technological world is generating large amounts of data every second. Streaming data, a technical term was devised to represent the constant generation of data on the World Wide Web. these data are constantly putting pressure and checking its limits on the current Intrusion Detection System(IDS). these systems are a device or software applications that keep a track log as well as monitors any malicious activity happening on the network. In order to detect and then prevent network security breaches in the system, many machine learning algorithms have been applied to the data generated in order to recognize any pattern in anomaly actives that are happening over the network system.

关键词： Classification algorithms Intrusion detection Clustering algorithms data mining Feature extraction Decision trees Principal component analysis

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A twin support vector machine based approach to classifying human skin

A twin support vector machine based approach to classifying ...

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international conference on Computing, Communication & Automation (ICCCA)

作者： Mayank Arya Chandra S S Bedi MJP Rohilkhand University Bareilly India

ISBN: (纸本)9781538669488

the classification of color image that contain skin pixel is a challenging process. It is challenging due to certain factor such as brightness, background similar to human skin represent the obstacles thereof. the basic approach of skin detection is color based classification. the human skin image is the composition of RGB color. the data values of skin is calculated by randomly sampling RGB color values of different face images which have different age, race, genders sets. However, its performance has not really been high because of the high overlapped degree between “skin” and “non-skin” pixels. this paper describes the linear norm fuzzy based twin support vector machine (LN-FTSVM)) approach for discriminate skin and non-skin data values of skin dataset and to enhance the skin recognition performance. the concept of fuzzy is resolved the unclassified and overlapped data region problems. If no decision function is positive for a data set, this data set is classified into a class with the large membership value. By computational experiments shows that Experiments result shows that the accuracy is improved by linear norm fuzzy based TSVM (LN-FTSVM)) over the conventional methods.

关键词： Skin Support vector machines Image color analysis Classification algorithms machine learning algorithms machine learning Adaptation models

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Modified Naive Bayes Algorithm for Network Intrusion Detection based on Artificial Bee Colony Algorithm

Modified Naive Bayes Algorithm for Network Intrusion Detecti...

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IEEE international Symposium on Wireless Systems (IDAACS-SWS)

作者： Juan Yang Zhiwei Ye Lingyu Yan Wei Gu Ruoxi Wang School of Computer Science Hubei University of Technology Wuhan China Wuhan FiberHome Technical Services Co. Ltd.

ISBN: (纸本)9781538675885

Intrusion detection algorithm based on machine learning is a research hotspot in network security detection. the diversity of network intrusion detection data sets is one of the major factors that affect the practical application of machine learning. therefore, some swarm intelligence algorithms were utilized to optimize parameters of machine learning methods for feature selection or feature weight in network intrusion. In this paper, a modified Naive Bayes algorithm based on artificial bee colony algorithm (ABCWNB, in brief) is proposed. the proposed method is tested on two public data sets and NSL-KDD data sets. Experimental results show that compared to Naive Bayes classifier based on genetic algorithm (GAWNB), Naive Bayes classifier based on grey wolf optimizer (GWOWNB), Naive Bayes classifier based on water wave optimization (WWOWNB) and basic Naive Bayes classifier, the proposed method can effectively improve the network intrusion detection rate, which can well detect various types of network intrusion and greatly improve the security performance of the network.

关键词： artificial bee colony algorithm Bayes methods computer network security feature selection genetic algorithms learning (artificial intelligence) particle swarm optimisation pattern classification artificial bee colony algorithm Network security Bayesian classifier data Security Feature extraction Particle swarm optimization Bayes methods pattern recognition Genetic Algorithm (GA) dataset Bayes

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Crowdsourced annotations as an additional form of data augmentation for CAD development 4

Crowdsourced annotations as an additional form of data augme...

