A systematic Analysis: Molecular Information in viral Disease using Deep Learning Auto Encoder

Hundreds of millions of people around the world suffer from viral infections every year. However, some of them have neither vaccine nor effective treatment during and after viral infection. Such as pneumonia, severe acute respiratory syndrome type 2 (SARS -2), HIV infection and Hepatitis-C virus. These viral diseases also directly and indirectly cause cardiovascular disease (CVD). Recently, the Deep Neural Network (DNN)-assisted molecular interaction (information) (MI) transceiver (transmitter Tx, and receiver Rx) design was brought to the fore to break the issues of traditional molecular information (MI) inside and outside human body. In this paper, we use DNN based approach to design and implement a new transceiver (Tx/Rx). We investigate DNN-assisted MI- Tx/Rx, multilayer perception DNN auto-encoder (MLP-AE), and convolutional neural network auto-encoder (CNN-AE), respectively. We apply an MLP-AE and CNN-AE to simultaneously accomplish the task of modulation, demodulation, and equalization as a point-to-point scheme. © 2021 IEEE.

Performance Evaluation of Hospital Site Suitability Using Multilayer Perceptron (MLP) and Analytical Hierarchy Process (AHP) Models in Malacca, Malaysia

This study focuses on suitable site identification for constructing a hospital in Malacca, Malaysia. Using significant environmental, topographic, and geodemographic factors, the study evaluated and compared machine learning (ML) and multicriteria decision analysis (MCDA) for hospital site suitability mapping to discover the highest influential factors that minimize the error ratio and maximize the effectiveness of the suitability investigation. Identification of the most significant conditioning parameters that impact the choice of an appropriate hospital site was accomplished using correlation-based feature selection (CFS) with a search algorithm (greedy stepwise). To model the potential hospital site map, we utilized multilayer perceptron (MLP) and analytical hierarchy process (AHP) models. The outcome of the predicted site models was validated utilizing CFS 10-fold cross-validation, as well as ROC curve (receiver operating characteristic curve). The analysis of CFS indicated a very high correlation with R2 values of 0.99 for the MLP model. However, the ROC curve indicated a prediction accuracy of 80% for the MLP model and 83% for the AHP model. The findings revealed that the MLP model is reliable and consistent with the AHP. It is a sufficiently promising approach to the location suitability of hospitals to ensure effective planning and performance of healthcare delivery.