This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+) for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction) is forecasted and the pattern mode estimated using a Nearest Neighbor (NN) ...

In this paper, we propose a spectral-spatial feature extraction and classification framework based on artificial neuron network (ANN) in the context of hyperspectral imagery. With limited labeled samples, only spectral information is exploited for training and spatial context is integrated posteriorly at the testing stage. Taking advantage of recent advances in face recognition, a joint supervi...

Speech is the most natural mode of communication. This work emphasizes on recognizing different emotions from speech signal. There are two major sections in this project namely feature extraction from speech signal and give this features as input to classifier to recognize emotions. Emotional states of speaker are considered as namely angry, happy, sad and neutral. The testing section classifie...

Maximum likelihood and neural classifiers are two typical techniques in image classification. This paper investigates how to adapt these approaches to hyperspectral imaging for the classification of five kinds of Chinese tea samples, using visible light hyperspectral spectroscopy rather than near-infrared. After removal of unnecessary parts from each imaged tea sample using a morphological crop...

Automatic inspection of petroleum well drilling has became paramount in the last years, mainly because of the crucial importance of saving time and operations during the drilling process in order to avoid some problems, such as the collapse of the well borehole walls. In this paper, we extended another work by proposing a fast petroleum well drilling monitoring through a modified version of the...

In this paper, a novel approach to analyse, extract and validate the key characteristics of the fault transient phenomena on a thyristor controlled series capacitor (TCSC) compensated transmission line is introduced. This method relies on utilising discrete wavelet transform (DWT) to decompose the traditional busbar voltages and line currents obtained from a single terminal into a series of tim...

Deep understanding of driving encounters could help self-driving cars make appropriate decisions when driving in complex settings with surrounding vehicles engaged. This paper develops an unsupervised classifier to group naturalistic driving encounters into several distinguishable clusters by combining an auto-encoder with a k-means clustering (AE-kMC). In order to show the effectiveness of our...

Huge amount of medical data is available today. In order to predict the disease we need a reliable method to diagnose the disease. In this paper we introduce a technique known as FFBAT-ANN prediction algorithm which is categorized as Feature reduction and Diabetes disease classification and such a process is carried out using LPP algorithm and FFBATartificial neural network classifier respectiv...

A novel automated metkiod is applied to Electroencephalogram (EEG) data to detect seizure events in newborns. The detection.scheme is based on observing the changing behavior of the wavelet coefficients (WCs) of the EEG signal:at different scales. An optimizing technique based on mutual information feature selection (MIFS) is employed. This technique evaluates a set of candidate features extrac...

Gray level Co occurrence matrix (GLCM) texture analysis has been aggressively researched for decade for multiple applications. Co occurrence matrix retains the spatial and frequency information of the image while compresses the image into a fraction of size enabling the application of classifier engines for analysis. Haralick features are secondary features derived from GLCM. There have been co...