Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment.

Cognitive assessment plays a vital role in clinical care and research fields related to cognitive aging and cognitive health. Lately, researchers have worked towards providing resolutions to measure individual cognitive health; however, it is still difficult to use those resolutions from the real world, and therefore using deep neural networks to evaluate cognitive health is becoming a hot research topic. Deep learning and human activity recognition are two domains that have received attention for the past few years. The former is for its relevance in application fields like health monitoring or ambient assisted living, and the latter is due to their excellent performance and recent achievements in various fields of application, namely, speech and image recognition. This research develops a novel Symbiotic Organism Search with a Deep Convolutional Neural Network-based Human Activity Recognition (SOSDCNN-HAR) model for Cognitive Health Assessment. The goal of the SOSDCNN-HAR model is to recognize human activities in an end-to-end way. For the noise elimination process, the presented SOSDCNN-HAR model involves the Wiener filtering (WF) technique. In addition, the presented SOSDCNN-HAR model follows a RetinaNet-based feature extractor for automated extraction of features. Moreover, the SOS procedure is exploited as a hyperparameter optimizing tool to enhance recognition efficiency. Furthermore, a gated recurrent unit (GRU) prototype can be employed as a categorizer to allot proper class labels. The performance validation of the SOSDCNN-HAR prototype is examined using a set of benchmark datasets. A far-reaching experimental examination reported the betterment of the SOSDCNN-HAR prototype over current approaches with enhanced precision of 86.51% and 89.50% on Penn Action and NW-UCLA datasets, respectively.

Estimation of tumor parameters using neural networks for inverse bioheat problem.

BACKGROUND AND OBJECTIVE: Some types of cancer cause rapid cell growth, while others cause cells to grow and divide at a slower rate. Certain forms of cancer result in visible growths called tumors. This work proposes an inverse estimation of the size and location of the tumor using a feedforward Neural Network (FFNN) model. METHODS: The forward model is a 3D model of the breast induced with a tumor of various sizes at different locations within the breast, and it is solved using the Pennes equation. The data obtained from the simulation of the bioheat transfer is used for training the neural network. In order to optimize the neural network architecture, the work proposes varying the number of neurons in the hidden layer and thus finding the best fit to create a relationship between the temperature profile and tumor parameters which can be used to estimate the tumor parameters given the temperature profile. RESULTS: These simulations resulted in a temperature distribution profile that could thus be used to locate and determine the parameters of the cancerous tumor within the breast. The prediction accuracy showed the capacity of the trained Feed Forward Neural Network to estimate the unknown parameters within an acceptable range of error. The model validations use the Root Mean Square Error method to quantify and minimize the prediction error. CONCLUSIONS: In this work, a non-intrusive method for the diagnosis of breast cancer was modelled, which yields conclusive results for the estimation of the tumor parameters.

A review study on blood in human coronary artery: Numerical approach.

Computational fluid dynamics (CFD) study of blood flow in human coronary artery is one of the emerging fields of Biomed- ical engineering. In present review paper, Finite Volume Method with governing equations and boundary conditions are briefly discussed for different coronary models. Many researchers have come up with astonishing results related to the various factors (blood viscosity, rate of blood flow, shear stress on the arterial wall, Reynolds number, etc.) affecting the hemodynamic of blood in the right/left coronary artery. The aim of this paper is to present an overview of all those work done by the researchers to justify their work related to factors which hampers proper functioning of heart and lead to Coronary Artery Disease (CAD). Governing equations like Navier-stokes equations, continuity equations etc. are widely used and are solved using CFD solver to get a clearer view of coronary artery blockage. Different boundary conditions and blood properties published in the last ten years are summarized in the tabulated form. This table will help new researchers to work on this area.

Prediction of stenosis behaviour in artery by neural network and multiple linear regressions.

Blood flow analysis in the artery is a paramount study in the field of arterial stenosis evaluation. Studies conducted so far have reported the analysis of blood flow parameters using different techniques, but the regression analysis is not adequately used. Artificial neural network is a nonlinear and nonparametric approach. It uses back-propagation algorithm for regression analysis, which is effective as compared to statistical model that requires a higher domain of statistics for prediction. In our manuscript, twofold analyses of data are done. First phase involves the determination of blood flow parameters using physiological flow pulse generator. The second phase includes regression modelling. The inputs to the model were axial length from stenosis, radial distance, inlet velocity, mean pressure, density, viscosity, time, and degree of blockage. Output included dependent variables in the form of output as mean velocity, root-mean-square (RMS) velocity, turbulent intensity, mean frequency, RMS frequency, frequency of turbulent intensity, gate time mean, gate time RMS. The temperature, density, and viscosity conditions were kept constant for various degrees of blockages. It was followed by regression analysis of variables using conventional statistical and neural network approach. The result shows that the neural network model is more appropriate, because value of percentage of response variation of dependent variable is almost approaching unity as compared to statistical analysis.