Faculty Publications
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Item Performance evaluation of machine learning techniques for customer churn prediction in telecommunication sector(IGI Global, 2021) Majhi, B.; Rajput, S.S.; Majhi, R.The principle objective of this chapter is to build up a churn prediction model which helps telecom administrators to foresee clients who are no doubt liable to agitate. Many studies affirmed that AI innovation is profoundly effective to anticipate this circumstance as it is applied through training from past information. The prediction procedure is involved three primary stages: normalization of the data, then feature selection based on information gain, and finally, classification utilizing different AI methods, for example, back propagation neural network (BPNNM), naïve Bayesian, k-nearest neighborhood (KNN), support vector machine (SVM), discriminant analysis (DA), decision tree (DT), and extreme learning machine (ELM). It is shown from simulation study that out of these seven methods SVM with polynomial based kernel is coming about 91.33% of precision where ELM is at the primary situation with 92.10% of exactness and MLANN-based CCP model is at third rank with 90.4% of accuracy. Similar observation is noted for 10-fold cross validation also. © 2021, IGI Global.Item Parallel scale space construction using SIMD hypercube(2012) Panda, A.C.; Mehrotra, H.; Majhi, B.This paper proposes parallel scale space construction of Scale Invariant Feature Transform (SIFT) using SIMD hypercube. The parallel SIFT approach is used for iris feature extraction. The input iris images and Gaussian filters are mapped to each processor in the hypercube and convolution takes place in each processor concurrently. The time complexity of parallel algorithm is O(N 2) whereas sequential algorithm performs with complexity of O(lsN2), where l is the number of octaves, s is the number of Gaussian scale levels within an octave for N2 sized iris image. © 2012 IEEE.Item Neighbor embedding based super-resolution using residual luminance(Institute of Electrical and Electronics Engineers Inc., 2015) Mishra, D.; Majhi, B.; Sa, P.K.Resolution plays a crucial role for study of information in an image. Therefore to enhance the resolution of an image, there are so many techniques have been proposed with respect to the reference images. In this paper, we proposed a new scheme for single image super-resolution based on the neighbor embedding method. Many feature selection methods have been proposed for the learning based super-resolution using manifold learning. Here a new feature selection has been proposed by combining first-order gradient and residual of the luminance component, inspired by Gaussian pyramid. In this Neighbor Embedding based Super-Resolution using the Residual Luminance (NESRRL) method the high resolution targeted image is estimated by the training image pairs. This approach imposes the local compatibility and smoothness constraints between patches in the estimated high resolution image. The experimental results show the comparisons of qualitative performance of proposed method with different existing methods using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). © 2014 IEEE.Item Estimating the impact of news on Indian government decisions to contain the spread of COVID-19 in India(American Institute of Physics Inc., 2023) Bhatnagar, V.; Majhi, B.; Majhi, R.Corona-Virus or COVID-19 has adversely affected human life. A large number of human deaths and infected cases were recorded. India has also severely affected and recorded many deaths and infected cases. Understanding the criticality of the situation, the Indian government had taken decisions on lockdown and unlocking of the state to stop spreading of this deadly virus. It is evident from the information theory that more pieces of information eventually help a decision maker to make sound and well-informed decisions. News is considered as an important source of information around the world. This study is an attempt towards finding the impact of news on decisions taken by the Indian government. Sentiment analysis and Chi-square testing methods have been employed in the present investigation. Results obtained from the analysis are in line with the people opinion and the sentiments expressed by news articles towards the decisions made by the Indian government. © 2023 Author(s).Item Development and performance evaluation of a novel knowledge guided artificial neural network (KGANN) model for exchange rate prediction(King Saud bin Abdulaziz University rectoroffice@ksu.edu.sa, 2015) Jena, P.R.; Majhi, R.; Majhi, B.This paper presents a new adaptive forecasting model using a knowledge guided artificial neural network (KGANN) structure for efficient prediction of exchange rate. The new structure has two parallel systems. The first system is a least mean square (LMS) trained adaptive linear combiner, whereas the second system employs an adaptive FLANN model to supplement the knowledge base with an objective to improve its performance value. The output of a trained LMS model is added to an adaptive FLANN model to provide a more accurate exchange rate compared to that predicted by either a simple LMS or a FLANN model. This finding has been demonstrated through an exhausting computer simulation study and using real life data. Thus the proposed KGANN is an efficient forecasting model for exchange rate prediction. © 2015 The Authors.Item Impact of COVID-19 on GDP of major economies: Application of the artificial neural network forecaster(Elsevier B.V., 2021) Jena, P.R.; Majhi, R.; Kalli, R.; Managi, S.; Majhi, B.The ongoing COVID-19 pandemic has caused global health impacts, and governments have restricted movements to a certain extent. Such restrictions have led to disruptions in economic activities. In this paper, the GDP figures for the April–June quarter of 2020 for eight countries, namely, the United States, Mexico, Germany, Italy, Spain, France, India, and Japan, are forecasted. Considering that artificial neural network models have higher forecasting accuracy than statistical methods, a