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共 3281 条记录，以下是2311-2320 订阅

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Privacy preserving data mining — ‘A state of the art’

Privacy preserving data mining — ‘A state of the art’

引用

international conference on Computing for Sustainable Global Development (INDIACom)

作者： Mamta Narwaria Suchita Arya School of Computer Science & Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781467394178

The process of extraction of interesting patterns or knowledge from the bulk of data refers to the data mining technique. “It is the process of discovering meaningful, new correlation patterns and trends through non-trivial extraction of implicit, previously unknown information from large amount of data stored in repositories using pattern recognition as well as statistical and mathematical techniques”. Due to the wide deployment of Internet and information technology, storage and processing of data technologies, the ever-growing privacy concern has been a major issue in data mining for information sharing. This gave rise to a new path in research, known as Privacy Preserving data mining (PPDM). The literature paper discusses various privacy preserving data mining algorithms and provide a wide analyses for the representative techniques for privacy preserving data mining along with their merits and demerits. The paper describes an overview of some of the well-known PPDM algorithms. Most of the algorithms are usually a modification of a well-known data-mining algorithm along with some privacy preserving techniques. This paper also focuses on the problems and directions for the future research here. The paper finally discusses the comparative analysis of some well-known privacy preservation techniques that are used. This paper is intended to be a summary and an overview of PPDM.

关键词： data privacy Algorithm design and analysis Distributed databases Classification algorithms Privacy

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Classification and Prediction of Heart Disease Risk Using data mining Techniques of Support Vector machine and Artificial Neural Network

Classification and Prediction of Heart Disease Risk Using Da...

引用

international conference on Computing for Sustainable Global Development

作者： S. Radhimeenakshi Bharathiar University Department of Computer Science Tiruppur Kumaran College for Women

ISBN: (纸本)9781467394178

Classification of coronary Heart Disease can be valuable for the medical practitioners in the event that it is automated with the end goal of quick finding and exact result. Foreseeing the presence of Heart Disease precisely can spare patients living days. The target of this paper is to break down the use of AI devices for order and expectation of heart illness. The work incorporates the classes of Heart Disease utilizing Support Vector machine (SVM) as well as Artificial Neural Network (ANN). Examination is completed among two strategies on the premise of accuracy and training time. This paper introduces a medical choice backing framework for coronary illness characterization in a sane, purpose, precise and fast manner. The dataset utilized are the Cleveland Heart database and Statlog database taken from UCI machine learning dataset vault. In the proposed system model we arrange the data records into two classes in Support Vector machine as well as Artificial Neural Network. Also analyze the performance of the both the datasets.

关键词： Heart Disease data mining (DM) Support Vector machine (SVM) Kernel functions Artificial Neural Network (ANN) Back Propagation

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Web Identification Image recognition Based on Deep learning

Web Identification Image Recognition Based on Deep Learning

引用

international conference on Information Science and Control Engineering (ICISCE)

作者： Yanling Zhao Xinchang Zhang Mei Xu Zhanquan Sun Guangqi Liu Shifeng Li Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center (National Supercomputer Center in Jinan) Jinan China Public Security Department of Shandong Province Jinan China

ISBN: (纸本)9781509025367

In big data era, digital information is growing rapidly. False and unlawful images influence our normal work and life, especially the exaggerated or fake propaganda of electronic commerce merchants. In this article, our purpose is to help people find out fake qualification certificate information automatically. Base on collecting and classifying web images, we apply Convolutional Neural Network (CNN) method to train a network and extract the corresponding models to recognize web identification images based on Caffe toolbox with GPU. It is saving costs on man power and material resources with high efficiency once the CNN model is trained. This model can be designed to find identification images and contrast in database. The experimental results show it is worked well in recognizing identification images with high efficiency.

关键词： machine learning Image recognition Training databases Big data Visualization Neural networks

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Unsupervised Feature Selection Algorithm Based on Sparse Representation

Unsupervised Feature Selection Algorithm Based on Sparse Rep...

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international conference on Systems and Informatics

作者： Guoqing Cui Jie Yang Masoumeh Zareapoor Jiechen Wang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Complex Network and Control Lab Shanghai Jiao Tong University

ISBN: (纸本)9781509055227

Feature selection, as a preprocessing step to machine learning, plays a pivotal role in removing irrelevant data, reducing dimensionality and improving performance evaluations. Recent years, sparse representation has become a useful tool for both supervised and unsupervised feature selection. So far, most of these algorithms still have many problems such as large computation load, performance with poor stability. Thus, this paper proposes a new unsupervised feature selection algorithm via sparse representation (UFSSR), with respect to efficiency and effectiveness. Firstly, this paper reconstructs part of data matrix via sparse representation, which makes the proposed algorithm be robust and independent of domain knowledge. Then, to reduce the reconstruction error, a new feature evaluation function is given to rank all features. Theoretical analysis and experiments compared with many popular algorithms on a set of datasets demonstrate the improvements brought by UFSSR.

