2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS)最新文献_第4页

Predictive Analytics of Sensor Data Based on Supervised Machine Learning Algorithms 基于监督机器学习算法的传感器数据预测分析

2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS) Pub Date : 2017-12-01 DOI: 10.1109/ICNGCIS.2017.12

Shreya Gupta, Mohit Mittal, Anupama Padha

{"title":"Predictive Analytics of Sensor Data Based on Supervised Machine Learning Algorithms","authors":"Shreya Gupta, Mohit Mittal, Anupama Padha","doi":"10.1109/ICNGCIS.2017.12","DOIUrl":"https://doi.org/10.1109/ICNGCIS.2017.12","url":null,"abstract":"Wireless sensor network (WSN) is one of emerging technologies in today's scenario. Due to progressive advancement in micro-electro-mechanical system (MEMS) technology it can easily deployed in harsh environment. Sensor node communicates with their neighboring sensor nodes via radio frequencies and has many notable capabilities like self-configurable, self-manageable and monitoring physical phenomenon. Wireless sensor network is gaining popularity due to presence of many characteristics like cheap, cost-effective, reliable etc. along with this it has one major challenge that is limited battery life. To overcome this challenge, many solutions have found till date such as improvising routing protocols, reduction in computation of data, modification in time-stamp synchronization etc but still need more work. In this paper, our major focus is on processing of sensor dataset using various machine learning algorithms. We have managed different range of datasets from hundreds to thousands values and processed with various supervised machine learning algorithms. Simulation result shows that Gaussian Naive Bayes algorithm prominently gives better results than other algorithms in terms of accuracy parameter.","PeriodicalId":314733,"journal":{"name":"2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122149736","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: 12

Analysis of Stock Price Flow Based on Social Media Sentiments 基于社交媒体情绪的股票价格流动分析

2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS) Pub Date : 2017-12-01 DOI: 10.1109/ICNGCIS.2017.34

Nishant Suman, P. Gupta, Pankaj Sharma

引用次数: 7

A QoS-Based Reactive Auto Scaler for Cloud Environment 云环境下基于qos的响应式自动缩放器

2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS) Pub Date : 2017-12-01 DOI: 10.1109/ICNGCIS.2017.22

Dhrub Kumar, N. Gondhi

引用次数: 2

An Overview of Data Storage in Cloud Computing 云计算中的数据存储概述

2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS) Pub Date : 2017-12-01 DOI: 10.1109/ICNGCIS.2017.9

I. Odun-Ayo, Olasupo O. Ajayi, B. Akanle, Ravin Ahuja

{"title":"An Overview of Data Storage in Cloud Computing","authors":"I. Odun-Ayo, Olasupo O. Ajayi, B. Akanle, Ravin Ahuja","doi":"10.1109/ICNGCIS.2017.9","DOIUrl":"https://doi.org/10.1109/ICNGCIS.2017.9","url":null,"abstract":"Cloud computing is a functional paradigm that is evolving and making IT utilization easier by the day for consumers. Cloud computing offers standardized applications to users online and in a manner that can be accessed regularly. Such applications can be accessed by as many persons as permitted within an organisation without bothering about the maintenance of such application. The Cloud also provides a channel to design and deploy user applications including its storage space and database without bothering about the underlying operating system. The application can run without consideration for on-premise infrastructure. Also, the Cloud makes massive storage available both for data and databases. Storage of data on the Cloud is one of the core activities in Cloud computing. Storage utilizes infrastructure spread across several geographical locations. Storage on the Cloud makes use of the internet, virtualization, encryption and others technologies to ensure security of data. This paper presents the state of the art from some literature available on Cloud storage. The study was executed by means of review of literature available on Cloud storage. It examines present trends in the area of Cloud storage and provides a guide for future research. The objective of this paper is to answer the question of what the current trend and development in Cloud storage is? The expected result at the end of this review is the identification of trends in Cloud storage, which can beneficial to prospective Cloud researches, users and even providers.","PeriodicalId":314733,"journal":{"name":"2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116107520","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: 22

A Comprehensive Study of Big Data Machine Learning Approaches and Challenges 大数据机器学习方法与挑战的综合研究

2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS) Pub Date : 2017-12-01 DOI: 10.1109/ICNGCIS.2017.14

Neelam Singh, D. P. Singh, B. Pant

{"title":"A Comprehensive Study of Big Data Machine Learning Approaches and Challenges","authors":"Neelam Singh, D. P. Singh, B. Pant","doi":"10.1109/ICNGCIS.2017.14","DOIUrl":"https://doi.org/10.1109/ICNGCIS.2017.14","url":null,"abstract":"Big data is spreading its span in almost every walk of science and engineering. Both public and private sector enterprises have been collecting and deploying enormous amount of domain-specific information to gain insights about areas like security, marketing, forecasting, fraud-detection, strategic planning etc.. This big data potential is unquestionably noteworthy; but to explore it fully and sensibly it requires new ideas and original learning techniques to address challenges associated with it. With the universe being getting more knowledge-based and computerized, an enormous range of applications shows interest in machine learning (ML) techniques. Machine learning is one of the most sought after field to handle big data challenge. With this paper we endow with a literature analysis related to the up-to-the-minute progress in researches on big data processing deploying Machine Learning as an analytical tool. We will review machine learning techniques with a focus on the promising learning methods like transfer learning, active learning, deep learning, representation learning, distributed, kernel-based learning and parallel learning. Also we will be reviewing the challenges in big data machine learning.","PeriodicalId":314733,"journal":{"name":"2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS)","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115969164","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: 11