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In a generic modern multi-antenna relay system, the incremental cooperative networks use the neighbor nodes to assist the source by sending the source message to the destination for achieving spatial diversity. Therefore, the involvement of cyber-physical Systems (CPS) in smart healthcare has proportionately improved in terms of the relaying schemes. These schemes are equally implicated to terrestrial and non-terrestrial networks. For any non-terrestrial-networks (NTN), the computational intelligence in terms of artificial intelligence (AI) plays a viral role, the work have introduced a novel paradigm of NTN applications using Explainable-artificial intelligence (X-AI) approach. Initially, we propose and analyze a new cooperative relaying scheme for the incremental multi-antenna relays system; namely, incremental multi-antenna relays cooperation with a hybrid relaying scheme (IMARC-H). The IMARC-H, either Amplify-and-forward (AF) or Decode-and-forward (DF) uses relaying scheme based on the X-AI approach. Mathematical formulations have been deducted to support an optimal relationship between signal-to-noise ratio (SNR) and neighbour nodes in the physical layer. The simulations are presented to showcase the validation of the work. The analysis of the obtained simulation results shows that the proposed scheme outperforms in minimizing Bit-Error-Rate (BER) and outage probability as compared to the existing CPS-based relaying schemes such as IMARC-AF, IMARC-DF, etc. [ABSTRACT FROM AUTHOR]

Patient health record analysis models assist the medical field to understand the current stands and medical needs. Similarly, collecting and analyzing the disease features are the best practice for encouraging medical researchers to understand the research problems. Various research works evolve the way of medical data analysis schemes to know the actual challenges against the diseases. The computer-based diagnosis models and medical data analysis models are widely applied to have a better understanding of different diseases. Particularly, the field of medical electronics needs appropriate health indicator extraction models in near future. The existing medical schemes support baseline solutions but lack optimal hypothesis-based solutions. This work describes the optimal hypothesis model and Akin procedures for health record users, to aid health sectors in clinical decision-making on health indications. This work proposes Medical Hypothesis and Health Indicators Extraction from Electronic Medical Records (EMR) and International Classification of Diseases (ICD-10) patient examination database using the Akin Method and Friendship method. In this Health Indicators and Disease Symptoms Extraction (HIDSE), the evidence checking procedures find and collect all possible medical evidence from the existing patient examination report. Akin Method is making the hypothesis decision from count-based evidence principles. The health indicators extraction scheme extracts all relevant information based on the health indicators query and partial input. Similarly, the friendship method is used for making information associations between medical data attributes. This Akin-Friendship model helps to build hypothesis structures and trait-based feature extraction principles. This is called as Composite Akin Friendship Model (CAFM). This proposed model consists of various test cases for developing the medical hypothesis systems. On the other hand, it provides limited accuracy in disease classification. In this regard, the proposed HIDSE implements Deep Learning (DL) based Akin Friendship Method (DLAFM) for improving the accuracy of this medical hypothesis model. The proposed DLAFM, Convolutional Neural Networks (CNN) associated Legacy Prediction Model for Health Indicator (LPHI) is developed to tune the CAFM principles. The results show the proposed health indicator extraction scheme has 8–10% of better system performance than other existing techniques. [ABSTRACT FROM AUTHOR]