关键词： Unsupervised feature selection Sparse representation Feature evaluation function Computation load Stability

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Construction of gazetteers from geo big data using machine learning technique on Hadoop

Construction of gazetteers from geo big data using machine l...

引用

international conference on Computing for Sustainable Global Development (INDIACom)

作者： S. Pradeepa K. R. Manjula Information Technology SASTRA University Thanjavur

ISBN: (纸本)9781467394178

Most gazetteers have been built and maintained for the purpose of visualizing geographical location on the Geographical Information system (GIS) client. The advent of big data allows us to construct gazetteers by directly mining rich volunteered information from the web. In this, we propose a technique for extracting location based spatial information from the web documents and media services like flickr, twitter, facebook for construction of gazetteers. To achieve this, we need to search the web for existing data pertaining location. A web crawler (Google search engine) generates the web pages based on the location keyword given by the user and maintaining the index of the web pages and the proposed system passes it to the Hadoop environment. For further simplification, the name node transfers the index group of web pages to different data nodes for extraction of spatial information from the dynamic web documents that we gather using machine learning process. Each data node is then utilized for the generation of a common template. The common template allows the extraction of location based spatial information from the dynamic web documents and media services. Resultant information from the data node is further merged using map reduce algorithms and the Hadoop Distributed File System (HDFS) is produced which is then converted to Geo-Java Script Object Notation (JSON) format, thus aiding in the task of visualizing the extracted information on the GIS client.

关键词： Web pages Spatial databases data mining data visualization Crawlers Hospitals File systems

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machine learning techniques for effective text analysis of social network E-health data

Machine learning techniques for effective text analysis of s...

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international conference on Computing for Sustainable Global Development (INDIACom)

作者： Sonia Saini Shruti Kohli Birla Institute of Technology NOIDA U.P. INDIA Faculty of Computer Science & Engineering Birla Institute of Technology NOIDA U.P. INDIA

ISBN: (纸本)9781467394178

The World Wide Web has evolved very drastically and the recent advent in social networking and media rich websites has necessitated analysis of the social networks and opinions expressed by various users in media like blogs, tweets, and website pages alike. While a lot of previous researches have been done on product / CRM domain, relatively few research has been done on the network analysis of social media with a focus on extraction and opinion of e-Health data i.e. data expressed in various blogs or website pages by users regarding various aspects of their health. While marketing and medical companies can leverage this data to augment customer reach and thus further their profits, sentiment analysis and network analysis on the data can help various organizations understand health patterns, address people's concerns or predict outbreak patterns in case of contagious diseases. This paper outlines the machine learning techniques which are helpful in the analysis of medical domain data from Social networks. In this paper we compare the existing techniques of machine learning, discuss the advantages and challenges encompassing the perspectives involving the use of text mining methods for applications in E-health and medicine. We evaluate medical domain data classification efficiency using various metrics like ROC, AUC implemented via R language packages. We need to infer which machine learning technique is more relevant for processing data from social networks having medical terms in it.

关键词： Decision support systems Handheld computers

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Understanding behavioral differences between short and long-term drinking abstainers from social media 1

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12th international conference on Advanced data mining and Applications, ADMA 2016

作者： Harikumar, Haripriya Nguyen, Thin Gupta, Sunil Rana, Santu Kaimal, Ramachandra Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia Computer Science and Engineering Department Amrita University Kollam India

ISBN: (数字)9783319495866

ISBN: (纸本)9783319495859

Drinking alcohol has high cost on society. The journey from being a regular drinker to a successful quitter may be a long and hard journey, fraught with the risk to relapse. Research has shown that certain behavioral changes can be effective towards staying abstained. Traditional way to conduct research on drinking abstainers uses questionnaire based approach to collect data from a curated group of people. However, it is an expensive approach in both cost and time and often results in small data with less diversity. Recently, social media has emerged as a rich data source. Reddit is one such social media platform that has a community (‘subreddit’) with an interest to quit drinking. The discussions among the group dates back to year 2011 and contain more than 40,000 posts. This large scale data is generated by users themselves and without being limited by any survey questionnaires. The most predictive factors from the features (unigrams, topics and LIWC) associated with short-term and long-term abstinence are identified using Lasso. It is seen that many common patterns manifest in unigrams, topics and LIWC. Whilst topics provided much richer associations between a group of words and the outcome, unigrams and LIWC are found to be good at finding highly predictive solo and psycho linguistically important words. Combining them we have found that many interesting patterns that are associated with the successful attempt made by the long-term abstainer, at the same time finding many of the common issues faced during the initial period of abstinence. © Springer international Publishing AG 2016.