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4th IAPR Asian conference on pattern recognition (ACPR)

作者： Pujitha, Appan K. Sivaswamy, Jayanthi IIIT Ctr Visual Informat Technol Hyderabad India

ISBN: (纸本)9781538633540

Annotations are critical for machine learning and developing Computer Aided Detection (CAD) algorithms. However, a majority of medical data is either unlabeled or annotated only at the image-level. this poses a problem specifically for employing deep learning based approaches for CAD development as they require large amounts of annotated data for training. data augmentation is a popular solution to address this need. We explore crowdsourcing as a solution for training a deep neural network (DNN) for lesion detection. Our solution employs a strategy to overcome the noisy nature of crowdsourced annotations by i) assigning a reliability factor for each subject of the crowd based on their performance (at global and local levels) and experience and ii) requiring region of interest (ROI) markings rather than pixel-level markings from the crowd. We present a solution for training the DNN with data drawn from a heterogeneous mixture of annotations, namely, very limited number of pixel-level markings by experts and crowdsourced ROI markings. Experimental results obtained for hard exudate detection from color fundus images show that training with processed/refined crowdsourced data is effective as detection performance improves by 25% over training with just expert-markings and by 11% over training with annotation derived using majority voting among the crowd.

关键词： Crowdsourcing Reliability Factor Deep Neural Net

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A Novel Deep-learning based Framework for Multi-Subject Emotion recognition

A Novel Deep-Learning based Framework for Multi-Subject Emot...

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

作者： Qiao, Rui Qing, Chunmei Zhang, Tong Xing, Xiaofen Xu, Xiangmin South China Univ Technol Sch Elect & Informat Engn Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538632574

Electroencephalogram (EEG) signal based emotion recognition, as a challenging pattern recognition task, has attracted more and more attention in recent years and widely used in medical, Affective Computing and other fields. Traditional approaches often lack of the high-level features and the generalization ability is poor, which are difficult to apply to the practical application. In this paper, we proposed a novel model for multi-subject emotion classification. the basic idea is to extract the high-level features through the deep learning model and transform traditional subject-independent recognition tasks into multi-subject recognition tasks. Experiments are carried out on the DEAP dataset, and our results demonstrate the effectiveness of the proposed method.

关键词： Feature extraction Brain modeling Computational modeling Electroencephalography data models Two dimensional displays Emotion recognition emotion recognition Brain modeling data models Electroencephalography Computational modeling Feature extraction High-level features

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Structured Discriminative Dictionary learning Based on Schatten-p norm Low-rank Representation 4

Structured Discriminative Dictionary Learning Based on Schat...

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4th IAPR Asian conference on pattern recognition (ACPR)

作者： Zheng, Hao Chang, Heyou Zhang, Fanlong Nanjing XiaoZhuang Univ Key Lab Trusted Cloud Comp & Big Data Anal Nanjing Jiangsu Peoples R China Nanjing Audit Univ Sch Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538633540

Dictionary plays an important role in low-rank representation, which has been widely used to deal with the data contaminated by noise. Traditional low-rank representation relax the non-convex rank minimization problem by the convex nuclear norm minimization, which makes the obtained solution seriously deviate from the practical solution. Meanwhile, the discriminative information between data and dictionary is not effectively exploited. To address these problems, this paper proposed to apply non-convex Schatten-p norm (p = 1/2) to approximate the rank minimization problem. Since Schatten-p norm (p = 1/2) is much closer to the rank function than nuclear norm, the proposed strategy can faithfully reflect the essential structure of noisy data. Moreover, a weighted representation regularization term, which encourages to generate a coefficient matrix with class-wise block-diagonal structure, is constructed to enhance the discrimination of dictionary. Experimental results demonstrate advantages of the proposed method over previous dictionary learning methods.

关键词： Dictionaries machine learning Training data Minimization Sparse matrices Training Optimization

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Study and Observation of the Variations of Accuracies for Handwritten Digits recognition with Various Hidden Layers and Epochs using Neural Network Algorithm

Study and Observation of the Variations of Accuracies for Ha...

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international conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Md. Abu Bakr Siddique Mohammad Mahmudur Rahman Khan Rezoana Bente Arif Zahidun Ashrafi Dept. of EEE International University of Business Agriculture and Technology Dhaka Bangladesh Dept. of ECE Mississippi State University MS Mississippi State USA Dept. of Statistics Rajshahi College Rajshahi Bangladesh

In recent days, Artificial Neural Network (ANN) can be applied to a vast majority of fields including business, medicine, engineering, etc. the most popular areas where ANN is employed nowadays are pattern and sequence recognition, novelty detection, character recognition, regression analysis, speech recognition, image compression, stock market prediction, Electronic nose, security, loan applications, data processing, robotics, and control. the benefits associated with its broad applications leads to increasing popularity of ANN in the era of 21st Century. ANN confers many benefits such as organic learning, nonlinear data processing, fault tolerance, and self-repairing compared to other conventional approaches. the primary objective of this paper is to analyze the influence of the hidden layers of a neural network over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the MNIST dataset. Also, another goal is to observe the variations of accuracies of ANN for different numbers of hidden layers and epochs and to compare and contrast among them.