multilayer artificial neural network model is developed in this paper. This model splits the dataset into two parts: the first with 80% of the observations and the second with 20%. The model then uses the first part to optimize the forecasting accuracy and then applies the optimized parameters to the second part of the dataset to assess the model performance. A forecasting error of less than 2% is achieved by the model during the testing procedure. The forecasted GDP figures show that the April–June quarter of the current year experienced sharp declines in GDP for all countries. Moreover, the annualized GDP growth is expected to reach double-digit negative growth rates. Such alarming prospects require urgent rescue actions by governments. © 2020 Economic Society of Australia, QueenslandItem Forecasting the CO2 emissions at the global level: A multilayer artificial neural network modelling(MDPI, 2021) Jena, P.R.; Managi, S.; Majhi, B.Better accuracy in short?term forecasting is required for intermediate planning for the national target to reduce CO2 emissions. High stake climate change conventions need accurate predictions of the future emission growth path of the participating countries to make informed decisions. The current study forecasts the CO2 emissions of the 17 key emitting countries. Unlike previous studies where linear statistical modeling is used to forecast the emissions, we develop a multilayer artificial neural network model to forecast the emissions. This model is a dynamic nonlinear model that helps to obtain optimal weights for the predictors with a high level of prediction accuracy. The model uses the gross domestic product (GDP), urban population ratio, and trade openness, as predictors for CO2 emissions. We observe an average of 96% prediction accuracy among the 17 countries which is much higher than the accuracy of the previous models. Using the optimal weights and available input data the forecasting of CO2 emissions is undertaken. The results show that high emitting countries, such as China, India, Iran, Indonesia, and Saudi Arabia are expected to increase their emissions in the near future. Currently, low emitting countries, such as Brazil, South Africa, Turkey, and South Korea will also tread on a high emission growth path. On the other hand, the USA, Japan, UK, France, Italy, Australia, and Canada will continuously reduce their emissions. These findings will help the countries to engage in climate mitigation and adaptation negotiations. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Item Estimating Long-Run Relationship between Renewable Energy Use and CO2 Emissions: A Radial Basis Function Neural Network (RBFNN) Approach(MDPI, 2022) Jena, P.R.; Majhi, B.; Majhi, R.The long-run relationship between economic growth and environmental quality has been estimated within the framework of the environmental Kuznets Curve (EKC). Several studies have estimated this relationship by using statistical models such as panel regression and time series regression. The current study argues that there is a nonlinear relationship between environmental quality indicators and economic and non-economic predictors and hence an appropriate nonlinear model is required to predict it. An adaptive and nonlinear model, namely radial basis function neural network (RBFNN) has been developed in this study. CO2 emission is used as the target output and renewable energy consumption share, real GDP, trade openness, urban population ratio, and democracy index are used as the predictors to estimate the EKC relationship for nineteen major CO2 emitting countries that account for 78% of the global emissions. The model developed in this study could predict the CO2 emissions of all the countries with more than 95% accuracy. This finding underlines the usefulness of the RBFNN model which can be used to predict emission levels of other pollution indicators at the global level. Further, comparing two models, one with all the predictors and the other excluding the renewable energy share, it was found that the model with renewable energy share predicts CO2 emissions more accurately. This reinforces the already strengthening campaign to encourage industries and governments to increase the share of renewable energy in total energy use. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Item Prediction of crop yield using climate variables in the south-western province of India: a functional artificial neural network modeling (FLANN) approach(Springer Science and Business Media B.V., 2023) Jena, P.R.; Majhi, B.; Kalli, R.; Majhi, R.To meet the demand of the growing population, there exists pressure on food production. In this context, appropriate prediction of crop yield helps in agricultural production planning. Given the inability of the traditional linear models to provide satisfactory prediction performance, there is a need to develop a crop yield prediction model that is simple in complexity, accurate in prediction, and less time-consuming during training and validation phases. Keeping these objectives in view, the present paper focuses on building an adaptive, low complexity, and accurate nonlinear model for the prediction of crop yield. A time series dataset for the period 1991–2012 of Karnataka, a southwestern state of India, is used for yield prediction. An empirical nonlinear relation between crop yield and the four independent attributes has been obtained from the proposed ANN model. The independent attributes employed are total rainfall, the cumulative distribution of temperature, the proportion of irrigated land, and the average amount of fertilizer used. It is demonstrated that the developed model exhibits better prediction accuracy, less root mean square error in the range of 0.07–0.14, less mean square error in the range of 0.01–0.04, and mean absolute error in the range of 0.07–0.15 compared to its corresponding linear regression model. It is recommended that the proposed ANN model can also be applied to predict other agricultural products of the same or other geographical regions of the globe. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.