关键词： Surveys

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Building a new model for feature optimization in agricultural sectors

Building a new model for feature optimization in agricultura...

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international conference on Computing for Sustainable Global Development (INDIACom)

作者： Soumya Sahoo Sushruta Mishra Bijayalaxmi Panda Nachiketa Jena C.V Raman College of Engineering Bhubaneswar India Natioual Institute of Technology Rourkela India

ISBN: (纸本)9781467394178

Agriculture is an integral part of India. There are several factors that impact a healthy agricultural production. Enhancing the stability and growth of agricultural sector with the help of advanced technologies is a primary issue to be dealt with. machine learning is an emerging area that can improve the agricultural productivity in India. In our research we have considered two vital agro datasets like mushroom and Soyabean. These datasets are subjected to PSO search algorithm that acts as an attribute selector. It reduces the datasets and removes the noisy features. To the resultant dataset different classifiers like Naive Bayes, Decision table are implemented and the final inference is deducted. The result was tested with some performance metrics like RMS and Classification accuracy. It is observed that machine learning methods in combination with PSO search technique act as a positive factor in enhancing the overall performance.

关键词： Agriculture conferences data mining Optimization Computational modeling Classification algorithms Niobium

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Diagnosing of heart diseases using average k-nearest neighbor algorithm of data mining

Diagnosing of heart diseases using average k-nearest neighbo...

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international conference on Computing for Sustainable Global Development (INDIACom)

作者： C. Kalaiselvi Tiruppur Kumaran College for Women Associate Professor and Head of Computer Applications Tirupur Tamilnadu India

ISBN: (纸本)9781467394178

The most important and prevalent diseases that commonly occur in people and causes 80% of death in country is heart disease. It is estimated that by 2030, approximately 25 millionpeople die because of heart diseases. Though many researchers have suggested and proposed methods for diagnosing the heart diseases from the enormous amount of heart disease data, there is no proper effective techniques and are not properly mined. To get improved classification accuracy and efficiency a new approach called average k-nearest neighbor algorithm is proposed in this paper. The dataset used for prediction is obtained and utilized from UCI machine learning repositories. The main objective of this research work is to diagnose heart disease with reduced number of attributes that are relevant to heart diseases.

关键词： Handheld computers Decision support systems conferences

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Oving K-Means Clustering using discretization technique in Network Intrusion Detection System

Oving K-Means Clustering using discretization technique in N...

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international conference on Computer and Information Sciences (ICCOINS)

作者： Hatim Mohamad Tahir Abas Md Said Nor Hayani Osman Nur Haryani Zakaria Puteri Nurul 'Ain M Sabri Norliza Katuk School of Computing Universiti Utara Malaysia Universiti Teknologi PETRONAS Bandar Seri Iskandar Perak MALAYSIA

Network Intrusion Detection Systems (NIDSs) have always been designed to enhance and improve the network security issue by detecting, identifying, assessing and reporting any unauthorized and illegal network connections and activities. The purpose of this research is to improve on the existing Anomaly Based Intrusion Detection (ABID) method using K-Means clustering technique as to maximize the detection rate and accuracy while minimizing the false alarm. The problem with outliers may disturb the K-Means clustering process as it might be avoided in the clustering process from mixing with the normal data that make the NIDSs become less accurate. Thus this research aims to improve the performance of the ABID systems that balance the loss of information or ignored data in clustering. An integrated machine learning algorithm using K-Means Clustering with discretization technique and Naïve Bayes Classifier (KMC-D+NBC) is proposed against ISCX 2012 Intrusion Detection Evaluation dataset. The outcome depicts that the proposed method generates better detection rate and accuracy up to 99.3% and 99.5% respectively and reduces the false alarm to 1.2% with better efficiency of 0.03 seconds time taken to build model.

关键词： Intrusion detection data mining Clustering algorithms machine learning algorithms Training data Computers Algorithm design and analysis

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