2018 Second International Conference on Computing Methodologies and Communication (ICCMC)最新文献_第6页

Evaluating the Usability and Learnability of the "Blackboard" LMS Using SUS and Data Mining 利用SUS和数据挖掘评估“黑板”LMS的可用性和可学习性

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2018-02-01 DOI: 10.1109/ICCMC.2018.8488038

Khalid Al-Omar

引用次数: 13

A Modern Approach for Load Balancing Using Forest Optimization Algorithm 基于森林优化算法的现代负载平衡方法

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2018-02-01 DOI: 10.1109/ICCMC.2018.8487765

Subasish Mohapatra, Ishan Aryendu, Anshuman Panda, A. K. Padhi

引用次数: 5

PARTS OF SPEECH TAGGING FOR KONKANI LANGUAGE konkani语词性标注

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2018-02-01 DOI: 10.1109/iccmc.2018.8487620

Diksha N. Prabhu Khorjuvenkar, Megha Ainapurkar, S. Chagas

引用次数: 5

Improved Automatic Feature Selection Approach for Health Risk Prediction 一种改进的健康风险预测自动特征选择方法

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2018-02-01 DOI: 10.1109/ICCMC.2018.8487695

Shreyal Gajare, S. Sonawani

{"title":"Improved Automatic Feature Selection Approach for Health Risk Prediction","authors":"Shreyal Gajare, S. Sonawani","doi":"10.1109/ICCMC.2018.8487695","DOIUrl":"https://doi.org/10.1109/ICCMC.2018.8487695","url":null,"abstract":"With the recent advances in machines & technology, there is revolution in healthcare industry also. Thus, it gave rise to the concept of Electronic Health Record (EHR) which stores patients demographics, lab tests & results, medical history, habits etc. in electronic form. EHR is voluminous data which is difficult to store, maintain or alter. To provide extended life for people, health risk prediction model using this EHR is formulated in this work. Feature Selection is used to select only associated or relevant data from the dataset. Logistic Regression is used with improved loss functionality parameter which increases the accuracy, response time and performance of the system. Representation Learning enables formation of feature vector of the selected features thus calculating their scores. Further, risk prediction is performed by the neural network model. Deep Neural Network (DNN) is used with many hidden layers containing activation functions. Transfer learning is used to avoid re-training of the whole system every time new data enters the model. Dataset used here is of hypertension. EHR dataset is also synthetically created for analysis.","PeriodicalId":6604,"journal":{"name":"2018 Second International Conference on Computing Methodologies and Communication (ICCMC)","volume":"10 1","pages":"816-819"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78401649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Survey on clustering techniques in Twitter data Twitter数据聚类技术综述

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2018-02-01 DOI: 10.1109/ICCMC.2018.8487969

R. Devika, S. Revathy, Sai surriya Priyanka U, V. Subramaniya swamy

引用次数: 3

Identification Of User Preference by Sequential Pattern Mining and Recommendation of Products using Geo-tagged data 基于顺序模式挖掘的用户偏好识别和使用地理标记数据的产品推荐

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2018-02-01 DOI: 10.1109/ICCMC.2018.8487238

R. Kanmani, V. Uma

{"title":"Identification Of User Preference by Sequential Pattern Mining and Recommendation of Products using Geo-tagged data","authors":"R. Kanmani, V. Uma","doi":"10.1109/ICCMC.2018.8487238","DOIUrl":"https://doi.org/10.1109/ICCMC.2018.8487238","url":null,"abstract":"Recommendation System provides the user with the interesting materials which are extracted from their preference. For simplifying the information retrieval and in order to provide the user with preferred result with more accuracy, recommendation system is being used. Recommendation based services are also used in social networks such as Facebook, Twitter, Instagram etc. Geo-tagged data plays a major role in case of recommendation systems as they will be providing recommendations with respect to the users locations. The semantic classification of the location is done using Support Vector Machine. By considering the location co-ordinates the nearest possible travel routes are identified by Google Maps and the shorter distance are computed using k-Nearest Neighbour. In this work, recommendation of products is given by means of considering the frequent buying pattern of the user using Prefix span algorithm, similar users ratings computed by Collaborative Filtering and the popular items available on the travel route. The proposed system has been implemented and evaluated.","PeriodicalId":6604,"journal":{"name":"2018 Second International Conference on Computing Methodologies and Communication (ICCMC)","volume":"20 1","pages":"108-113"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72870104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance Evaluation of Face Recognition System using various Distance Classifiers 使用不同距离分类器的人脸识别系统性能评价

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2018-02-01 DOI: 10.1109/ICCMC.2018.8487835

Preeti, Dinesh Kumar

引用次数: 3

Multiple Sensitive Attributes Based Privacy Preserving Data Publishing 基于多敏感属性的隐私保护数据发布

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2018-02-01 DOI: 10.1109/ICCMC.2018.8487483

Jasmina N Vanasiwala, Nirali R. Nanavati

{"title":"Multiple Sensitive Attributes Based Privacy Preserving Data Publishing","authors":"Jasmina N Vanasiwala, Nirali R. Nanavati","doi":"10.1109/ICCMC.2018.8487483","DOIUrl":"https://doi.org/10.1109/ICCMC.2018.8487483","url":null,"abstract":"The advances in digital information applications facilitates the collection of huge amount of data about governments, healthcare, other organizations and individuals. To make this data available for researchers, businesses and other users, it needs to be released. This in turn increases the demand of exchanging and publishing this collected data. However, data in its original form, typically contains sensitive information about individuals and/or organizations, and publishing such data will violate individual or organizational privacy. Hence, Privacy Preserving Data Publishing (PPDP) provides methods and tools for publishing useful information while preserving data privacy. Before data is published to the concerned users, it is altered to maintain its privacy without compromising data utility, using various anonymization techniques. Real-time datasets contain different types of Multiple Sensitive Attributes (MSAs) (which could be numerical or categorical). Anonymization for only Single Sensitive Attribute is not suitable for functional usage. Thus, it is important to maintain the association between these MSAs and to preserve the privacy of Mixed (numerical and categorical) MSAs efficiently while working with high dimensional data. The main focus of this paper is to analyse the different schemes proposed in literature for PPDP of MSAs.","PeriodicalId":6604,"journal":{"name":"2018 Second International Conference on Computing Methodologies and Communication (ICCMC)","volume":"22 1","pages":"394-400"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85396597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

EAACK - Enhanced Adaptive Acknowledgment 增强的自适应承认

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2018-02-01 DOI: 10.1109/ICCMC.2018.8487752

V. Sanju, G. Venu, Reenu Sara Joseph, M. Stephen, Melvin Mathew

引用次数: 0

Multilayered Risk analysis of Mobile systems and Apps 移动系统和应用程序的多层风险分析

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2018-02-01 DOI: 10.1109/ICCMC.2018.8487535

R. Katarya, Chhavi Jain

引用次数: 0