Neural Network Model of Estimation of Body Mass Index Based on Indirect Input Factors

A well-prepared One of the main concerns of people in developing and developed societies is increasing the Body Mass Index (BMI) level. BMI, in fact can be considered as an indicator of overall health condition. Genetic aspects aside, the BMI level is affected by different factors, such as socio-economic, environmental, and physical activity level. This study investigated the effect of different factors on the BMI level of a sample population of 470 adults of three residential neighborhoods in Shiraz, Iran. The Pearson correlation coefficient, independent sample T-test and One Way ANOVA were used to extract the variables which significantly influenced the BMI. The statistical analysis showed that despite the apparent association of BMI with physical activity level, it is influenced by several factors such as age, residence record, number of children, distance to bus or taxi stop, indoor or sport exercise. Then, an Artificial Neural Network (ANN) was applied to predict the level of personal BMI. Artificial Neural Network-based methodology results showed that the generalized estimating ANN model was satisfactory in estimating the BMI based on the introduced pattern

Assessing hyper parameter optimization and speedup for convolutional neural networks

The increased processing power of graphical processing units (GPUs) and the availability of large image datasets has fostered a renewed interest in extracting semantic information from images. Promising results for complex image categorization problems have been achieved using deep learning, with neural networks comprised of many layers. Convolutional neural networks (CNN) are one such architecture which provides more opportunities for image classification. Advances in CNN enable the development of training models using large labelled image datasets, but the hyper parameters need to be specified, which is challenging and complex due to the large number of parameters. A substantial amount of computational power and processing time is required to determine the optimal hyper parameters to define a model yielding good results. This article provides a survey of the hyper parameter search and optimization methods for CNN architectures

Classification improvement of spoken arabic language based on radial basis function

The important task in the computer interaction is the languages recognition and classification. In the Arab world, there is a persistent need for the Arabic spoken language recognition To help those who have lost the upper parties in doing what they want through speech computer interaction. While, the Arabic automatic speech recognition (AASR) did not receive the desired attention from the researchers. In this paper, the Radial Basis Function(RBF) is used for the improvement of the Arabic spoken language letter. The recognition and classification process are based on three steps; these are; preprocessing, feature extraction and classification (Recognition). The Arabic  Language Letters  (ALL) recognition is done by using the combination between the statistical features and the Temporal Radial Basis Function for different letter situation and noisy condition. The recognition percent are from 90% - 99.375% has been gained with independent speaker, where these results are over-perform the earlier works by nearly 2.045%. The simulati.on has been made by using Matlab 2015b

Neural Network Model of Estimation of Body Mass Index Based on Indirect Input Factors

A well-prepared One of the main concerns of people in developing and developed societies is increasing the Body Mass Index (BMI) level. BMI, in fact can be considered as an indicator of overall health condition. Genetic aspects aside, the BMI level is affected by different factors, such as socio-economic, environmental, and physical activity level. This study investigated the effect of different factors on the BMI level of a sample population of 470 adults of three residential neighborhoods in Shiraz, Iran. The Pearson correlation coefficient, independent sample T-test and One Way ANOVA were used to extract the variables which significantly influenced the BMI. The statistical analysis showed that despite the apparent association of BMI with physical activity level, it is influenced by several factors such as age, residence record, number of children, distance to bus or taxi stop, indoor or sport exercise. Then, an Artificial Neural Network (ANN) was applied to predict the level of personal BMI. Artificial Neural Network-based methodology results showed that the generalized estimating ANN model was satisfactory in estimating the BMI based on the introduced pattern

A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer

Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students' outcomes. This proposed system would improve instruction by the faculty and enhance the students' learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models.Comment: 34 pages, published in PLoS ON

Detection of Abnormalities based on Gamma Wave EEG Signal for Autism Spectrum Disorder

Diagnosing Autism Spectrum Disorder (ASD) by using the traits of abnormalities in their gamma waveform has been proposed in this study to suggest an objective method to detect the disorder using Electroencephalography (EEG) signal. Gamma waveform plays an important role in learning, memory and information processing where it shows slower activities in ASD person compared to a normal person, thus, causing the patients to have trouble in processing knowledge, communicate and pay attention. This study applies Probabilistic Neural Network (PNN) and General Regression Neural Network (GRNN) to classify the data into normal and abnormal classes. Classification algorithm by PNN was used as a benchmark for the outcomes. The results show that even though PNN and GRNN have similar architecture, but with fundamental difference, the outcomes are different. In this case, PNN performs better than GRNN. To obtain the desired results, we used three and four statistical features (mean, minimum, maximum and standard deviation) for both methods. The outcomes of using PNN with four features are more accurate (99.5% for normal class and 80.5% for abnormal class) compared to only three features. Furthermore, the outcomes of using GRNN with four features also have improvement (95% for normal class and 63.5% for abnormal class) compared to only three features

Stunting Classification in Children's Measurement Data Using Machine Learning Models

The study conducted a stunting classification of measurement data for children under 5 years old. The dataset has attributes such as: gender, age, weight (BB), height (TB), weight / height (BBTB), weight / age (BBU), and height / age (TBU). The research uses the CRISP-DM methodology in processing the data. The data were tested on several classification models, namely: logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (KNN), classification and regression trees (CART), nave bayes (NB), support vector machine - linear kernel (SVM-Linear), support vector machine - rbf kernel (SVM-RBF), random forest classifier (RPC), adaboost (ADA), and neural network (MLPC). These models were tested on the dataset to find out the best model in accuracy. The test results show that SVM-RBF produces an accuracy of 78%. SVM-RBF has consistently been at the highest accuracy in several tests. Testing through k-fold cross validation with k=10