2018 4th International Conference on Computational Intelligence & Communication Technology (CICT)最新文献_第4页

DGS and SRR Based FSS Microstrip Antenna for 5G Communication 基于DGS和SRR的5G通信FSS微带天线

2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) Pub Date : 2018-02-01 DOI: 10.1109/CIACT.2018.8480366

Anubhav Kumar, Shushrut Das, R. L. Yadava

引用次数: 5

Automated Visible Range Imaging Scheme to Identify Toxic Substance from Common Starchy Food 从普通淀粉类食品中识别有毒物质的自动可见光范围成像方案

2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) Pub Date : 2018-02-01 DOI: 10.1109/CIACT.2018.8480363

Anjali Yadav, M. Dutta, Radim Burget

引用次数: 0

The Role of Big Data in the Media and Entertainment Industry 大数据在媒体和娱乐行业中的作用

2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) Pub Date : 2018-02-01 DOI: 10.1109/CIACT.2018.8480281

M. Suri, S. Singh

引用次数: 2

Battery Energy Storage System 电池储能系统

2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) Pub Date : 2018-02-01 DOI: 10.1109/CIACT.2018.8480377

P. Pathak, A. Gupta

引用次数: 10

Auto Predictive Customer Feedback from Textual Analysis of Online Chat Logs 基于在线聊天记录文本分析的自动预测客户反馈

2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) Pub Date : 2018-02-01 DOI: 10.1109/CIACT.2018.8480350

S. Garg, S. Singh

{"title":"Auto Predictive Customer Feedback from Textual Analysis of Online Chat Logs","authors":"S. Garg, S. Singh","doi":"10.1109/CIACT.2018.8480350","DOIUrl":"https://doi.org/10.1109/CIACT.2018.8480350","url":null,"abstract":"Text analytics is the process to convert unstructured data into structured or meaningful data for analysis by structuring the input text for deducing patterns and trends to evaluate and interpret the output data. It measures customer opinions, product reviews and feedback for providing sentiment analysis to support a decision making which is based upon facts. Data mining and text analytics when combined with statistics creates predictive intelligence to uncover patterns and relationships for both unstructured and structured data. This research work presents a concept to collect online chat logs for the customer at all timestamps for a chat conversation to auto predict the customer feedback using textual sentiment analysis. Using term document frequency, a matrix is obtained with the word count as an input for performing sentiment analysis on the available set of words. The obtained results deduce positive and negative sentiments for the provided words in a matrix which derives association rules using “Apriori algorithm” among the positive and negative sentiments for the most common set of pre-defined words in a dictionary. These association rules will infer relationships among these sentiments for auto predicting customer feedback via online chat logs, eliminating manual filling of feedback forms.","PeriodicalId":358555,"journal":{"name":"2018 4th International Conference on Computational Intelligence & Communication Technology (CICT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130843297","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

A Survey of Recommendation Systems in Twitter 推特推荐系统研究

2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) Pub Date : 2018-02-01 DOI: 10.1109/CIACT.2018.8480264

R. Katarya, Yamini Arora

{"title":"A Survey of Recommendation Systems in Twitter","authors":"R. Katarya, Yamini Arora","doi":"10.1109/CIACT.2018.8480264","DOIUrl":"https://doi.org/10.1109/CIACT.2018.8480264","url":null,"abstract":"Social data mining is a major research area in this new era of technology. Various popular social networking websites such as Facebook, YouTube, Twitter provide a platform to the people for exchanging information and maintain a connection with the friends, relatives, and other active users. Twitter presents a platform to the active users for expressing their views and opinions on a trending topic by posting 280-character tweets. This feature of Twitter makes it different from other social networking sites. This popular microblogging site has approximately 328 million users and the number of tweets that are generated every day is approximately 500 million. Hence, the amount of information that users receive daily on their timeline is quite large. Recommender Systems have been introduced to solve this major problem of information overload. These systems help users to find useful and interesting information. Information filtering is a major step to provide important and useful tweets to the active users. They may miss out the important information due to overwhelming tweets on their timeline. The paper presents the different approaches and techniques that recommender systems have implemented to recommend the important tweets as well as followees to the users based on their behavior and other important features.","PeriodicalId":358555,"journal":{"name":"2018 4th International Conference on Computational Intelligence & Communication Technology (CICT)","volume":"19 3-4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120915376","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: 5

Efficient Detection of Islanding for Self-Healing Grid-A Case Study in Indian Grid 自愈电网孤岛的有效检测——以印度电网为例

2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) Pub Date : 2018-02-01 DOI: 10.1109/CIACT.2018.8480295

A. Khair, M. Rihan

引用次数: 4

Prediction of Precipitation Based on Sliding Grey Theory and Weighted Markov Chain 基于滑动灰色理论和加权马尔可夫链的降水预测

2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) Pub Date : 2018-02-01 DOI: 10.1109/CIACT.2018.8480150

Yiqi Gui, Hwangkyu Choi

引用次数: 2

A Low Profile Super Wideband Antenna with Circularly Polarized Band 一种低轮廓圆极化超宽带天线

2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) Pub Date : 2018-02-01 DOI: 10.1109/CIACT.2018.8480223

Devvrat Tyagi, Sachin Kumar, R. Kumar

引用次数: 1

Managing Customers Online Recovery – An Insight for E-Retailers Using Conjoint Analysis 管理客户在线恢复——对电子零售商使用联合分析的洞察

2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) Pub Date : 2018-02-01 DOI: 10.1109/CIACT.2018.8480207

Kumari Anshu, Loveleen Gaur

{"title":"Managing Customers Online Recovery – An Insight for E-Retailers Using Conjoint Analysis","authors":"Kumari Anshu, Loveleen Gaur","doi":"10.1109/CIACT.2018.8480207","DOIUrl":"https://doi.org/10.1109/CIACT.2018.8480207","url":null,"abstract":"It is essential for the retailers to avert any sort of complications that the customers’ might face from the product/service they receive from the retailers. In case any kind of complication arise, they should be fixed instantaneously. The customer tends to become distrustful about online buying and more specifically to the e-retailer in case they are not assured that their problem will be fixed speedily, accurately and without any hassle. Redress is referred as the retort or answer to the customer’s objection or grievances that trade provides to them in case any such problem arises during the transaction. It is very important for online retailers to be ready with strategies and processes to counteract the problems and to re-establish and fortify buyer trust. Here in this paper study of the redress/recovery aspects of online customers has been done through the E-RecS-QUAL model. Recovery attributes followed by e-retailers are analysed and those that are important and preferred by the customers are identified using the conjoint analysis. The aim is also to develop a broader conceptual model of e- recovery ascertaining significant levels for every attribute and also finding the relative prominence of the attribute resulting in an overall grading/preference This would help the e-retailers to focus more on crucial attributes and may also help in curbing the extra cost and resources borne by them and at the same time pleasing the customer to a greater extent.","PeriodicalId":358555,"journal":{"name":"2018 4th International Conference on Computational Intelligence & Communication Technology (CICT)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114955370","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