scholarly journals COVID-19 mortality rate prediction for India using statistical neural networks and gaussian process regression model

2021 ◽  
Vol 21 (1) ◽  
pp. 194-206
Author(s):  
S Dhamodharavadhani ◽  
R Rathipriya
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mortality Rate ◽  
Regression Model ◽  
Gaussian Process ◽  
Learning Process ◽  
Regression Models ◽  
Gaussian Process Regression ◽  
Statistical Neural Networks ◽  
Death Cases

The primary purpose of this research is to identify the best COVID-19 mortality model for India using regression models and is to estimate the future COVID-19 mortality rate for India. Specifically, Statistical Neural Networks ( Radial Basis Function Neural Network (RBFNN), Generalized Regression Neural Network (GRNN)), and Gaussian Process Regression (GPR) are applied to develop the COVID-19 Mortality Rate Prediction (MRP) model for India. For that purpose, there are two types of dataset used in this study: One is COVID-19 Death cases, a Time Series Data and the other is COVID-19 Confirmed Case and Death Cases where Death case is dependent variable and the Confirmed case is an independent varia- ble. Hyperparameter optimization or tuning is used in these regression models, which is the process of identifying a set of optimal hyperparameters for any learning process with minimal error. Here, sigma (σ) is a hyperparameter whose value is used to constrain the learning process of the above models with minimum Root Mean Squared Error (RMSE). The perfor- mance of the models is evaluated using the RMSE and 'R2 values, which shows that the GRP model performs better than the GRNN and RBFNN. Keywords: Covid-19; India; mortality rate; mortality prediction; regression model; hyperparameter tuning; GPR; GRNN; RBFNN.

scholarly journals A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling

Machines ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 13 ◽  
Author(s):  
Nikolaos Karkalos ◽  
Nikolaos Efkolidis ◽  
Panagiotis Kyratsis ◽  
Angelos Markopoulos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Model ◽  
Soft Computing ◽  
Regression Models ◽  
Fuzzy Inference ◽  
Process Conditions ◽  
Neural Network Models ◽  
Inference System ◽  
Computing Models

Apart from experimental research, the development of accurate and efficient models is considerably important in the field of manufacturing processes. Initially, regression models were significantly popular for this purpose, but later, the soft computing models were proven as a viable alternative to the established models. However, the effectiveness of soft computing models can be often dependent on the size of the experimental dataset, and it can be lower compared to that of the regression models for a small-sized dataset. In the present study, it is intended to conduct a comparison of the performance of various neural network models, such as the Multi-layer Perceptron (MLP), the Radial Basis Function Neural Network (RBF-NN), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) models with the performance of a multiple regression model. For the development of the models, data from drilling experiments on an Al6082-T6 workpiece for various process conditions are employed, and the performance of models related to thrust force (Fz) and cutting torque (Mz) is assessed based on several criteria. From the analysis, it was found that the MLP models were superior to the other neural networks model and the regression model, as they were able to achieve a relatively lower prediction error for both models of Fz and Mz.

IMPLEMENTING GAUSSIAN PROCESS INFERENCE WITH NEURAL NETWORKS

2006 ◽  
Vol 16 (05) ◽  
pp. 321-327 ◽  
Author(s):  
MARCUS FREAN ◽  
MATT LILLEY ◽  
PHILLIP BOYLE
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Neural Networks ◽  
Finite Number ◽  
Gaussian Process ◽  
Recurrent Neural Network ◽  
Hebbian Learning ◽  
Stable State ◽  
Gaussian Process Regression ◽  
Hidden Neurons

Gaussian processes compare favourably with backpropagation neural networks as a tool for regression, and Bayesian neural networks have Gaussian process behaviour when the number of hidden neurons tends to infinity. We describe a simple recurrent neural network with connection weights trained by one-shot Hebbian learning. This network amounts to a dynamical system which relaxes to a stable state in which it generates predictions identical to those of Gaussian process regression. In effect an infinite number of hidden units in a feed-forward architecture can be replaced by a merely finite number, together with recurrent connections.

