Satish Kumar Jain

Bio: Satish Kumar Jain is an academic researcher. The author has contributed to research in topics: Artificial neural network. The author has an hindex of 1, co-authored 1 publications receiving 115 citations.

An Introductory Study on Time Series Modeling and Forecasting

Abstract: Modeling and forecasting of time series data has fundamental importance in various practical domains The aim of this book is to present a concise description of some popular time series forecasting models with their salient features Three important classes of time series models, viz stochastic, neural networks and support vector machines are studied together with their inherent forecasting strengths and weaknesses The book also meticulously discusses about several basic issues related to time series analysis, such as stationarity, parsimony, overfitting, etc Our study is enriched by presenting the empirical forecasting results, conducted on six real-world time series datasets Five performance measures are used to evaluate the forecasting accuracies of different models as well as to compare the models For each of the six time series datasets, we further show the obtained forecast diagram which graphically depicts the closeness between the original and predicted observations

Comparative study of attribute selection using gain ratio and correlation based feature selection

Abstract: Feature subset selection is of great importance in the field of data mining. The high dimension data makes testing and training of general classification methods difficult. In the present paper two filters approaches namely Gain ratio and Correlation based feature selection have been used to illustrate the significance of feature subset selection for classifying Pima Indian diabetic database (PIDD). The C4.5 tree uses gain ratio to determine the splits and to select the most important features. Genetic algorithm is used as search method with Correlation based feature selection as subset evaluating mechanism. The feature subset obtained is then tested using two supervised classification method namely, Back propagation neural network and Radial basis function network. Experimental results show that the feature subsets selected by CFS filter resulted in marginal improvement for both back propagation neural network and Radial basis function network classification accuracy when compared to feature subset selected by information gain filter.

Multi-criteria ABC analysis using artificial-intelligence-based classification techniques

Abstract: ABC analysis is a popular and effective method used to classify inventory items into specific categories that can be managed and controlled separately. Conventional ABC analysis classifies inventory items three categories: A, B, or C based on annual dollar usage of an inventory item. Multi-criteria inventory classification has been proposed by a number of researchers in order to take other important criteria into consideration. These researchers have compared artificial-intelligence (AI)-based classification techniques with traditional multiple discriminant analysis (MDA). Examples of these AI-based techniques include support vector machines (SVMs), backpropagation networks (BPNs), and the k-nearest neighbor (k-NN) algorithm. To test the effectiveness of these techniques, classification results based on four benchmark techniques are compared. The results show that AI-based techniques demonstrate superior accuracy to MDA. Statistical analysis reveals that SVM enables more accurate classification than other AI-based techniques. This finding suggests the possibility of implementing AI-based techniques for multi-criteria ABC analysis in enterprise resource planning (ERP) systems.

The impact of COVID-19 on tourism sector in India

Abstract: The novel coronavirus (COVID-19), which is one of its kind of humanitarian disasters, has affected people and businesses worldwide, triggering a global economic crisis. In this aspect, the tourism sector is not being left behind. The pandemic has not only affected the foreign exchange earnings (FEE) but also affected various regional developments, job opportunities, thereby disrupting the local communities as a whole. As there has been a substantial decline in the arrivals of overseas tourists in India in 2020, the paper aims to predict foreign tourists’ arrival in India and FEE using artificial neural networks (ANN). Furthermore, we analyse the impact of COVID-19 based on four scenarios considering with and without lockdown in terms of loss and gain in FEE. Lastly, the results obtained will help policymakers make necessary strategic and operational decisions, along with maximizing the FEE.

Suitable MLP Network Activation Functions for Breast Cancer and Thyroid Disease Detection

Abstract: This paper compared various MLP activation functions for classification problems. The most well-known (Artificial Neural Network) ANN architecture is the Multilayer Perceptron (MLP) network which is widely used for solving problems related to data classifications. Selection of the activation functions in the MLP network plays an essential role on the network performance. A lot of studies have been conducted by reseachers to investigate special activation function to solve different kind of problems. Therefore, this paper intends to investigate the activation functions in MLP networks in terms of the accuracy performances. The activation functions under investigation are sigmoid, hyperbolic tangent, neuronal, logarithmic, sinusoidal and exponential. Medical diagnosis data from two case studies, thyroid disease classification and breast cancer classification, have been used to test the performance of the MLP network. The MLP networks are trained using Back Propagation learning algorithm. The performance of the MLP networks are calculated based on the percentage of correct classificition. The results show that the hyperbolic tangent function in MLP network had the capability to produce the highest accuracy for classifying breast cancer data. Meanwhile, for thyroid disease classification, neuronal function is the most suitable function that performed the highest accuracy in MLP network.