Affiliations: [a] Department of Mathematics, The Arab Academic College for Education in Israel-Haifa, Israel
| [b] Department of Biochemistry, Symbiosis Medical College for Women, Symbiosis International (Deemed University), Pune, India
| [c] Research Centre for Human-Machine Collaboration (HUMAC), Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Kuala Lumpur, Malaysia
| [d] College of Engineering, Department of Chemical Engineering, University of Bahrain, Bahrain
| [e] Universidad Peruana Los Andes, Decano de la Facultad de Ingenieria, Huancayo, Perú | [f] Universidad Privada del Norte, Peru, Doctor of Management, Project Management Master’s student, Operations Manager of LaboratoriosSostenibles para América Latina, Certified Green Project Manager, GPM-bTM, Visiting professor at the Universidad Nacional Autónoma de México (UNAM) | [g] Head of the Department, Department of Artificial Intelligence and Data Science, K. Ramakrishnan College of Engineering, Trichy, Tamil Nadu, India
Correspondence:
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Corresponding author. Yousef Methkal Abd Algani, Department of Mathematics,The Arab Academic College for Education in Israel-Haifa, Israel. E-mail: [email protected].
Abstract: Growing older is a phenomenon that is associated with increasingly complex health situations as a result of the coexistence of several chronic diseases. As a result, there is a downward tendency in both older people and their caretakers’ quality of life, which frequently results in frailty. There are numerous solutions available to treat the issue, which primarily affects older people. The basic and most popular imaging method for predicting cognitive impairment is magnetic resonance imaging. Furthermore, few of the earlier models had a definite level of accuracy when diagnosing the condition. Further, there is a critical need to put in place a stronger, more reliable approach to precise prediction. When compared to other procedures, using magnetic resonance images to predict cognitive decline is the safest and most straightforward. The advanced concept for a better optimized strategy to predict cognitive impairment at an early stage is presented in this research. The hybrid krill herd and grey wolf optimization method is offered as a solution to address the challenges in locating the impacted area. In a short amount of time, a significant number of MRI images are analyzed, and the results show a more precise or higher rate of recognition.
