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Your search keyword '"Kuila, Pratyay"' showing total 30 results
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30 results on '"Kuila, Pratyay"'

4. Particle Swarm Optimization for Efficient Placement of Relay Nodes in Cluster-Based Wireless Sensor Networks

Author

Som, Suman, Bey, Marlom, Kuila, Pratyay, Chan, Albert P. C., Series Editor, Hong, Wei-Chiang, Series Editor, Mellal, Mohamed Arezki, Series Editor, Narayanan, Ramadas, Series Editor, Nguyen, Quang Ngoc, Series Editor, Ong, Hwai Chyuan, Series Editor, Sachsenmeier, Peter, Series Editor, Sun, Zaicheng, Series Editor, Ullah, Sharif, Series Editor, Wu, Junwei, Series Editor, Zhang, Wei, Series Editor, Murugan, R, editor, Karsh, Ram Kumar, editor, Goel, Tripti, editor, and Laskar, Rabul Hussain, editor

Published
2024
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5. Particle Swarm Optimization based Efficient Cluster Formation in Vehicular Networks

Author

Kumar, Saket, Kuila, Pratyay, Chan, Albert P. C., Series Editor, Hong, Wei-Chiang, Series Editor, Mellal, Mohamed Arezki, Series Editor, Narayanan, Ramadas, Series Editor, Nguyen, Quang Ngoc, Series Editor, Ong, Hwai Chyuan, Series Editor, Sachsenmeier, Peter, Series Editor, Sun, Zaicheng, Series Editor, Ullah, Sharif, Series Editor, Wu, Junwei, Series Editor, Zhang, Wei, Series Editor, Murugan, R, editor, Karsh, Ram Kumar, editor, Goel, Tripti, editor, and Laskar, Rabul Hussain, editor

Published
2024
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6. Energy Efficient Workflow Scheduling in Cloud Computing Systems using Particle Swarm Optimization

Author

Kumar, Abhishek, Ghosh, Santanu, Naik, B. Balaji, Kuila, Pratyay, Chan, Albert P. C., Series Editor, Hong, Wei-Chiang, Series Editor, Mellal, Mohamed Arezki, Series Editor, Narayanan, Ramadas, Series Editor, Nguyen, Quang Ngoc, Series Editor, Ong, Hwai Chyuan, Series Editor, Sachsenmeier, Peter, Series Editor, Sun, Zaicheng, Series Editor, Ullah, Sharif, Series Editor, Wu, Junwei, Series Editor, Zhang, Wei, Series Editor, Murugan, R, editor, Karsh, Ram Kumar, editor, Goel, Tripti, editor, and Laskar, Rabul Hussain, editor

Published
2024
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7. Detection and Forecasting of Extreme event in Stock Price Triggered by Fundamental, Technical, and External Factors

Author

Rai, Anish, Luwang, Salam Rabindrajit, Nurujjaman, Md, Hens, Chittaranjan, Kuila, Pratyay, and Debnath, Kanish

Subjects
Quantitative Finance - Statistical Finance
Abstract

The sporadic large fluctuations are seen in the stock market due to changes in fundamental parameters, technical setups, and external factors. These large fluctuations are termed as Extreme Events (EE). The EEs may be positive or negative depending on the impact of these factors. During such events, the stock price time series is found to be nonstationary. Hence, the Hilbert-Huang transformation (HHT) is used to identify EEs based on their high instantaneous energy ($IE$) concentration. The analysis shows that the $IE$ concentration in the stock price is very high during both positive and negative EE with $IE>E_{\mu}+4\sigma,$ where $E_{\mu}$ and $\sigma$ are the mean energy and standard deviation of energy, respectively. Further, support vector regression is used to predict the stock price during an EE, with the close price being the most helpful input than the open-high-low-close (OHLC) inputs. The maximum prediction accuracy for one step using close price and OHLC prices are 95.98\% and 95.64\% respectively. Whereas, for the two steps prediction, the accuracies are 94.09\% and 93.58\% respectively. The EEs found from the predicted time series shows similar statistical characteristics that were obtained from the original data. The analysis emphasizes the importance of monitoring factors that lead to EEs for a compelling entry or exit strategy as investors can gain or lose significant amounts of capital due to these events., Comment: 13 pages

Published
2022
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29. Improved genetic algorithm-based sensor nodes deployment for barrier coverage

Author

Harizan, Subash, Kuila, Pratyay, Kumar, Rajeev, Khare, Akhilendra, Clonia, Reeta, and Perti, Ashwin

Abstract

Barrier coverage is widely used for an intruder detection. However, sensor nodes (SNs) are prone to failure. Hence it is very challenging to construct a barrier with an efficient coverage and connectivity with minimum number of SNs. From a given set of potential positions (PPs), finding minimum number of PPs for the placement of SNs to form a barrier is an NP-complete problem. In this paper, we propose an improved genetic algorithm (GA)-based approach to solve the aforesaid problem. For the better performance and fast convergence of the algorithm, a novel mutation operation is introduced. In our proposed approach chromosomes are efficiently represented along with an efficient fitness function to evaluate the quality. An extensive simulation is conducted on the various scenarios of the network. The efficiency of the proposed algorithm is shown by comparing the simulated results with traditional genetic algorithm (GA), differential evolution (DE) and GreedyCSC algorithms.

Published
2023
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30. MAINTENANCE POLICY SELECTION OF N-COMPONENT REPAIRABLE SYSTEM USING GENETIC ALGORITHM.

Author

Srivastava, Nishit Kumar, Kuila, Pratyay, Chatterjee, Namrata, Subramanic, A. K., and Jan, N. Akbar

Subjects
GENETIC algorithms, MANUFACTURING processes, PRODUCTION planning, MAINTENANCE costs, CHROMOSOMES, ARTIFICIAL intelligence
Abstract

Copyright of Serbian Journal of Management is the property of Serbian Journal of Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2022
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