Deep neural network based on generalized neo-fuzzy neurons and its learning based on backpropagation

2021 ◽  
Vol 26 (jai2021.26(1)) ◽  
pp. 32-41
Author(s):  
Bodyanskiy Y ◽  
◽  
Antonenko T ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Process ◽  
Activation Function ◽  
Point Of View ◽  
Basic Unit ◽  
Theoretical Point ◽  
Activation Functions ◽  
Approximation Properties ◽  
Network Training

Modern approaches in deep neural networks have a number of issues related to the learning process and computational costs. This article considers the architecture grounded on an alternative approach to the basic unit of the neural network. This approach achieves optimization in the calculations and gives rise to an alternative way to solve the problems of the vanishing and exploding gradient. The main issue of the article is the usage of the deep stacked neo-fuzzy system, which uses a generalized neo-fuzzy neuron to optimize the learning process. This approach is non-standard from a theoretical point of view, so the paper presents the necessary mathematical calculations and describes all the intricacies of using this architecture from a practical point of view. From a theoretical point, the network learning process is fully disclosed. Derived all necessary calculations for the use of the backpropagation algorithm for network training. A feature of the network is the rapid calculation of the derivative for the activation functions of neurons. This is achieved through the use of fuzzy membership functions. The paper shows that the derivative of such function is a constant, and this is a reason for the statement of increasing in the optimization rate in comparison with neural networks which use neurons with more common activation functions (ReLU, sigmoid). The paper highlights the main points that can be improved in further theoretical developments on this topic. In general, these issues are related to the calculation of the activation function. The proposed methods cope with these points and allow approximation using the network, but the authors already have theoretical justifications for improving the speed and approximation properties of the network. The results of the comparison of the proposed network with standard neural network architectures are shown

Multiscale Topology Optimization With Gaussian Process Regression Models

2021 ◽  
Author(s):  
Joel C. Najmon ◽  
Homero Valladares ◽  
Andres Tovar
Keyword(s):  
Topology Optimization ◽  
Gaussian Process ◽  
Regression Models ◽  
Computational Cost ◽  
Gaussian Process Regression ◽  
Analytical Sensitivity ◽  
Inverse Homogenization ◽  
Discrete Location ◽  
Unit Cells ◽  
Periodic Microstructures

Abstract Multiscale topology optimization (MSTO) is a numerical design approach to optimally distribute material within coupled design domains at multiple length scales. Due to the substantial computational cost of performing topology optimization at multiple scales, MSTO methods often feature subroutines such as homogenization of parameterized unit cells and inverse homogenization of periodic microstructures. Parameterized unit cells are of great practical use, but limit the design to a pre-selected cell shape. On the other hand, inverse homogenization provide a physical representation of an optimal periodic microstructure at every discrete location, but do not necessarily embody a manufacturable structure. To address these limitations, this paper introduces a Gaussian process regression model-assisted MSTO method that features the optimal distribution of material at the macroscale and topology optimization of a manufacturable microscale structure. In the proposed approach, a macroscale optimization problem is solved using a gradient-based optimizer The design variables are defined as the homogenized stiffness tensors of the microscale topologies. As such, analytical sensitivity is not possible so the sensitivity coefficients are approximated using finite differences after each microscale topology is optimized. The computational cost of optimizing each microstructure is dramatically reduced by using Gaussian process regression models to approximate the homogenized stiffness tensor. The capability of the proposed MSTO method is demonstrated with two three-dimensional numerical examples. The correlation of the Gaussian process regression models are presented along with the final multiscale topologies for the two examples: a cantilever beam and a 3-point bending beam.

Gaussian Process Regression Models for the Prediction of Hydrogen Bond Acceptor Strengths

2018 ◽  
Vol 38 (4) ◽  
pp. 1800115 ◽  
Author(s):  
Christoph A. Bauer ◽  
Gisbert Schneider ◽  
Andreas H. Göller
Keyword(s):  
Hydrogen Bond ◽  
Gaussian Process ◽  
Regression Models ◽  
Gaussian Process Regression ◽  
Hydrogen Bond Acceptor

Anomaly Detection in Video Surveillance via Gaussian Process

2015 ◽  
Vol 29 (06) ◽  
pp. 1555011 ◽  
Author(s):  
Nannan Li ◽  
Xinyu Wu ◽  
Huiwen Guo ◽  
Dan Xu ◽  
Yongsheng Ou ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Regression Model ◽  
Gaussian Process ◽  
Video Surveillance ◽  
Gaussian Process Regression ◽  
Bayesian Regression ◽  
Current Frame ◽  
Motion Patterns ◽  
Online Clustering ◽  
Efficient Calculation

In this paper, we propose a new approach for anomaly detection in video surveillance. This approach is based on a nonparametric Bayesian regression model built upon Gaussian process priors. It establishes a set of basic vectors describing motion patterns from low-level features via online clustering, and then constructs a Gaussian process regression model to approximate the distribution of motion patterns in kernel space. We analyze different anomaly measure criterions derived from Gaussian process regression model and compare their performances. To reduce false detections caused by crowd occlusion, we utilize supplement information from previous frames to assist in anomaly detection for current frame. In addition, we address the problem of hyperparameter tuning and discuss the method of efficient calculation to reduce computation overhead. The approach is verified on published anomaly detection datasets and compared with other existing methods. The experiment results demonstrate that it can detect various anomalies efficiently and accurately.

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