An exploratory simulation study and prediction model on human brain behavior and activity using an integration of deep neural network and biosensor Rabi antenna
Nhat Truong Pham,
Montree Bunruangses,
Phichai Youplao,
Anita Garhwal,
Kanad Ray,
Arup Roy,
Sarawoot Boonkirdram,
Preecha Yupapin,
Muhammad Arif Jalil,
Jalil Ali,
Shamim Kaiser,
Mufti Mahmud,
Saurav Mallik,
Zhongming Zhao
Affiliations
Nhat Truong Pham
Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
Montree Bunruangses
Department of Computer Engineering, Faculty of Industrial Education, Rajamangala University of Technology Phra Nakhon, Bangkok 10300, Thailand
Phichai Youplao
Department of Electrical Engineering, Faculty of Industry and Technology, Rajamangala University of Technology Isan Sakon Nakhon Campus, 199 Village no. 3, Phungkon, Sakon Nakhon 47160, Thailand
Anita Garhwal
Asia Metropolitan University, 6, Jalan Lembah, Bandar Baru Seri Alam 81750, Masai, Johor, Malaysia
Kanad Ray
Amity School of Applied Sciences, Amity University Rajasthan, Jaipur, India; Facultad de CienciasFisico-Matematicas, Benemérita Universidad Autónoma de Puebla, Av. San Claudio y AV. 18 sur, Col. San Manuel Ciudad Universitaria, Pueble Pue. 72570, Mexico; Faubert Lab, Ecole d'optométrie, Université de Montréal, Montréal, QC H3T1P1, Canada
Arup Roy
School of Computing and Information Technology, Reva University, Bengaluru, Karnataka 560064, India
Sarawoot Boonkirdram
Program of Electrical and Electronics, Faculty of Industrial Technology, Sakon Nakhon Rajabhat University, 680 Nittayo, Mueang, Sakon Nakhon 47000, Thailand
Preecha Yupapin
Department of Electrical Technology, School of Industrial Technology, Sakonnakhon Technical College, Institute of Vocational Education Northeastern 2, Sakonnakhon 47000, Thailand
Muhammad Arif Jalil
Department of Physics, Faculty of Science, Unversiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia
Jalil Ali
Department of Electrical Engineering, Faculty of Industry and Technology, Rajamangala University of Technology Isan Sakon Nakhon Campus, 199 Village no. 3, Phungkon, Sakon Nakhon 47160, Thailand
Shamim Kaiser
Institute of Information Technology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
Mufti Mahmud
Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, United Kingdom
Saurav Mallik
Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA 02115, USA; Corresponding author.
Zhongming Zhao
Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Corresponding author.
The plasmonic antenna probe is constructed using a silver rod embedded in a modified Mach-Zehnder interferometer (MZI) ad-drop filter. Rabi antennas are formed when space-time control reaches two levels of system oscillation and can be used as human brain sensor probes. Photonic neural networks are designed using brain-Rabi antenna communication, and transmissions are connected via neurons. Communication signals are carried by electron spin (up and down) and adjustable Rabi frequency. Hidden variables and deep brain signals can be obtained by external detection. A Rabi antenna has been developed by simulation using computer simulation technology (CST) software. Additionally, a communication device has been developed that uses the Optiwave program with Finite-Difference Time-Domain (OptiFDTD). The output signal is plotted using the MATLAB program with the parameters of the OptiFDTD simulation results. The proposed antenna oscillates in the frequency range of 192 THz to 202 THz with a maximum gain of 22.4 dBi. The sensitivity of the sensor is calculated along with the result of electron spin and applied to form a human brain connection. Moreover, intelligent machine learning algorithms are proposed to identify high-quality transmissions and predict the behavior of transmissions in the near future. During the process, a root mean square error (RMSE) of 2.3332(±0.2338) was obtained. Finally, it can be said that our proposed model can efficiently predict human mind, thoughts, behavior as well as action/reaction, which can be greatly helpful in the diagnosis of various neuro-degenerative/psychological diseases (such as Alzheimer's, dementia, etc.) and for security purposes.
