Human recognizes speech emotions by extracting features from the speech signals received through the cochlea and later passed the information for processing. In this paper we propose the use of Mel-Frequency Cepstral Coefficient (MFCC) to extract the speech emotion information to provide both the frequency and time domain information for analysis. Since features extracted using the MFCC simulat...

The main goal in this research is to find out possible ways to built hybrid systems, based on neural network (NN) and hidden Markov (HMM) models, for the task of automatic speech recognition. The investigation that has been conducted covers different types of neural network and hidden Markov models, and the combination of them into some hybrid models. The neural networks used were basically MLP...

This paper proposes a novel approach for an optimal multi-objective optimization for VLSI implementation of Artificial Neural Network (ANN) which is area-power-speed efficient and has high degree of accuracy and dynamic range. A VLSI implementation of feed forward neural network in floating point arithmetic IEEE-754 single precision 32 bit format is presented that makes the use of digital weigh...

Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall prediction. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multilayer perceptron neural networks (MLP-NN). In fact, ...

Classifier fusion strategies have shown great potential to enhance the performance of pattern recognition systems. There is an agreement among researchers in classifier combination that the major factor for producing better accuracy is the diversity in the classifier team. Re-sampling based approaches like bagging, boosting and random subspace generate multiple models by training a single learn...

A data-driven approach for modeling indoor-air-quality (IAQ) sensors used in heating, ventilation, and air conditioning (HVAC) systems is presented. The IAQ sensors considered in the paper measure three basic parameters, temperature, CO2, and relative humidity. Three models predicting values of IAQ parameters are built with various data mining algorithms. Four data mining algorithms have been t...

This work presents a Machine Learning (ML) approach for classifying areas of brain tissue in a stack of high resolution human brain slice images. Compared with standard image segmentations algorithms, this ML approach provides more reliable results by concentrating on pixel classification. The presented ML approach is fourfold. First, four feature extraction methods were developed to extract fe...

In place, sustainable rehabilitation of existing deteriorated concrete highways through rubblization is considered to be a green and economical alternative to other options involving total reconstruction, etc. There is currently no standardised method of backcalculating the rubblized pavement layer moduli from pavement non-destructive test data through inverse analysis. This paper explores the ...

This research paper proposes an intelligent classification technique to identify normal and abnormal slices of brain MRI data. The manual interpretation of tumor slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed which caters the n...