2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence最新文献_第10页

5G-A deep insight and its future in India 5G-A的深刻见解及其在印度的未来

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943244

Rashmi Rupam, G. Dubey, S. Singh

引用次数: 2

Sentiment analysis of the demonitization of economy 2016 India, Regionwise 2016年印度地区经济废钞的情绪分析

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943240

Falguni Gupta, S. Singal

引用次数: 10

An efficient hybrid approach using differential evolution and practical swarm optimization 一种基于差分进化和实用群优化的高效混合算法

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943122

Paurush Bhulania, Heena Saxena, S. Tomar

引用次数: 0

A rating approach based on sentiment analysis 基于情感分析的评级方法

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943213

Anshuman, S. Rao, Misha Kakkar

引用次数: 16

3D implementation of heterogeneous topologies on MPSoC 异构拓扑在MPSoC上的3D实现

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943197

Paurush Bhulania, M. Tripathy, Ayoub Khan

引用次数: 1

A review on knowledge extraction for Business operations using data mining 基于数据挖掘的业务操作知识提取综述

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943205

Sanyam Bharara, A. Sabitha, Abhay Bansal

引用次数: 3

Airborne Internet — The Internet in the air 机载互联网——空中的互联网

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943191

Neetu Gupta, Abhishek Aggarwal

引用次数: 5

Effect of temperature and rainfall on paddy yield using data mining 温度和降雨对水稻产量影响的数据挖掘

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943204

K. Kaur, Kanwal Preet Singh Attwal

{"title":"Effect of temperature and rainfall on paddy yield using data mining","authors":"K. Kaur, Kanwal Preet Singh Attwal","doi":"10.1109/CONFLUENCE.2017.7943204","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943204","url":null,"abstract":"Prediction of agriculture yield is a job that requires unification of knowledge from several areas such as data mining, statistics and agriculture. Subject of crop yield prediction has been very popular among various organizations working in agriculture, producers etc. Ρrediction of crop yield helps in managing the storage of crops as well as it directs the transportation decisions, and risk management issues related to crops. Pattern of rainfall and temperature aredynamic due to global warming, and resulting in undergoing impingement on crop productivity. Data Mining focuses upon methodologies for extracting useful knowledge from data and there are several tools to extract the knowledge that is it is a proficiency of examining the dataset such that the end results can be deduced easily and rapidly from the dataset. The knowledge gathered can be used ο forecast the paddy yield. But farmers do not use any knowledge discovery process approach on paddy yield data. Data mining can be used in agriculture for decision making. In this study, we collected data from different government organizations, after preprocessing and discretization of data applied Predictive Apriori algorithm using Data Mining tool (WEKA) for analysis of daily temperature, daily rainfall and paddy yield to predict the paddy yield and to analyze the effect of temperature and rainfall on the paddy yield.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"1 1","pages":"506-511"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89949364","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

Cloud security ecosystem for data security and privacy 云安全生态系统，保障数据安全和隐私

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943164

Akshay Arora, Abhirup Khanna, Anmol Rastogi, A. Agarwal

{"title":"Cloud security ecosystem for data security and privacy","authors":"Akshay Arora, Abhirup Khanna, Anmol Rastogi, A. Agarwal","doi":"10.1109/CONFLUENCE.2017.7943164","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943164","url":null,"abstract":"In the past couple of years Cloud Computing has become an eminent part of the IT industry. As a result of its economic benefits more and more people are heading towards Cloud adoption. In present times there are numerous Cloud Service providers (CSP) allowing customers to host their applications and data onto Cloud. However Cloud Security continues to be the biggest obstacle in Cloud adoption and thereby prevents customers from accessing its services. Various techniques have been implemented by provides in order to mitigate risks pertaining to Cloud security. In this paper, we present a Hybrid Cryptographic System (HCS) that combines the benefits of both symmetric and asymmetric encryption thus resulting in a secure Cloud environment. The paper focuses on creating a secure Cloud ecosystem wherein we make use of multi-factor authentication along with multiple levels of hashing and encryption. The proposed system along with the algorithm are simulated using the CloudSim simulator. To this end, we illustrate the working of our proposed system along with the simulated results.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"5 1","pages":"288-292"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87073451","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: 36

An enterprise resource management model for business intelligence, data mining and predictive analytics 用于商业智能、数据挖掘和预测分析的企业资源管理模型

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943200

A. Jayaram, S. Singal

{"title":"An enterprise resource management model for business intelligence, data mining and predictive analytics","authors":"A. Jayaram, S. Singal","doi":"10.1109/CONFLUENCE.2017.7943200","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943200","url":null,"abstract":"Enterprise Resource Management System (ERMS) is used in the management of enterprise for the computerization of enterprise processes such as management of customer data, employee data, client data, financial data, sales reports, attendance reports, inventory details, equity details and payroll details. An ERMS can handle different user roles such as manager, CEO, employees, customers and has abstraction features for its users. It is the core software used by all enterprises as it provides an interface for the overall management of the enterprise. The proposed ERMS model is easy to use, easily configurable as well as economical in terms of time and cost. Moreover, it can adapt easily to any browser or device through its inbuilt bootstrap framework. Migration of data from the existing enterprise system is also feasible. Business Intelligence can be obtained by performing data mining and predictive analytics with the massive data obtained in the central cloud storage area of the proposed ERMS model. Implementation of the proposed ERMS model can be extremely beneficial for the enterprise as it can gain valuable insights regarding the running of its business, customers and competitors.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"26 1","pages":"485-490"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85909822","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: 3