Intelligent Arabic letters speech recognition system based on mel frequency cepstral coefficients

Speech recognition is one of the important applications of artificial intelligence (AI). Speech recognition aims to recognize spoken words regardless of who is speaking to them. The process of voice recognition involves extracting meaningful features from spoken words and then classifying these features into their classes. This paper presents a neural network classification system for Arabic letters. The paper will study the effect of changing the multi-layer perceptron (MLP) artificial neural network (ANN) properties to obtain an optimized performance. The proposed system consists of two main stages; first, the recorded spoken letters are transformed from the time domain into the frequency domain using fast Fourier transform (FFT), and features are extracted using mel frequency cepstral coefficients (MFCC). Second, the extracted features are then classified using the MLP ANN with back-propagation (BP) learning algorithm. The obtained results show that the proposed system along with the extracted features can classify Arabic spoken letters using two neural network hidden layers with an accuracy of around 86%

An Optimized Recursive General Regression Neural Network Oracle for the Prediction and Diagnosis of Diabetes

Diabetes is a serious, chronic disease that has been seeing a rise in the number of cases and prevalence over the past few decades. It can lead to serious complications and can increase the overall risk of dying prematurely. Data-oriented prediction models have become effective tools that help medical decision-making and diagnoses in which the use of machine learning in medicine has increased substantially. This research introduces the Recursive General Regression Neural Network Oracle (R-GRNN Oracle) and is applied on the Pima Indians Diabetes dataset for the prediction and diagnosis of diabetes. The R-GRNN Oracle (Bani-Hani, 2017) is an enhancement to the GRNN Oracle developed by Masters et al. in 1998, in which the recursive model is created of two oracles: one within the other. Several classifiers, along with the R-GRNN Oracle and the GRNN Oracle, are applied to the dataset, they are: Support Vector Machine (SVM), Multilayer Perceptron (MLP), Probabilistic Neural Network (PNN), Gaussian NaEF;ve Bayes (GNB), K-Nearest Neighbor (KNN), and Random Forest (RF). Genetic Algorithm (GA) was used for feature selection as well as the hyperparameter optimization of SVM and MLP, and Grid Search (GS) was used to optimize the hyperparameters of KNN and RF. The performance metrics accuracy, AUC, sensitivity, and specificity were recorded for each classifier. The R-GRNN Oracle was able to achieve the highest accuracy, AUC, and sensitivity (81.14%, 86.03%, and 63.80%, respectively), while the optimized MLP had the highest specificity (89.71%)