关键词： Neurons Biological neural networks Artificial neural networks Handwriting recognition Speech recognition Artificial intelligence Character recognition

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Deep learning and its Importance for Early Signature of Neuronal Disorders

Deep Learning and its Importance for Early Signature of Neur...

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international conference on Computing, Communication & Automation (ICCCA)

作者： Sudip Paul Jitendra Kumar Verma Amitava Datta Rabindra Nath Shaw Angana Saikia School of Computer Science & Software Engineering The University of Western Australia Perth Australia School of Computing Science & Engineering Galgotias University Greater Noida India SECE Galgotias University Greater Noida India Department of Biomedical Engineering North-Eastern Hill University Shillong India

ISBN: (纸本)9781538669488

Deep learning is a nomenclature given to a recently developed domain of pattern recognition. It has potency to influence the development of artificial intelligence research. the concept of deep learning has been originated from the capability of making educated decisions/guesses regarding a topic/situation in spite of having no prior experience. From mathematical perspective, deep learning allows the extraction of features from a given set of data which allows much more capable and accurate training for a certain technique. With the advent of more powerful mathematical techniques deep learning is seen as an effective method to enhance the capabilities of presently developed algorithms without needlessly complicating their mode of operation. this paper presents importance of deep learning to detect neural disorder.

关键词： Electroencephalography Deep learning Biological neural networks Electrodes Recurrent neural networks Convolution Diseases

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Iris Flower Species Identification Using machine learning Approach

Iris Flower Species Identification Using Machine Learning Ap...

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international conference for Convergence of Technology (I2CT)

作者： Joylin Priya Pinto Soumya Kelur Jyothi Shetty NMAM Institute of Technology Nitte

ISBN: (数字)9781538652329

ISBN: (纸本)9781538652336

Classification is one of the most important approach of machine learning. Main task of machine learning is data analysis. Various algorithms are available for classification like decision tree, Navie Bayes, Back propagation, Neural Network, Artificial Neural, Multi-layer perception, Multi class classification, Support vector machine, K-nearest neighbor etc. In this paper, three methods are explained in detail. Implementation is done using iris dataset. Scikit tool is used for the implementation purpose. this paper mainly applies classification and regression algorithms on IRIS dataset, by discovering and analyzing the patterns, using sepal and petal size of the flower. We have found that SVM classifier gives best accuracy compared to KNN and logistic regression models.

关键词： Iris Iris recognition machine learning Support vector machines Logistics Training Classification algorithms

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Randomized algorithms for some clustering problems 7th

Randomized algorithms for some clustering problems

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7th international conference on Optimization Problems and their Applications, OPTA 2018

作者： Kel’manov, Alexander Khandeev, Vladimir Panasenko, Anna Sobolev Institute of Mathematics 4 Koptyug Avenue Novosibirsk630090 Russia Novosibirsk State University 2 Pirogova Street Novosibirsk630090 Russia

ISBN: (纸本)9783319937991

We consider two strongly NP-hard problems of clustering a finite set of points in Euclidean Space. Both problems have applications, in particular, in data analysis, data mining, pattern recognition, and machine learning. In the first problem, an input set is given and we need to find a cluster (i.e., a subset) of a given size which minimizes the sum of squared distances between the elements of this cluster and its centroid (the geometric center). Every point outside this cluster is considered as singleton cluster. In the second problem, we need to partition a finite set into two clusters minimizing the sum over both clusters of the weighted intracluster sums of the squared distances between the elements of the clusters and their centers. the center of the first cluster is unknown and determined as the centroid, while the center of the second one is the origin. the weight factors for both intracluster sums are the given clusters sizes. In this paper, we present parameterized randomized algorithms for these problems. For given upper bounds of the relative error and failure probability, the parameter value is defined for which both our algorithms find approximate solutions in a polynomial time. this running time is linear on the space dimension and on the input set size. the conditions are found under which these algorithms are asymptotically exact and have the time complexity that is linear on the space dimension and quadratic on the size of the input set. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： Approximation algorithms

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