Intelligent Elderly Care: Practicing Ambiguity Neutrosophic Theory for Optimization Contemporary Machine Learning Techniques in Elderly Care

Abstract

 One of the biggest challenges facing healthcare systems throughout the world is the aging 
population. The growing number of elderly citizens in need of specialized care is severely straining 
the available resources and care methods. It is sometimes difficult for traditional care techniques to 
address the complex and varied requirements of this expanding population. To fix these challenges, 
inclusion of contemporary technology is imperative  and pragmatic solutions such as Internet of 
Thing (IoT), cloud computing (CC), and artificial intelligence (AI) techniques such as machine 
learning (ML), deep learning (DL). Such AI has potential role to make elderly care services (ECSs) 
to be intelligent ECSs (IECSs) through providing proactivity and earlier detection based on smart 
IoT sensors. Therefore, this study seeks to achieve two objectives. Firstly, leveraging the capabilities 
of ML techniques to revolutionize care delivery to be intelligent, optimize resource allocation, and 
proactive. Secondly, evaluating the robustness of utilized ML techniques in serving study's 
objectives. Accordingly, utilized ML Techniques consider alternatives (MLTs ) that evaluate based 
on CRiteria Importance Through Inter-criteria Correlation (CRITIC) to obtain weights for criteria 
which alternatives evaluated based on. These weights are leveraging in Technique for Order of 
Preference by Similarity to Ideal Solution (TOPSIS) to rank MLTs alternatives. For bolstering the 
evaluation process, we are integrating uncertainty theory of Probabilistic Simplified Neutrosophic 
Set (PSNS) which effectively captures the inherent uncertainty and imprecision   

DOI: 10.5281/zenodo.14538192