Education•Thiruvananthapuram, Kerala, India•
About: Indian Institute of Information Technology and Management, Kerala is a education organization based out in Thiruvananthapuram, Kerala, India. It is known for research contribution in the topics: Deep learning & Support vector machine. The organization has 183 authors who have published 257 publications receiving 1629 citations. The organization is also known as: IIITM-K.
Topics: Deep learning, Support vector machine, Sentiment analysis, Cluster analysis, Filter (signal processing)
Papers published on a yearly basis
16 Dec 2020
TL;DR: The Dravidian-CodeMix-FIRE 2020 Track as discussed by the authors focused on sentiment analysis of code-mixed text in code mixed text for Tamil and Malayalam, and participants were given a dataset of YouTube comments and the goal of the shared task submissions was to recognise the sentiment of each comment by classifying them into positive, negative, neutral, mixed-feeling classes or by recognizing whether the comment is not in the intended language.
Abstract: Sentiment analysis of Dravidian languages has received attention in recent years However, most social media text is code-mixed and there is no research available on sentiment analysis of code-mixed Dravidian languages The Dravidian-CodeMix-FIRE 2020, a track on Sentiment Analysis for Dravidian Languages in Code-Mixed Text, focused on creating a platform for researchers to come together and investigate the problem There were two languages for this track: (i) Tamil, and (ii) Malayalam The participants were given a dataset of YouTube comments and the goal of the shared task submissions was to recognise the sentiment of each comment by classifying them into positive, negative, neutral, mixed-feeling classes or by recognising whether the comment is not in the intended language The performance of the systems was evaluated by weighted-F1 score
TL;DR: A graphical model representing the vulnerability relations in the IIoT network is proposed that acts as a security framework for the risk assessment of the network and a set of risk mitigation strategies to improve the overall security of thenetwork are proposed.
Abstract: Industrial IoT (IIoT) refers to the application of IoT in industrial management to improve the overall operational efficiency. With IIoT that accelerates the industrial automation process by enrolling thousands of IoT devices, strong security foundations are to be deployed befitting the distributed connectivity and constrained functionalities of the IoT devices. Recent years witnessed severe attacks exploiting the vulnerabilities in the devices of IIoT networks. Moreover, attackers can use the relations among the vulnerabilities to penetrate deep into the network. This paper addresses the security issues in IIoT network because of the vulnerabilities existing in its devices. As graphs are efficient in representing relations among entities, we propose a graphical model representing the vulnerability relations in the IIoT network. This helps to formulate the security issues in the network as graph-theoretic problems. The proposed model acts as a security framework for the risk assessment of the network. Furthermore, we propose a set of risk mitigation strategies to improve the overall security of the network. The strategies include detection and removal of the attack paths with high risk and low hop-length. We also discuss a method to identify the strongly connected vulnerabilities referred as hot-spots. A use-case is discussed and various security parameters are evaluated. The simulation results with graphs of different sizes and structures are presented for the performance evaluation of the proposed techniques against the changing dynamics of the IIoT networks.
06 Mar 2020
TL;DR: A set of quality metrics to evaluate the dataset and categorize them accordingly is proposed and will assist users in various natural language processing tasks such as part-of-speech tagging, named entity recognition, sentiment analysis, conversational systems, and machine translation, etc.
Abstract: Code switching is a prevalent phenomenon in the multilingual community and social media interaction. In the past ten years, we have witnessed an explosion of code switched data in the social media that brings together languages from low resourced languages to high resourced languages in the same text, sometimes written in a non-native script. This increases the demand for processing code-switched data to assist users in various natural language processing tasks such as part-of-speech tagging, named entity recognition, sentiment analysis, conversational systems, and machine translation, etc. The available corpora for code switching research played a major role in advancing this area of research. In this paper, we propose a set of quality metrics to evaluate the dataset and categorize them accordingly.
03 Aug 2012
TL;DR: The International Conference on Advances in Computing, Communications and Informatics (ICACCI'12) was held in Chennai, India during August 3-5, 2012 and hosted at RMK Engineering College, Chennai.
Abstract: Globalization tends to be most perceptible and observable in almost every facet of life mainly due to the emergence of new digital technologies of computing and communications. At the same time, informatics with its strong focus on providing fast and ready access for human based on these developments in computing and communications plays more very crucial role in people's lives and permeates all it in all respects, from entertainment to healthcare and from databases to e-governance. The International Conference on Advances in Computing, Communications and Informatics (ICACCI'12) was held in Chennai, India during August 3-5, 2012 and hosted at RMK Engineering College, Chennai. ICACCI provides an international forum for exchange of ideas among interested researchers, students, developers, and practitioners in the areas of computing, communications, and informatics. ICACCI'12 was organized in association with National Association of Software and Services Companies (NASSCOM), Offenburg University of Applied Sciences, Germany; Computer Society of India (CSI), The International Society for Computers and Their Applications, Inc. (ISCA), USA; International Neural Network Society (INNS), India Chapter; Indian Association for Medical Informatics (IAMI), Software process improvement network (Spin); and Research Publishing Services (RPS), Singapore.
11 May 2020
TL;DR: In this article, a model of language models for minority and historical languages was developed using a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 P2 (Insight 2), co-funded by the European Regional Development Fund as well as the EU H2020 programme under grant agreements 731015 (ELEXIS-European Lexical Infrastructure), 825182 (Pret- ˆ a-LLOD), and IRCLA/2017/129 (CARDAMOM-Comparative Deep Models of Language
Abstract: This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (Insight), SFI/12/RC/2289 P2 (Insight 2), co-funded by the European Regional Development Fund as well as by the EU H2020 programme under grant agreements 731015 (ELEXIS-European Lexical Infrastructure), 825182 (Pret- ˆ a-LLOD), and Irish Research Council ` grant IRCLA/2017/129 (CARDAMOM-Comparative Deep Models of Language for Minority and Historical Languages).
Showing all 184 results
|Alex Pappachen James||20||72||1152|
|Sabu M. Thampi||17||124||2115|
|Alex Pappachen James||16||154||1865|
|Joseph Suresh Paul||15||98||1327|
|J. P. Jayan||6||21||129|
|C. Prem Sankar||5||7||119|
|V. S. Anoop||4||15||50|
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