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Your search keyword '"Singh, Rimjhim Padam"' showing total 22 results
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22 results on '"Singh, Rimjhim Padam"'

8. Using Attractive–Repulsive Binary Local Gradient Contours for Sample-Consensus Background Modeling

Author

Singh, Rimjhim Padam, Sharma, Poonam, 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, Tiwari, Aruna, editor, Ahuja, Kapil, editor, Yadav, Anupam, editor, Bansal, Jagdish Chand, editor, Deep, Kusum, editor, and Nagar, Atulya K., editor

Published
2021
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9. Motion Detection Using a Hybrid Texture-Based Approach

Author

Singh, Rimjhim Padam, Sharma, Poonam, Madarkar, Jitendra, 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, Das, Kedar Nath, editor, Bansal, Jagdish Chand, editor, Deep, Kusum, editor, Nagar, Atulya K., editor, Pathipooranam, Ponnambalam, editor, and Naidu, Rani Chinnappa, editor

Published
2020
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11. Improving Change Detection Using Centre-Symmetric Local Binary Patterns

Author

Singh, Rimjhim Padam, Sharma, Poonam, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Deka, Bhabesh, editor, Maji, Pradipta, editor, Mitra, Sushmita, editor, Bhattacharyya, Dhruba Kumar, editor, Bora, Prabin Kumar, editor, and Pal, Sankar Kumar, editor

Published
2019
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16. A weighted hybrid recommendation approach for user's contentment using natural language processing.

Author

Paul, Pretty and Singh, Rimjhim Padam

Subjects
*MACHINE learning, *CONTENTMENT, *RECOMMENDER systems, *CONSUMER preferences, *WEBSITES, *USER-generated content, *NATURAL language processing
Abstract

The purpose of this study is to examine a weighted hybrid recommendation approach by utilizing the user reviews and the rating data. We provide an experimental evaluation using the Amazon Reviews dataset. The most well-known application of recommendation systems is on e-commerce Web sites, where they leverage information about a customer's preferences to build a list of suggested items. Sentiment analysis can aid in gaining a better understanding of user's emotions, attitudes and opinions. Collaborative-filtering which focuses on the relationship between users and items and the similarity of two items is established by the similarity of their ratings by users, and is useful for incorporating into recommender systems to improve suggestion reliability. For each user review, the sentiment rating was predicted by using the Vader lexicon approach to calculate the polarity score, which was then evaluated using various machine learning algorithms such as K Nearest Neighbour, Random Forest, Extra Tree, SGD, Decision Tree, and Naive Bayes. The user's rating was used to perform the collaborative filtering. And these predictions are integrated to static weights and a rating inference strategy incorporating collaborative filtering and textual user reviews is performed. As a result of this study, the proposed approach results in better performance with a precision score of 0.971. [ABSTRACT FROM AUTHOR]

Published
2023
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22. A Light-Weight Change Detection Method Using YCbCr-Based Texture Consensus Model.

Author

Singh, Rimjhim Padam and Sharma, Poonam

Subjects
*COMPUTER vision, *ALGORITHMS, *TEXTURES, *APPLICATION software
Abstract

Background subtraction is a prerequisite and often the very first step employed in several high-level and real-time computer vision applications. Several parametric and non-parametric change detection algorithms employing multiple feature spaces have been proposed to date but none has proven to be robust against all challenges that can possibly be posed in a complex real-time environment. Amongst the varied challenges posed, illumination variations, shadows, dynamic backgrounds, camouflaged and bootstrapping artifacts are some of the well-known problems. This paper presents a light-weight hybrid change detection algorithm that integrates a novel combination of RGB color space and conditional YCbCr-based XCS-LBP texture descriptors (YXCS-LBP) into a modified pixel-based background model. The conditional employment of light-weight YXCS-LBP texture features with the modified Visual background extractor (ViBe) aiming at reduction in false positives, produces outperforming results without incurring much memory and computational cost. The random and time-subsampled update strategy employed with the proposed classification procedure ensures the efficient suppression of shadows and bootstrapping artifacts along with the complete retention of long-term static objects in the foreground masks. Comprehensive performance analysis of the proposed technique on publicly available Change Detection dataset (2014 CDnet dataset) demonstrates the superiority of the proposed technique over different state-of-the-art-methods against varied challenges. [ABSTRACT FROM AUTHOR]

Published
2020
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