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2. Study of Various Word Vectors for Sentiment Analysis

Author

Madhusudhanan, S., Jyothi, N. M., Vishnukumar, A., Sathya, S., Arun Vignesh, N., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Kumar Singh, Koushlendra, editor, Bajpai, Manish Kumar, editor, and Sheikh Akbari, Akbar, editor

Published
2023
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3. Cough Audio Signal-Based Clinical Emergency Classification of Corona Variant Infected Patients Using Multiclass SVM

Author

Jyothi, N. M., Madhusudhanan, S., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Kulkarni, Anand J., editor, Mirjalili, Seyedali, editor, and Udgata, Siba Kumar, editor

Published
2023
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4. An Ensemble Sentiment Classification on Multidomain Dataset

Author

Madhusudhanan, S., Jyothi, N. M., Vishnukumar, A., Sathya, S., Arun Vignesh, N., 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, Pati, Bibudhendu, editor, Panigrahi, Chhabi Rani, editor, Mohapatra, Prasant, editor, and Li, Kuan-Ching, editor

Published
2023
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7. Mental health model to assess psychiatric fitness in women by speech analysis using machine learning.

Author

Jyothi, N. M., Tabassum, Husna, Thota, Rajani, Gupta, Saurabh, Pavani, A., and Madhusudhanan, S.

Subjects
*MACHINE learning, *SPEECH, *AUTOMATIC speech recognition, *MENTAL illness treatment, *MENTAL health, *SOCIAL media, *MENTAL illness, *JOB stress, *INTRUSION detection systems (Computer security)
Abstract

Women psychiatric health is one of the most neglected factors. Women mental health is very important as mentally healthy woman is the backbone of entire family and society. Even though many papers are published related to mental illness detection and treatment using various methods through survey, questionnaires, public health databases, clinical records, social media usage etc., by applying different Machine Learning (ML) algorithms for prediction, still there is huge room left for identifying and analyzing the mental illness through different means and applying ML for prediction, detection and assessment. The main aim of this research work is to demonstrate that speech is the easiest way to recognize symptoms of mental illness like depression, stress, anxiety, trauma etc. The speech signal carries hidden attributes like intensity, pauses, speech rate which reveal lot of information about the psychological fitness of a woman. The model is deployed on all the kernels of SVM to study and analyze the prediction accuracy of classification using three classification labels. The result obtained is more realistic in assessing the psychiatric fitness with overall accuracy of 90.78 percent. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Assessment and prediction of communication competency of the students for placement readiness using speech signal processing and kernels of support vector classifiers.

Author

Jyothi, N. M. and Madhusudhanan, S.

Subjects
*ABILITY grouping (Education), *SIGNAL processing, *SPEECH, *PREDICTION models, *PREPAREDNESS, *KERNEL operating systems, *AUTOMATIC speech recognition
Abstract

This research work is carried out to assess and predict the classification on communication competency among students using different kernels of support vector classifiers (SVC). The communication competency assessment and prediction classification model eliminates the human intervention in judging the communication competency among student for the purpose of placement. The model uses pre-recorded speech signal and the required features are drawn and analyzed in speech signal processing software. The prediction model classifies the competency as Good, Average and Poor which helps in faster assessment of student's communication competency efficiently, saving lot of time involved in scheduling and conducting presentation activity for trainers The prediction model is constructed using support vector classifier kernels. All the kernels are applied and the result is judged and analyzed for its suitability to get more accuracy in competency prediction. The model predicted with average accuracy rate of 94%. [ABSTRACT FROM AUTHOR]

Published
2023
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12. A Contemporary Access of Improved BEE Colony Optimization for Scheduling Problems in Cloud Environment

Author

V. Murali Mohan, Jyothi N M, M.N.V Kiranbabu, and R.M Balajee

Subjects
Computer science, business.industry, Distributed computing, Interoperability, Evolutionary algorithm, Resource distribution, Cloud computing, Resource management, business, Swarm intelligence, Scheduling (computing), Task (project management)
Abstract

Today's competitive environment improves interoperability across many devices, illuminating resource distribution for user demands and preferences. In cloud computing, task scheduling triggers the goal of increasing performance by leveraging resource distribution efficiently. Many approaches that lead to task optimization for resource utilization were carried out with an emphasis on static and dynamic models. Today's computing systems are dynamic, and meta-heuristic algorithms can be useful in solving problems. Evolutionary algorithms with swarm intelligence help provide ideal solutions to today's difficult problems. This research work has implemented an improvement of Bee colony optimization for scheduling the tasks in cloud environment.

Published
2021

14. Text-to-Speech translation using Support Vector Machine, an approach to find a potential path for human-computer speech synthesizer

Author

M. Hanumanthappa, Jyothi N M, and Rashmi S

Subjects
Propagation of uncertainty, Computer science, business.industry, Speech recognition, Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing), Speech synthesis, Pattern recognition, Context (language use), computer.software_genre, 01 natural sciences, 010305 fluids & plasmas, Support vector machine, 030507 speech-language pathology & audiology, 03 medical and health sciences, Statistical classification, ComputingMethodologies_PATTERNRECOGNITION, Computer Science::Sound, 0103 physical sciences, Feature (machine learning), Artificial intelligence, Pruning (decision trees), 0305 other medical science, business, Representation (mathematics), computer
Abstract

Text-to-Speech (TTS), an astounding feature to assemble computer with intelligence and to induce sound is seemingly a challenging task as it is related to the propagation of uncertainty with the input. This is because TTS evolutes the input based on the probabilities and not with certainty ratios. TTS is accomplished by generating the sound structure/phoneme and then classifying these phonemes in the phonetic dictionary. The Wards' algorithms, BIRCH, Support Vector Machine (SVM) are used to figure out the appropriate sound representation for the given context. To distinguish correct elocution, the SVM procedures are equipped with the principles of pruning. The output was analyzed using divergent stages of uncertainty. In order to study the effect of the output 10 listeners were considered for determining Signal-to-Noise (SNR) ratio. SNR shows that the errors of both type phase and uncertainty were approximately 6% resulting 94% of accuracy. These results manifested that SVM stratagem can be used to obtain better results for TTS synthesizer.

Published
2016

15. Multi Mobile Agents for Multimedia File Download in E-Commerce Environment

Author

V. Ramaswamy, Jyothi N M, and Nirmala C R

Subjects
File Transfer Protocol, Multimedia, Computer science, Download, BitTorrent tracker, business.industry, Computer file, Remote evaluation, computer.software_genre, Unix file types, World Wide Web, Client–server model, Upload, Self-certifying File System, Data file, Mobile agent, The Internet, business, SSH File Transfer Protocol, computer
Abstract

So far, the global standard for file uploading and downloading over internet has been FTP. A technology which is emerging fast on web today is mobile agent technology which can be applied for file download from remote server to remote client or server. We investigate the use of mobile agents for downloading of files from remote hosts. We believe this technology has the potential to foster new ways of downloading of files when compared to traditional FTP. This finds its application in achieving faster ecommerce, while purchasing the items online. We send mobile agent embedded within it the various types of file name/s to be downloaded from the remote server by specifying the itinerary details. The agent on reaching the server gets the file/s from the server and returns to the client along with the file. An e-commerce system of purchasing multimedia files (Audio, Video, and Image) online is implemented here. Simulation of this work has been carried out on IBM‟S Aglets framework and the performance has been tested by computing the round trip time. We have also devised an algorithm for purchasing the products which are in restricted realm. This technique appears to be faster when compared to traditional client server FTP based file download system.

Published
2010

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