Search

Your search keyword '"Kumar, Shailender"' showing total 213 results
Start Over Author "Kumar, Shailender"
213 results on '"Kumar, Shailender"'

9. A Data-Driven Diabetes Predictive Model Using a Novel Optimized Weighted Ensemble Approach

Author

Arora, Sunny, Kumar, Shailender, Kumar, Pardeep, 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, Namasudra, Suyel, editor, Trivedi, Munesh Chandra, editor, Crespo, Ruben Gonzalez, editor, and Lorenz, Pascal, editor

Published
2024
Full Text
View/download PDF

17. A Survey on Representation Learning in Visual Question Answering

Author

Sahani, Manish, Singh, Priyadarshan, Jangpangi, Sachin, Kumar, Shailender, 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, Misra, Rajiv, editor, Shyamasundar, Rudrapatna K., editor, Chaturvedi, Amrita, editor, and Omer, Rana, editor

Published
2022
Full Text
View/download PDF

18. A Deep Learning Approach for Anomaly-Based Network Intrusion Detection Systems: A Survey and an Objective Comparison

Author

Kumar, Shailender, Jha, Namrata, Sachdeva, Nikhil, 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, Misra, Rajiv, editor, Shyamasundar, Rudrapatna K., editor, Chaturvedi, Amrita, editor, and Omer, Rana, editor

Published
2022
Full Text
View/download PDF

23. Fusion of Semantic, Visual and Network Information for Detection of Misinformation on Social Media.

Author

Ahuja, Nishtha and Kumar, Shailender

Abstract

Fake news is the most prevailing buzzword in today's world. Fake news detection is a very time-consuming task that requires fact-checking either manually or automatically using machine learning techniques. The existing techniques focus only on the textual and image content. In this article, we have proposed a multimodal model—FakeMine for the mining of fake news content on social media. Our model explores the network structure of social media posts using Graph Neural Networks and combines them with semantic information from text and images in order to attain better accuracy. Our proposed work uses BERT for textual representations while preserving the semantic relationships in news articles. The image features are represented using VGG-19. The propagation structure of the circulating fake news is captured using Graph Neural Networks. All the features computed are fused together for a better classification. For classification, we have used LSTM which is optimized using Chimp Optimization. The FakeMine model was able to achieve an accuracy of 97.65 which exceeded all other baseline models for multiple modalities. It performed better than other models when tested on individual modalities as well. The proposed optimized LSTM classifier was also able to perform better than all other baseline classifiers. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

39. Feasibility Evaluation of VANET using Directional-Location Aided Routing (D-LAR) Protocol

Author

Raw, Ram Shringar, Das, Sanjoy, Singh, Nanhay, Kumar, Sanjeet, and Kumar, Shailender

Subjects
Computer Science - Networking and Internet Architecture
Abstract

Vehicular Ad hoc Networks (VANETs) allow vehicles to form a self-organized network without any fixed infrastructure. VANETs have received wide attention and numerous research issues have been identified in the recent time. The design and implementation of efficient and scalable routing protocols for VANETs is a challenging task due to high dynamics and mobility constraints. In this paper, we have proposed D-LAR (Directional-Location Aided Routing), is an extension of Location Aided Routing (LAR) with Directional Routing (DIR) capability. D-LAR is a greedy approach based-position based routing protocol to forward packet to the node present in request zone within the transmission range of the source node as most suitable next-hop node. We have justified the feasibility of our proposed protocol for VANET.

Published
2012

Catalog

Books, media, physical & digital resources
See catalog results