International Journal of Medical and All Body Health Research  |  e-ISSN: 2582-8940  |  Double-Blind Peer Review  |  Open Access  |  CC BY-NC-SA 4.0

Current Issues
     2026:7/4

International Journal of Medical and All Body Health Research

e-ISSN: 2582-8940 | Open Access

NeuroWave EEG and the Cognitive Connection: Advancing Mental Health AI with Facial Recognition and Graph Theory

Article Information

Volume / Issue2 / 1 (January-March 2021)
Pages36-40
AccessOpen Access

Publication Timeline

  • Received23 December 2020
  • Accepted20 January 2021

Full Text (PDF)

Open Access - Free to Download

Download Full Article (PDF) JATS XML

Abstract

Recent advances in drug pharmacokinetic and pharmacodynamic behavior now allow for a more logical approach to the creation of an ideal drug delivery system. It is already apparent that multidisciplinary efforts will mostly be responsible for future success in drug delivery research. Any therapeutic agent that can be made safer and more effective while using an enhanced drug delivery method represents a step forward in the treatment of human diseases as well as significant marketing potential for pharmaceutical companies. The best drug delivery systems send a predetermined dose of medication to the intended site at the right time and place, depending on the etiology and physiological requirements of the body. Traditional pharmaceutical dosage forms are unable to regulate how quickly a medicine is delivered to the target site. Because higher doses frequently result in serious side effects during treatment, novel drug delivery systems (NDDS) are carriers that keep the drug concentration in the therapeutic range for longer periods of time and also, in addition, may deliver the content to the targeted site if so desired and as needed. As a result, the distribution of drugs in non-target tissue and body fluids necessitates therapeutic doses that could greatly exceed the amount required in target cells.

