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1,927 results on '"Sugumaran, Vijayan"'

6. An E-Coupon Service Based on Blockchain

Author

Deepika, S., Vijayakumar, K. P., Sugumaran, Vijayan, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Mandal, Jyotsna Kumar, editor, Jana, Biswapati, editor, Lu, Tzu-Chuen, editor, and De, Debashis, editor

Published
2024
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10. Effective Information Retrieval, Question Answering and Abstractive Summarization on Large-Scale Biomedical Document Corpora

Author

Shenoy, Naveen, Nayak, Pratham, Jain, Sarthak, Sowmya Kamath, S., Sugumaran, Vijayan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Métais, Elisabeth, editor, Meziane, Farid, editor, Sugumaran, Vijayan, editor, Manning, Warren, editor, and Reiff-Marganiec, Stephan, editor

Published
2023
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11. Text to Image Synthesis Using Bridge Generative Adversarial Network and Char CNN Model

Author

Gajendran, Sudhakaran, Arunarani, Ar., Manjula, D., Sugumaran, Vijayan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Métais, Elisabeth, editor, Meziane, Farid, editor, Sugumaran, Vijayan, editor, Manning, Warren, editor, and Reiff-Marganiec, Stephan, editor

Published
2023
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17. Emotion Correlation Mining Through Deep Learning Models on Natural Language Text

Author

Wang, Xinzhi, Kou, Luyao, Sugumaran, Vijayan, Luo, Xiangfeng, and Zhang, Hui

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Emotion analysis has been attracting researchers' attention. Most previous works in the artificial intelligence field focus on recognizing emotion rather than mining the reason why emotions are not or wrongly recognized. Correlation among emotions contributes to the failure of emotion recognition. In this paper, we try to fill the gap between emotion recognition and emotion correlation mining through natural language text from web news. Correlation among emotions, expressed as the confusion and evolution of emotion, is primarily caused by human emotion cognitive bias. To mine emotion correlation from emotion recognition through text, three kinds of features and two deep neural network models are presented. The emotion confusion law is extracted through orthogonal basis. The emotion evolution law is evaluated from three perspectives, one-step shift, limited-step shifts, and shortest path transfer. The method is validated using three datasets-the titles, the bodies, and the comments of news articles, covering both objective and subjective texts in varying lengths (long and short). The experimental results show that, in subjective comments, emotions are easily mistaken as anger. Comments tend to arouse emotion circulations of love-anger and sadness-anger. In objective news, it is easy to recognize text emotion as love and cause fear-joy circulation. That means, journalists may try to attract attention using fear and joy words but arouse the emotion love instead; After news release, netizens generate emotional comments to express their intense emotions, i.e., anger, sadness, and love. These findings could provide insights for applications regarding affective interaction such as network public sentiment, social media communication, and human-computer interaction.

Published
2020

18. Exploring the Suitability of Semantic Spaces as Word Association Models for the Extraction of Semantic Relationships

Author

Kapetanios, Epaminondas, Sugumaran, Vijayan, and Angelopoulou, Anastassia

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval, I.7
Abstract

Given the recent advances and progress in Natural Language Processing (NLP), extraction of semantic relationships has been at the top of the research agenda in the last few years. This work has been mainly motivated by the fact that building knowledge graphs (KG) and bases (KB), as a key ingredient of intelligent applications, is a never-ending challenge, since new knowledge needs to be harvested while old knowledge needs to be revised. Currently, approaches towards relation extraction from text are dominated by neural models practicing some sort of distant (weak) supervision in machine learning from large corpora, with or without consulting external knowledge sources. In this paper, we empirically study and explore the potential of a novel idea of using classical semantic spaces and models, e.g., Word Embedding, generated for extracting word association, in conjunction with relation extraction approaches. The goal is to use these word association models to reinforce current relation extraction approaches. We believe that this is a first attempt of this kind and the results of the study should shed some light on the extent to which these word association models can be used as well as the most promising types of relationships to be considered for extraction., Comment: 10 pages, 11 tables

Published
2020

20. Vehicle Over Speed Detection System

Author

Ganesan, K., Manikandan, N. S., Sugumaran, Vijayan, Kacprzyk, Janusz, Series Editor, Roy, Sanjiban Sekhar, editor, Hsu, Ching-Hsien, editor, and Kagita, Venkateshwara, editor

