Effect of Learning Rate on the Recognition of Images

This paper presents a study for the effect of learning rate on an approach for texture classification and detection based on the neural network principle. This neural network consists of three layers, which are input, output, and hidden layers. The back propagation technique is considered. A computer algorithm is deduced and applied. In this work, the synthetic textures are generated. The results are taken for the modern computer of AT 486 type. The mathematical analysis is summarized in order to illustrate the effect of learning rate parameter on the exact discrimination during processing. This effect is studied through applications. The minimum consumed time for the computational time of classification in industry is correlated to correspond only the use of only 2 units in the hidden layer of a neural network for real images instead of 11 units

ICA and Sparse ICA for Biomedical Signals

Biomedical signs or bio signals are a wide range of signals obtained from the human body that can be at the cell organ or sub-atomic level Electromyogram refers to electrical activity from muscle sound signals electroencephalogram refers to electrical activity from the encephalon electrocardiogram refers to electrical activity from the heart electroretinogram refers to electrical activity from the eye and so on Monitoring and observing changes in these signals assist physicians whose work is related to this branch of medicine in covering predicting and curing various diseases It can also assist physicians in examining prognosticating and curing numerous condition

Variables Affecting the Mothers Access to Quality Care during Childbirth using the Neural Networks and Logistic Regression Models

Quality pregnancy and birth care is crucial in reducing maternal and child mortality in Egypt, requiring both supply and demand interventions. Using data from the Egypt Demographic Health Survey 2014, a neural networks and logistic regression models were built to determine demographic, social, and economic determinants affecting mothers access to care during childbirth. The study found that mothers working status had a significant impact on access to care, with an inverse relationship. Logistic regression outperformed neural networks in analyzing the relationship between explanatory variables and mothers access to care during childbirth

Forecasting Remission Time of a Treatment Method for Leukemia as an Application to Statistical Inference Approach

In this paper, Weibull-Linear Exponential distribution (WLED) has been investigated whether being it is a well - fit distribution to a clinical real data. These data represent the duration of remission achieved by a certain drug used in the treatment of leukemia for a group of patients. The statistical inference approach is used to estimate the parameters of the WLED through the set of the fitted data. The estimated parameters are utilized to evaluate the survival and hazard functions and hence assessing the treatment method through forecasting the duration of remission times of patients. A two-sample prediction approach has been applied to obtain a predictive sample based on the Bayes estimates of the parameters. The statistical inference approach is applied to the case of censored data namely Type-II hybrid censoring scheme, which is common in clinical studies

Forecasting the Climate Change through the Distributions of Solar Radiation and Maximum Temperature

The climate change crisis is negatively affecting the world and is the focus of many researchers attention for its life-threatening economic and climate impact on Earth. Therefore, this study aims to estimate the joint distribution function (EFXY) of both daily solar radiation (S) and daily maximum temperature (T) along with the Markov property. In this study, three-parameter distributions have been utilized with S and T, which are generalized extreme value (GEV) and Weibull (W-3P), respectively. Each of these parameters and the joint distribution function ((, )) have been estimated. Four real data of S and T in Queensland, Australia during two consecutive years are applied. The method of maximum likelihood estimation (MLE) is applied on the proposed distributions of S and T to estimate their parameters, which was validated using Goodness-of-Fit tests. In addition, the logarithmic (LFXY) model and the multi-regression model (MFXY) for (, ) are obtained. The results have been compared and the EFXY and LFXY are found to be non-equivalently, while the EFXY and MFXY are equivalent and homogeneous, confirming the validity of the joint distribution function estimate with the least error. Thus, the climate change probabilities are more accurately predictable by knowing both X and Y or by knowing both () and () with minimal error