Search

Your search keyword '"Rohilla, Vinita"' showing total 11 results
Start Over Author "Rohilla, Vinita"
11 results on '"Rohilla, Vinita"'

1. Intelligent Smart Waste Management Using Regression Analysis: An Empirical Study

Author

Rath, Abinash, Das Gupta, Ayan, Rohilla, Vinita, Balyan, Archana, Mann, Suman, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Balas, Valentina E., editor, Sinha, G. R., editor, Agarwal, Basant, editor, Sharma, Tarun Kumar, editor, Dadheech, Pankaj, editor, and Mahrishi, Mehul, editor

Published
2022
Full Text
View/download PDF

3. Empowering Agile Method Feature-Driven Development by Extending It in RUP Shell

Author

Dwivedi, Rinky, Rohilla, Vinita, 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, Bhatia, Sanjiv K., editor, Mishra, Krishn K., editor, Tiwari, Shailesh, editor, and Singh, Vivek Kumar, editor

Published
2017
Full Text
View/download PDF

7. An Empirical Framework for Recommendation-based Location Services Using Deep Learning.

Author

Rohilla, Vinita, Kaur, Mandeep, and Chakraborty, Sudeshna

Subjects
DEEP learning, RECOMMENDER systems, BENCH press, DECISION making
Abstract

The large amount of possible online services throws a significant load on the users' service selection decision-making procedure. A number of intelligent suggestion systems have been created in order to lower the excessive decision-making expense. Taking this into consideration, a? RLSD (Recommendation-based Location Services using Deep Learning) model is proposed in this paper. Alongside robustness, this research considers the geographic interface between the client and the service. The suggested model blends a Multi-Layer-Perceptron (MLP) with a similarity Adaptive Corrector (AC), which is meant to detect high-dimensional and non-linear connections, as well as the location correlations amongst client and services. This not only improves recommendation results but also considerably reduces difficulties due to data sparseness. As a result, the proposed RLSD has strong flexibility and is extensible when it comes to leveraging context data like location. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results