An exploratory simulation study and prediction model on human brain behavior and activity using an integration of deep neural network and biosensor Rabi antenna

• Main Authors: Pham, Nhat Truong, Bunruangses, Montree, Youplao, Phichai, Garhwal, Anita, Ray, Kanad, Roy, Arup, Boonkirdram, Sarawoot, Yupapin, Preecha, Jalil, Muhammad Arif, Ali, Jalil, Kaiser, Shamim, Mahmud, Mufti, Mallik, Saurav, Zhao, Zhongming
• Format: Article
• Language: English
• Published: Elsevier Ltd 2023
• Subjects: QC Physics
• Online Access: http://eprints.utm.my/107547/1/MuhammadArifJalil2023_AnExploratorySimulationStudyAndPredictionModel.pdf; http://eprints.utm.my/107547/; http://dx.doi.org/10.1016/j.heliyon.2023.e15749
• Institution: Universiti Teknologi Malaysia

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.