References

  1. World Health Organization. Neurological disorders: public health challenges. World Health Organization; 2006. [Google Scholar][Crossref]
  2. Mehta A, Niaz M, Adetoro A, Nwagwu U. Advancements in manufacturing technology for the biotechnology industry: the role of artificial intelligence and emerging trends. International Journal of Chemistry, Mathematics and Physics. 2024;8(2):12-8. [Google Scholar][Crossref]
  3. Sameer Ali, Hassan Tanveer. A focus on brain health through artificial intelligence and machine learning. International Journal of Multidisciplinary Research and Growth Evaluation. [Google Scholar][Crossref]
  4. Muhammad Tabraiz Ali, Umar Ali, Sameer Ali, Hassan Tanveer. Transforming cardiac care: AI and machine learning innovations. International Journal of Multidisciplinary Research and Growth Evaluation. [Google Scholar][Crossref]
  5. Ahmad A, Hussain HK, Tanveer H, Kiruthiga T, Gupta K. The intelligent heart rate monitoring model for survivability prediction of cardiac arrest patients using deep cardiac learning model. 2023 International Conference on Intelligent Systems and Applications. 2023. [Google Scholar][Crossref]
  6. Tanveer H, Zahra N, Batool N. Time series classification and anomaly detection with deep models. International Journal of Multidisciplinary Research and Growth Evaluation. [Google Scholar][Crossref]
  7. Tanveer H, Adam MA, Khan MA, Ali MA, Shakoor A. Analyzing the performance and efficiency of machine learning algorithms, such as deep learning, decision trees, or support vector machines, on various datasets and applications. The Asian Bulletin of Big Data Management. 2023;3(2):126-36. [Google Scholar][Crossref]
  8. Tanveer H, Zahra N, Batool N. Efficient data delivery through decentralized networks. International Journal of Multidisciplinary Research and Growth Evaluation. [Google Scholar][Crossref]
  9. Zahra N, Tanveer H, Batool N. Efficient data delivery through decentralized networks. International Journal of Multidisciplinary Research and Growth Evaluation. [Google Scholar][Crossref]
  10. Zahra N, Tanveer H, Batool N. Technological advancement in COVID-19 rehabilitation: therapists’ views. International Journal of Multidisciplinary Research and Growth Evaluation. [Google Scholar][Crossref]
  11. Zahra N, Tanveer H, Batool N. Technological advancement in COVID-19 rehabilitation: therapists’ views. International Journal of Multidisciplinary Research and Growth Evaluation. [Google Scholar][Crossref]
  12. Chen JJ, Husnain A, Cheng WW. Exploring the trade-off between performance and cost in facial recognition: deep learning versus traditional computer vision. In: Arai K, editor. Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems. Cham: Springer; 2024. Vol 823. [Google Scholar][Crossref]
  13. Khan AH, Zainab H, Khan R, Hussain HK. Deep learning in the diagnosis and management of arrhythmias. Journal of Social Research. 2024;4(1):50-66. [Google Scholar][Crossref]
  14. Ahmad A, Dharejo N, Saeed F, Shiwlani A, Tahir A, Umar M. Prediction of fetal brain and heart abnormalities using artificial intelligence algorithms: a review. American Journal of Biomedical Science & Research. 2024;22(3):456-66. [Google Scholar][Crossref]
  15. Kan DP, Lee PF. Decrease alpha waves in depression: an electroencephalogram (EEG) study. In: 2015 International Journal of Medical and All Body Health Research www.allmedicaljournal.com International Conference on BioSignal Analysis, Processing and Systems (ICBAPS). 2015 May 26. p. 156-61. IEEE. [Google Scholar][Crossref]
  16. Mehta A, Choudhary V, Niaz M, Nwagwu U. Artificial intelligence chatbots and sustainable supply chain optimization in manufacturing: examining the role of transparency, innovativeness, and industry. Innovations in Industry. 2023;4:1-12. [Google Scholar][Crossref]
  17. Cohen MX, Elger CE, Fell J. Oscillatory activity and phase–amplitude coupling in the human medial frontal cortex during decision making. Journal of Cognitive Neuroscience. 2008;21(2):390-402. [Google Scholar][Crossref]
  18. Husnain A, Saeed A. AI-enhanced depression detection and therapy: analyzing the VPSYC system. IRE Journals. 2024;8(2):162-8. [Google Scholar][Crossref]
  19. Mehta A, Niaz M, Adetoro A, Nwagwu U. Advancements in manufacturing technology for the biotechnology industry: the role of artificial intelligence and emerging trends. International Journal of Chemistry, Mathematics and Physics. 2024;8(2):12-8. [Google Scholar][Crossref]
  20. Priyadarshani M, Kumar P, Babulal KS, Rajput DS, Patel H. Human brain waves study using EEG and deep learning for emotion recognition. IEEE Access. 2024 Jul 15. [Google Scholar][Crossref]
  21. Dodda S, Kamuni N, Arlagadda JS, Vuppalapati VS, Vemasani P. A survey of deep learning approaches for natural language processing tasks. International Journal on Recent and Innovation Trends in Computing and Communication. 2024;9:27-36. [Google Scholar][Crossref]
  22. Abid N. Enhanced IoT network security with machine learning techniques for anomaly detection and classification. International Journal of Current Engineering and Technology. 2023;13(6):536-44. [Google Scholar][Crossref]
  23. Khan AH, Zainab H, Khan R, Hussain HK. Deep learning in the diagnosis and management of arrhythmias. Journal of Social Research. 2024;4(1):50-66. [Google Scholar][Crossref]
  24. Borsboom D. A network theory of mental disorders. World Psychiatry. 2017;16(1):5-13. [Google Scholar][Crossref]
  25. Arif A, Khan MI, Khan A. An overview of cyber threats generated by AI. International Journal of Multidisciplinary Sciences and Arts. 2024;3(4):67-76. [Google Scholar][Crossref]
  26. Benedetti F. The patient's brain: the neuroscience behind the doctor-patient relationship. Oxford: OUP; 2010. [Google Scholar][Crossref]
  27. Khan MI, Arif A, Khan A. AI's revolutionary role in cyber defense and social engineering. International Journal of Multidisciplinary Sciences and Arts. 2024;3(4):57-66. [Google Scholar][Crossref]
  28. Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A. A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR). 2021;54(6):1-35. [Google Scholar][Crossref]

How to Cite

Tanveer, H., & Shirazi, R. (2021). NeuroWave EEG and the Cognitive Connection: Advancing Mental Health AI with Facial Recognition and Graph Theory. International Journal of Medical and All Body Health Research, 2(1), 36-40.

Tanveer, Hassan, and Rozhin Shirazi. "NeuroWave EEG and the Cognitive Connection: Advancing Mental Health AI with Facial Recognition and Graph Theory." International Journal of Medical and All Body Health Research, vol. 2, no. 1, 2021, pp. 36-40.

Tanveer, H. and Shirazi, R. (2021) 'NeuroWave EEG and the Cognitive Connection: Advancing Mental Health AI with Facial Recognition and Graph Theory', International Journal of Medical and All Body Health Research, 2(1), pp. 36-40.

Tanveer H, Shirazi R. NeuroWave EEG and the Cognitive Connection: Advancing Mental Health AI with Facial Recognition and Graph Theory. International Journal of Medical and All Body Health Research. 2021;2(1):36-40.

Share

Find this Article on

Google Scholar Semantic Scholar OpenAlex ResearchGate BASE

A newly published article may take a few days to appear on these services.