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Your search keyword '"Sahu, Tirath Prasad"' showing total 107 results
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107 results on '"Sahu, Tirath Prasad"'

2. Exploring Label-Specific Feature Weights for Multi-label Feature Selection Using FWMABAC-MFS

Author

Verma, Gurudatta, Sahu, Tirath Prasad, 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, Kole, Dipak Kumar, editor, Roy Chowdhury, Shubhajit, editor, Basu, Subhadip, editor, Plewczynski, Dariusz, editor, and Bhattacharjee, Debotosh, editor

Published
2024
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3. Binary Chaotic Gray Wolf Optimizer-Based Feature Selection for Intrusion Detection: A Comprehensive Study and Performance Evaluation

Author

Upendra, Tripathi, Rakesh, Sahu, Tirath Prasad, 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, Das, Swagatam, editor, Saha, Snehanshu, editor, Coello, Carlos A. Coello, editor, Rathore, Hemant, editor, and Bansal, Jagdish Chand, editor

Published
2024
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4. Stock Market Forecasting Using Additive Ratio Assessment-Based Ensemble Learning

Author

Verma, Satya, Sahu, Satya Prakash, Sahu, Tirath Prasad, 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, Hassanien, Aboul Ella, editor, Castillo, Oscar, editor, Anand, Sameer, editor, and Jaiswal, Ajay, editor

Published
2024
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5. A Multi-label Feature Selection Method Based on Multi-objectives Optimization by Ratio Analysis

Author

Verma, Gurudatta, Sahu, Tirath Prasad, 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, Hassanien, Aboul Ella, editor, Castillo, Oscar, editor, Anand, Sameer, editor, and Jaiswal, Ajay, editor

Published
2024
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17. Global Best Guided Binary Crow Search Algorithm for Feature Selection

Author

Agarwal, Unnati, Sahu, Tirath Prasad, 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, Oneto, Luca, 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, Majhi, Sudhan, editor, Prado, Rocío Pérez de, editor, and Dasanapura Nanjundaiah, Chandrappa, editor

Published
2022
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31. Wavelet decomposition-based multi-stage feature engineering and optimized ensemble classifier for stock market prediction.

Author

Verma, Satya, Sahu, Satya Prakash, and Sahu, Tirath Prasad

Subjects
DISCRETE wavelet transforms, BUSINESS forecasting, STOCK price indexes, PARTICLE swarm optimization, NASDAQ composite index
Abstract

Expanded feature engineering to handle complex and noisy stock data is not much explored in financial forecasting. Along with feature engineering, hyperparameter tuning is also important. This paper provides a solution to handle these issues by elaborating on Discrete Wavelet Transform (DWT)-based feature engineering and hyperparameter-tuned ensemble model. The Multi-Stage Feature Engineering (MSFE) is proposed in which DWT-based decomposition is utilized to handle the noise. DWT decomposition expands the features; therefore, two-stage feature reduction is proposed in which first the filter method is used, and then the probabilistic method is utilized. Next, hyperparameter tuning of the ensemble model is offered through Particle Swarm Optimization (PSO). The proposed model is named as Wavelet-Particle Swarm Optimization (WPSO). WPSO is tested and evaluated on the three stock indices (NIFTY, NASDAQ, and NYSE), and provided 92.51%, 94.18%, and 87.62% accuracy for NIFTY, NASDAQ, and NYSE, respectively. The WPSO is validated by comparing it with state-of-the-art methods. The performance of the WPSO is statistically analyzed through the Bonferroni–Dunn post hoc test where WPSO positioned at rank 1 for all the evaluation metric and datasets. The WPSO empirically verifies that improving feature quality through MSFE and hyperparameter tuning of ensemble model significantly improves the predictive outcomes. [ABSTRACT FROM AUTHOR]

Published
2024
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37. MCSO: Levy's Flight Guided Modified Chicken Swarm Optimization.

Author

Verma, Satya, Sahu, Satya Prakash, and Sahu, Tirath Prasad

Subjects
CHICKENS, SWARM intelligence, METAHEURISTIC algorithms, CHICKS, ROOSTERS
Abstract

This paper proposes a Modified Chicken Swarm Optimization (MCSO) in which the local optima and early convergence problem of Chicken Swarm Optimization (CSO) is addressed and solved. The CSO adopted the Swarm Intelligence (SI) of chickens to solve the optimization problem in which the behaviour of roosters, hens, and chicks for food search is mathematically formulated. Hens follow their group rooster, whereas mother hen is followed by chicks in search of food. The problem occurs whenever the rooster follows the wrong path and is stuck in the local optima so the mother hen and, therefore, the chick. This situation leads to early convergence and may not provide global optimization. Most of the existing research studies focused on solving the local optima problem of hens. Hence, there is a need to address the local optima problem of roosters as well. The paper offers a solution to this problem by using the randomness phenomenon of Levy's Fight. Levy's flight is offered to guide the roosters, hens, and chicks, which allows the chickens to choose a random direction in a situation when there is no way to find the optimal solution. The inclusion of Levy's flight enhances the self-learning capability of the chicken. The MCSO is tested on the benchmark functions, IEEE CEC-2017 functions and an engineering problem. The results are validated by a comparative analysis with well-known SI agorithms. The results indicate that the MCSO provides competitive performance. The results are statistically verified with the win-tie-loss, Bonferroni-Dunn post-hoc, and Wilcoxon tests. [ABSTRACT FROM AUTHOR]

Published
2024
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