Search

Your search keyword '"Lamba, Tripti"' showing total 19 results
Start Over Author "Lamba, Tripti"

Refine your results

more »

more »

19 results on '"Lamba, Tripti"'

4. Comparative Analysis of Face Recognition Models for Criminal Detection

Author

Goyal, Gouri, Kumar, Vaibhav, Aggarwal, Piyush, Chugh, Gunjan, Lamba, Tripti, 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, Sharma, Harish, editor, Chakravorty, Antorweep, editor, Hussain, Shahid, editor, and Kumari, Rajani, editor

Published
2024
Full Text
View/download PDF

6. Chatbot Farming System

Author

Lamba, Tripti, Keshri, Ritika, Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Goyal, Dinesh, editor, Kumar, Anil, editor, Piuri, Vincenzo, editor, and Paprzycki, Marcin, editor

Published
2023
Full Text
View/download PDF

7. Global Outlook of Cyber Security

Author

Lamba, Tripti, Kandwal, Shivani, Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Goyal, Dinesh, editor, Kumar, Anil, editor, Piuri, Vincenzo, editor, and Paprzycki, Marcin, editor

Published
2023
Full Text
View/download PDF

11. Security Analysis of Cyber Attacks Using Machine Learning Algorithms in eGovernance Projects

Author

Malhotra, Harmeet, Dave, Meenu, Lamba, Tripti, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Founding Editor, Singh, Pradeep Kumar, editor, Sood, Sanjay, editor, Kumar, Yugal, editor, Paprzycki, Marcin, editor, Pljonkin, Anton, editor, and Hong, Wei-Chiang, editor

Published
2020
Full Text
View/download PDF

12. Comparative Analysis of Different Machine Learning Techniques

Author

Srivastava, Nidhi, Lamba, Tripti, Agarwal, Manisha, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Founding Editor, Singh, Pradeep Kumar, editor, Sood, Sanjay, editor, Kumar, Yugal, editor, Paprzycki, Marcin, editor, Pljonkin, Anton, editor, and Hong, Wei-Chiang, editor

Published
2020
Full Text
View/download PDF

13. Facial emotion recognition music player: Enhancing music experience through computer vision and machine learning.

Author

Garg, Raghav, Lathwal, Nitay, Kumar, Mayank, Chugh, Gunjan, and Lamba, Tripti

Subjects
EMOTION recognition, MUSIC & emotions, MACHINE learning, FACIAL expression & emotions (Psychology), EMOTIONAL state, COMPUTER vision
Abstract

Face Emotion Detection Music Player is an innovative idea that combines computer vision and machine learning techniques to create a unique and interactive music player. This Research Paper studies the uses of Convolutional Neural Networks (CNN) to detect and analyze facial emotions in real-time and dynamically create playlists based on detected emotions. The music player is designed to record facial expressions using a webcam or other suitable camera. To reliably identify facial expressions including happiness, sorrow, rage, surprise, and more, a CNN model is trained on a sizable collection of face images. The trained model demonstrated an astounding 74.46% accuracy in identifying emotions when combined with music player software running on a PC or other suitable device. The CNN model analyses the photos to determine the user's emotional state as they interact with the music player in real-time, as captured by the camera. Based on the detected emotions, the player automatically selects and plays songs from a predefined music collection that matches the user's emotional state. For example, if the user looks happy, the player can play happy and energetic songs, while if the user looks angry, it can play more calming and soothing music. Face Detection Music Player offers a personalized and dynamic music listening experience as the music selection is constantly updated according to the user's emotions. This research paper illustrates how computer vision and machine learning techniques may be used to produce interactive programs that can instantly adjust to the moods and preferences of their users. It has uses in user experience research, entertainment, and mental health, among others. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results