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417 results on '"Sharma, Manmohan"'

29. Performance Evaluation of SDN Controllers

Author

Ryait, Deepjyot Kaur, Sharma, Manmohan, 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, Ranganathan, G., editor, Papakostas, George A., editor, and Rocha, Álvaro, editor

Published
2023
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30. Load Balancing using Probability Distribution in Software Defined Network

Author

Ryait, Deepjyot Kaur, Sharma, Manmohan, 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 Coello, Carlos A., editor, and Bansal, Jagdish Chand, editor

Published
2023
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31. Statistical Influence of Parameters on the Performance of SDN

Author

Ryait, Deepjyot Kaur, Sharma, Manmohan, 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, Sharma, Harish, editor, Shrivastava, Vivek, editor, Bharti, Kusum Kumari, editor, and Wang, Lipo, editor

Published
2023
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39. Alcohol reduces choosiness and relaxes mate preferences in female Drosophila simulans.

Author

Archer, C Ruth, Alper, Cleo, Mack, Laura, Weedon, Melanie, Sharma, Manmohan D, Sutter, Andreas, and Hosken, David J

Subjects
SEXUAL selection, HUMAN sexuality, MATE selection, GENETIC variation, GENETIC testing
Abstract

Alcohol can alter sexual behaviour. However, our understanding of alcohol's effects on sexual interactions is limited because males are usually the focus of nonhuman studies. Alcohol could in theory alter female mate-choice and, in turn, affect sexual selection, a major mechanism of evolution. Here we tested whether alcohol exposure affected female mate-preference, choosiness, and copulation duration in the fly Drosophila simulans, while simultaneously testing for genetic variation in these effects. We found that alcohol exposure did not affect copulation duration, but did weaken mate-preference, as females copulated with a broader range of males after exposure, and it tended to reduce female choosiness as females mated more quickly. Although female genotype significantly affected copulation duration and choosiness, the effects of alcohol appeared to be reasonably consistent across genotypes, but this depended on the analysis employed. Nonetheless, alcohol reduced female choosiness and weakened mate-preference, and may therefore reduce the strength of sexual selection. Testing whether these changes impact female fitness is now required. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Automated detection of Bornean white-bearded gibbon (Hylobates albibarbis) vocalizations using an open-source framework for deep learning.

Author

Owens, A. F., Hockings, Kimberley J., Imron, Muhammed Ali, Madhusudhana, Shyam, Mariaty, Setia, Tatang Mitra, Sharma, Manmohan, Maimunah, Siti, Van Veen, F. J. F., and Erb, Wendy M.

Subjects
ARTIFICIAL neural networks, BIOACOUSTICS, ACOUSTIC models, SOUND recordings, RECORD collecting, DEEP learning
Abstract

Passive acoustic monitoring is a promising tool for monitoring at-risk populations of vocal species, yet, extracting relevant information from large acoustic datasets can be time-consuming, creating a bottleneck at the point of analysis. To address this, an open-source framework for deep learning in bioacoustics to automatically detect Bornean white-bearded gibbon (Hylobates albibarbis) "great call" vocalizations in a long-term acoustic dataset from a rainforest location in Borneo is adapted. The steps involved in developing this solution are described, including collecting audio recordings, developing training and testing datasets, training neural network models, and evaluating model performance. The best model performed at a satisfactory level (F score = 0.87), identifying 98% of the highest-quality calls from 90 h of manually annotated audio recordings and greatly reduced analysis times when compared to a human observer. No significant difference was found in the temporal distribution of great call detections between the manual annotations and the model's output. Future work should seek to apply this model to long-term acoustic datasets to understand spatiotemporal variations in H. albibarbis' calling activity. Overall, a roadmap is presented for applying deep learning to identify the vocalizations of species of interest, which can be adapted for monitoring other endangered vocalizing species. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Intrusion Detection and Prevention Systems: A Review

Author

Ganesh, Vaishnavi, Sharma, Manmohan, 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, Ranganathan, G., editor, Chen, Joy, editor, and Rocha, Álvaro, editor

Published
2021
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42. Mobility Management Scheme During Offloading in Mobile Cloud Computing

Author

Mathur, Robin Prakash, Sharma, Manmohan, 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, Choudhury, Sushabhan, editor, Gowri, R., editor, Sena Paul, Babu, editor, and Do, Dinh-Thuan, editor

Published
2021
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47. Predicting Academic Performance of International Students Using Machine Learning Techniques and Human Interpretable Explanations Using LIME—Case Study of an Indian University

Author

Kumar, Pawan, Sharma, Manmohan, 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, Khanna, Ashish, editor, Gupta, Deepak, editor, Bhattacharyya, Siddhartha, editor, Snasel, Vaclav, editor, Platos, Jan, editor, and Hassanien, Aboul Ella, editor

Published
2020
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