Search

Your search keyword '"Briskilal, J."' showing total 17 results
Start Over Author "Briskilal, J."
17 results on '"Briskilal, J."'

2. Abstractive Text Summarization for Tamil Language Using m-T5

Author

Saraswathi, C., Prinitha, V., Briskilal, J., 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, Suma, V., editor, Lorenz, Pascal, editor, and Baig, Zubair, editor

Published
2023
Full Text
View/download PDF

3. Image Classification of Indian Rural Development Projects Using Transfer Learning and CNN

Author

Mangla, Aditya, Briskilal, J., Senthil Kumar, D., 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, Jacob, I. Jeena, editor, Kolandapalayam Shanmugam, Selvanayaki, editor, and Bestak, Robert, editor

Published
2022
Full Text
View/download PDF

4. Classification of Idiomatic Sentences Using AWD-LSTM

Author

Briskilal, J., Subalalitha, C. 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, Jeena Jacob, I., editor, Gonzalez-Longatt, Francisco M., editor, Kolandapalayam Shanmugam, Selvanayaki, editor, and Izonin, Ivan, editor

Published
2022
Full Text
View/download PDF

6. Classification of Idioms and Literals Using Support Vector Machine and Naïve Bayes Classifier

Author

Briskilal, J., Subalalitha, C. 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, 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, Bajpai, Manish Kumar, editor, Kumar Singh, Koushlendra, editor, and Giakos, George, editor

Published
2021
Full Text
View/download PDF

8. Detection of offensive text in memes using deep learning techniques.

Author

Briskilal, J., Karthik, M. Jaya, and Praneeth, Sai

Subjects
*MEMES, *DEEP learning, *LINGUISTIC complexity, *CONVOLUTIONAL neural networks
Abstract

Memes have grown in significance as social media has grown in popularity. Memes are challenging to categorizeusing conventional techniques since they are typically constructed using a combination of images and text, and their contentis frequently hilarious or sarcastic. We suggest a deep learning-based strategy in this paper for classifying memes into a variety of categories, such as sexism, politics, and criticism. This highlights the necessity for a system that can evaluate memes automatically before they raise controversy or spread humor. This study offers a three-step method for locating offensive memes. Before judging the text as offensive or not, it will first extract the text from the supplied image. If the language is found to be unacceptable, the third phase will further categorize the information into three categories: mildly offensive, very offensive, and hateful offensive. The five thousand memes that made up the dataset for this study were divided into four categories: hateful offensive, highly offensive, slightly offensive, and not offensive at all. Concern over the dissemination of offensive memes on social media has grown in recent years. Memes that are offensive can hurt and distress people individually as well as in communities since they frequently contain graphic or disparaging content. Due ofthe complexity of language and the range of cultural allusions used in memes, it can be difficult to identify and remove offensive memes. In this study, we provide a CNNs, Roberta, and BERT-based deep learning approach for categorizing offensive memes. We enhanced pre-trained algorithms using a dataset of offensive and non-offensive memes. We evaluatedthe performance of each model using various evaluation metrics, such as accuracy, precision, recall, and F1-score. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

15. AI Music Generator.

Author

Gupta, Saransh, Marwah, Sparsh, and Briskilal, J.

Subjects
ARTIFICIAL intelligence, COMPUTER music, SPEECH perception, DATABASES, AUTOMATIC speech recognition, DEEP learning, MUSIC stores
Abstract

Artificial intelligence is the study and creation of machines that can perform tasks that would ordinarily require human intelligence such as face detection, speech recognition, and decision-making. We will be using deep learning as a source to generate music through a computer using a database of music tunes without any expertise of any trained music artist. The music is generated through sentimental analysis, which helps in mood detection through a sample image of a human self. The music is stored as a piano-roll which is divided in several types of emotions that is generated through the testing process. The use of deep learning is that the machine trains itself through iteration of inputs that is provided in the database which will help in generating music from the input of a face of the user and will make music generation easier. The various models of deep learning are LSTM, CNN, GAN and many more that help in music generation and simultaneously help in enhancing the exposure to sentimental analysis. This project will be using some of these models to demonstrate music generation through sentimental analysis. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results