Research Papers

Paper Title: Intelligent System For Customer Sentiment Analysis Authors: Shailendra Singh Kathait, Shubhrita Tiwari, Anvesha Bagaria, Vinod Kumar Singh Summary: Sentiment analysis is widely acknowledged in the web and social media monitoring. It allows businesses to gain a comprehensive public opinion on the organization and its services. The ability to deduce insights from the text and emoticons from social media is a practice that is now widely adopted by the organizations worldwide. Digital media represents an extensive opportunity for businesses of any industry to acquire the needs, opinions and intent that users share on social media and web. Listening to consumer’s voice requires in-depth understanding of what customer’s express in Natural Language. This research paper describes the designing of an Intelligent System to understand the human language and crack the sentiment behind it. Download Research Paper

Paper Title: Unsupervised Automated Clustering of Chinese Articles Authors: Shailendra Singh Kathait, Shubhrita Tiwari Summary: A lot of insights can be drawn from the articles that are published online. Instead of manually reading all the articles and assigning relevant tags to them satisfying the content, it will be highly efficient if there exists an automated process for performing the task. In this paper, an unsupervised approach for the automated tagging of articles in the Chinese language has been implemented. The input is an article and output is the tags to that article. The major challenge is the segmentation of the Chinese characters, which do not make use of separators, unlike the English characters. To overcome this, different approaches are combined together in order to get accurate results. Efficient tagging of articles is required, which can be used for many applications in the analysis, one of which is in Recommendation Engine. The tagging process should consider all the aspects of the article and assign the most relevant tags accordingly. The proposed algorithm was implemented for a Chinese Publication House and relevant tags were assigned to its articles of different categories. At the end of the project, the results were manually checked for, in a corpus of 10000 Chinese articles, which reflected the attainment of the overall accuracy of around 85%, greater than that obtained through different traditional methods. Download Research Paper

Paper Title: Machine Learning based Hybrid Method for Surface Defect Detection and Categorization in PU Foam  Authors: Shailendra Singh Kathait, Sakshi Mathur Summary: Foam making is an important industry with foam mattress being one of the main end products. To ensure quality production, their manufacturing is subject to very strict safety checks. There are many types of defects that can arise during their manufacturing process such as holes, cuts, mis-configuration in the material etc. Manual defect detection leads to inaccuracy and increases the chances of defects going unnoticed. This further reduces the process efficiency and creates adverse affect on overall production. To counter this situation, this research paper proposes a hybrid approach that identifies defects present in and on the surface of PU (Polyurethane) foam material. Both supervised and unsupervised approaches are used to classify the PU foam into two categories: normal and defective, considering the type of defect. Then the reliable model is selected according to the precision rate of both the models. Download Research Paper