Published
2023
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25. Early Prognosis of Preeclampsia Using Machine Learning

Author

Sivaram, E., Vadivu, G., Sangeetha, K., Sugumaran, Vijayan, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Fernando, Xavier, editor, Lu, Joan, editor, Piramuthu, Selwyn, editor, and Chandrabose, Aravindan, editor

Published
2022
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26. Multi-agent System for Weather Forecasting in India

Author

Sreedevi, A. G., Palaniappan, S., Shankar, P., Sugumaran, Vijayan, van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Abramowicz, Witold, editor, Auer, Sören, editor, and Stróżyna, Milena, editor

Published
2022
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33. Predicting Vaccine Hesitancy and Vaccine Sentiment Using Topic Modeling and Evolutionary Optimization

Author

Krishnan, Gokul S., Sowmya Kamath, S., Sugumaran, Vijayan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Métais, Elisabeth, editor, Meziane, Farid, editor, Horacek, Helmut, editor, and Kapetanios, Epaminondas, editor

Published
2021
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37. Multimodal Medical Image Fusion with Multi Resolution Discrete Cosine Transform

Author

Ch., Hima Bindu, Sugumaran, Vijayan, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Sugumaran, Vijayan, editor, Xu, Zheng, editor, P., Shankar, editor, and Zhou, Huiyu, editor

Published
2019
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42. Controlling Risk from Design Changes in Chinese Prefabricated Construction Projects: An Empirical Investigation

Author

Du, Juan, Zhang, Jiajun, Gu, Yifei, Sugumaran, Vijayan, van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Rosemann, Michael, Series Editor, Shaw, Michael J., Series Editor, Szyperski, Clemens, Series Editor, Lang, Karl R., editor, Xu, Jennifer, editor, Zhu, Bin, editor, Liu, Xiao, editor, Zhang, Han, editor, and Fan, Ming, editor

Published
2020
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43. A Big Data Conceptual Model to Improve Quality of Business Analytics

Author

Park, Grace, Chung, Lawrence, Johng, Haan, Sugumaran, Vijayan, Park, Sooyong, Zhao, Liping, Supakkul, Sam, van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Rosemann, Michael, Series Editor, Shaw, Michael J., Series Editor, Szyperski, Clemens, Series Editor, Dalpiaz, Fabiano, editor, Zdravkovic, Jelena, editor, and Loucopoulos, Pericles, editor

Published
2020
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44. To Do or Not to Do: How Socio-demographic Characteristics of Older Adults Are Associated with Online Activities

Author

Leukel, Joerg, Schehl, Barbara, Sugumaran, Vijayan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gao, Qin, editor, and Zhou, Jia, editor

Published
2020
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46. Dynamic Evaluation of Road Network Resilience to Traffic Accidents: An Emergency Management Perspective for Sustainable Cities in China.

Author

Yu, Gang, Xie, Jiayi, and Sugumaran, Vijayan

Abstract

When assessing road network resilience, emergency management behavior should be considered, as this represents the road network's capacity to adapt to and recover from traffic accidents. Given the timeliness and variability of emergency management behavior, deterministic approaches seem inadequate to represent real road network performance. Thus, this paper innovatively designs an emergency management perspective-based dynamic evaluation method of road network resilience to traffic accidents. Firstly, based on four stages of emergency management, a road network resilience evaluation index system encompassing resilience capabilities, resilience attributes and traffic accident emergency management ability indicators is constructed. Afterwards, the gray relational technique for order preference by similarity to the ideal solution (GRA-TOPSIS) evaluation method based on combination weighting, which integrates factor analysis with hesitant intuitionistic fuzzy expert scoring, is designed to quantify resilience. Finally, the obstacle degree model is utilized for identifying resilience constraints as the input of a long short-term memory (LSTM) model to predict the resilience variation trend. The fast road network of Shanghai in China is adopted as a case study, and the results indicate that road network resilience embodies significant spatial distribution characteristics. Road length, number of tractors, perception and response and disposal time of traffic accidents cast notable effects on resilience. Additionally, some roads are forecast to show descending resilience. The proposed method is valuable for helping policymakers identify current and potential vulnerable roads and to formulate proposals to effectively improve the resilience of urban agglomerations and promote sustainable cities. [ABSTRACT FROM AUTHOR]

Published
2024
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