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4. An Automated Sleep Stages Classification Using BrainEEG Signal: A Machine Learning Approaches

Author

Satapathy, Santosh Kumar, Sangameswar, M. V., Loganathan, D., 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, Chandramohan, S., editor, Venkatesh, Bala, editor, Sekhar Dash, Subhransu, editor, Das, Swagatam, editor, and Sharmeela, C., editor

Published
2022
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5. A Machine Learning Model for Automated Classification of Sleep Stages using Polysomnography Signals

Author

Satapathy, Santosh Kumar, Loganathan, D., Sharathkumar, S., Narayanan, Praveena, 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, Hassanien, Aboul Ella, editor, Anand, Sameer, editor, and Jaiswal, Ajay, editor

Published
2022
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6. A Machine Learning Model for Automated Classification of Sleep Stages Using Polysomnography Signals

Author

Satapathy, Santosh Kumar, Kondaveeti, Hari Kishan, Loganathan, D., Sharathkumar, S., 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, 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, Bajpai, Manish Kumar, editor, Kumar Singh, Koushlendra, editor, and Giakos, George, editor

Published
2021
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7. SleepEEG Study: Automated Sleep Stage Scoring with Machine Learning Techniques from Dual-Channel Brain-EEG Signals

Author

Satapathy, Santosh Kumar, Loganathan, D., 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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13. Automated Sleep Staging Using Convolution Neural Network Based on Single-Channel EEG Signal

Author

Satapathy, Santosh Kumar, Sharathkumar, S., Loganathan, D., 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, Gupta, Mukesh Kumar, editor, Tomar, G. S., editor, and Lipo, Wang, editor

Published
2021
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14. Effect of Linear Features to Determination of Sleep Stages Classification from Dual Channel of EEG Signal Using Machine Learning Techniques

Author

Satapathy, Santosh Kumar, Loganathan, D., 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, Mandal, Jyotsna K., editor, Mukhopadhyay, Somnath, editor, and Roy, Alak, editor

Published
2021
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15. Automated Sleep Staging of Human Polysomnography Recordings Using Single-Channel of EEG Signals

Author

Satapathy, Santosh Kumar, Loganathan, D., Pattnaik, Shrinibas, Rath, Ramakrushna, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, di Mare, Francesca, Series Editor, Manik, Gaurav, editor, Kalia, Susheel, editor, Sahoo, Sushanta Kumar, editor, Sharma, Tarun K., editor, and Verma, Om Prakash, editor

Published
2021
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20. Effect of EEG Dual-Channel Acquisition and Gender Specification Subjects on the Classification of Sleep Stages Using Machine Learning Techniques

Author

Satapathy, Santosh Kumar, Loganathan, D., Mitra, Anirban, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Gupta, Shilpi, editor, and Sarvaiya, Jignesh N., editor

Published
2020
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21. Automated Sleep Stage Classification Based on Multiple Channels of Electroencephalographic Signals Using Machine Learning Algorithm

Author

Satapathy, Santosh Kumar, Loganathan, D., 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, Dutta, Maitreyee, editor, Krishna, C. Rama, editor, Kumar, Rakesh, editor, and Kalra, Mala, editor

Published
2020
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23. Contributors

Author

Adeniyi, Abidemi Emmanuel, primary, Adhikari, Surabhi, additional, Ajagbe, Sunday Adeola, additional, Awotunde, Joseph Bamidele, additional, Barsocchi, Paolo, additional, Bhardwaj, Sachit, additional, Bhoi, Akash Kumar, additional, Huy Cuong, Nguyen Ha, additional, Deepalakshmi, P., additional, Fong, Simon James, additional, Ganeshkumar, M., additional, Ghidoni, Stefano, additional, Ghimire, Awishkar, additional, Bernardo Gois, Francisco Nauber, additional, González-Briones, Alfonso, additional, Gopalakrishnan, E.A., additional, Guleria, Pratiyush, additional, Ijaz, Muhammad Fazal, additional, Izonin, Ivan, additional, Kosana, Vishalteja, additional, Li, Tengyue, additional, Loganathan, D., additional, Lumini, Alessandra, additional, Maguolo, Gianluca, additional, Lôbo Marques, João Alexandre, additional, Nagaraj, P., additional, Nanni, Loris, additional, Rana, Abu ul Hassan S., additional, Satapathy, Santosh, additional, Sharma, Akhilesh Kumar, additional, Nunes da Silveira, Jarbas Aryel, additional, Soman, K.P., additional, Sood, Manu, additional, Sowmya, V., additional, Srivastava, Devesh Kumar, additional, Teeparthi, Kiran, additional, Thapa, Surendrabikram, additional, Tiwari, Shamik, additional, Tkachenko, Roman, additional, and Madeiro, João Paulo do Vale, additional

Published
2022
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36. OPTIMIZING EFFICIENCY AND PERFORMANCE IN 5G NETWORKS THROUGH A DYNAMIC RESOURCE ALLOCATION ALGORITHMIC FRAMEWORK.

Author

Sridevi, M. J., Sharma, Pradosh Kumar, Kumar, M. Dhiliphan, Loganathan, D., and Ahmed, Saleem

Subjects
RESOURCE allocation, NETWORK performance, AUGMENTED reality, STOCHASTIC learning models
Abstract

With the exponential growth of data demand and the advent of 5G networks, the need for efficient resource allocation algorithms has become paramount. This study presents a dynamic resource allocation algorithmic framework aimed at optimizing efficiency and performance in 5G networks. The framework focuses on frequency reuse at the edges while employing fractional pilots for enhanced spectrum utilization. 5G networks promise unprecedented speeds and low latency, enabling a wide array of applications from IoT to augmented reality. However, the efficient allocation of resources remains a challenge, especially at the network edges where interference is high. Traditional static resource allocation schemes fail to adapt to dynamic network conditions, leading to suboptimal performance. The main challenge lies in effectively managing resources to meet the diverse demands of various applications while mitigating interference and maximizing spectral efficiency. The proposed framework employs a dynamic resource allocation algorithm that adapts to changing network conditions in real-time. Leveraging fractional pilots, the algorithm optimizes frequency reuse at the network edges, thereby enhancing spectral efficiency. The framework integrates stochastic learning for predictive analytics to anticipate resource demands and interference patterns. Simulation results demonstrate significant improvements in spectral efficiency and network performance compared to traditional static allocation methods. The utilization of fractional pilots effectively reduces interference, enabling higher throughput and lower latency, especially at the network edges. The dynamic nature of the algorithm ensures adaptability to varying traffic loads, leading to enhanced overall network efficiency. [ABSTRACT FROM AUTHOR]

Published
2024
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