College of Engineering, Trivandrum

About: College of Engineering, Trivandrum is a based out in . It is known for research contribution in the topics: Inverter & Control theory. The organization has 1884 authors who have published 1601 publications receiving 11758 citations.

Sentiment analysis in twitter using machine learning techniques

Abstract: Sentiment analysis deals with identifying and classifying opinions or sentiments expressed in source text. Social media is generating a vast amount of sentiment rich data in the form of tweets, status updates, blog posts etc. Sentiment analysis of this user generated data is very useful in knowing the opinion of the crowd. Twitter sentiment analysis is difficult compared to general sentiment analysis due to the presence of slang words and misspellings. The maximum limit of characters that are allowed in Twitter is 140. Knowledge base approach and Machine learning approach are the two strategies used for analyzing sentiments from the text. In this paper, we try to analyze the twitter posts about electronic products like mobiles, laptops etc using Machine Learning approach. By doing sentiment analysis in a specific domain, it is possible to identify the effect of domain information in sentiment classification. We present a new feature vector for classifying the tweets as positive, negative and extract peoples' opinion about products.

A Simple Space Vector PWM Generation Scheme for Any General $n$ -Level Inverter

Abstract: This paper proposes a generalized method for the generation of space vector pulsewidth modulation (SVPWM) signals for multilevel inverters. In the proposed method, the actual sector containing the tip of the reference space vector need not be identified. A method is presented to identify the center of a sub-hexagon containing the reference space vector. Using the center of the sub-hexagon, the reference space vector is mapped to the innermost sub-hexagon, and the switching sequence corresponding to a two-level inverter is determined. A new technique is proposed in this paper, by which these two-level vectors are translated to the switching vectors of the multilevel inverter by adding the center of the sub-hexagon to the two-level vectors. The proposed method can be extended to any n-level inverter, and a generalized algorithm is proposed. The scheme is explained for a five-level inverter, and experimental results are presented for a three-level inverter.

Environmental Sound Classification Using Deep Convolutional Neural Networks and Data Augmentation

Abstract: This work is about environmental sound classification by deep convolutional neural networks and data augmentation. Data augmentation is applied to increase the labeled training dataset. Data augmentation process improves the performance of audio classification. In this paper, first we present a strategy for generating a deep convolutional neural network (CNN) framework for environmental sound analysis with Urban-sound8K audio dataset. Secondly we analyze the performance of data augmentation methods on Urbansound8K audio dataset and compare the performance of CNN with different data augmentation methodologies. Data augmentation is basically a deformation technique. By this approach we can increase the number of dataset elements into its multiples. Here, compare the performance of different augmentation method to identify which one is the best augmentation technique for environmental sound analysis. Different types of data augmentations were applied to the dataset in the previous works. We introduce a new data augmentation method using LPCC feature.

Systematic and Systemic Approaches to Reducing Attrition Rates in Online Higher Education

Abstract: Prior to 1997, the Department of Instructional & Performance Technology (IPT) online program at Boise State University faced a high student dropout rate. The IPT turned to Keller's ARCS model, Kaufman's Organizational Elements Model, and Kirkpatrick's evaluation model throughout the processes of improving the motivational appeal of the online course for the first‐time adult learners and solving the attrition problem. In this article, the author describes a long‐term evaluation case study and explains how she systematically designed and implemented various instructional interventions to reduce attrition. She also presents the results of systemic evaluations.