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With the evolution of the electricity market into a restructured smart version, load forecasting has emerged as an eminent research domain. Many forecasting models have been proposed by researchers for electricity price and load forecasting. This state of art introduces a load time series modeled with a hybrid technique culminating from the logical amalgamation of GARCH, a conventional hard computing method, Fuzzy ARTMAP, an artificial intelligence-based soft computing technique, and wavelet transform, for treating the load time series. The study investigates into the ability of the proposed hybrid model in tackling the electricity load time series forecasting problems. The work under this study also includes comparisons drawn among models which use either one or two of the mentioned techniques and the model proposed. Results certify the efficacy and effectiveness of the model over others. 15

Background : Ventricular Arrhythmias are one of the fatal heart diseases, requires timely recognition. The nonlinear and random nature of heart rate makes the diagnosis challenging. Introduction: The research work in this paper is divided into three phases. In first phase classification of some of the ventricular arrhythmias is done in four classes as Premature Ventricular Contraction (PVC), Left Bundle Branch Block (LBBB) and Right Bundle Brach Block (RBBB) with some Normal (N) samples and the analysis of classifying algorithms to improve the classifiers accuracy. A Support Vector Machine (SVM), Random Forest, Artificial Neural Network (ANN) and K Nearest Neighbor (KNN) algorithms were used to train and test the classifier, with the help of online available MIT-BIH Arrhythmia Database. Then, in the second phase, the variance analysis of the data has been carried out using Principle Component Analysis (PCA) to improve the classifier performance. In last phase the whole process was repeated after including Quadratic features with best performing classifier only. Method: Signal processing, generation of Heart Rate Variability (HRV) signals from the available Electrocardiogram (ECG) signals and training, testing of ANN classifier was done in MATLAB environment, and the training and testing of SVM, and Random Forest classifier was done in R project software. Result: Random Forest shows the best result among all classifiers with 86.11% accuracy, 87.1% after applying PCA with top 16 features, and 91.4% after including quadratic features with top 28 features. Conclusion: The present study envisages helping ECG and HRV data analyses while selecting the AI techniques for classification purpose according to data.