Faculty Publications

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    Natural Capital and Climate Smart Agriculture: Measuring Progress towards Sustainability and Policy Making in India
    (Taylor and Francis, 2024) Jena, P.R.; Managi, S.; Majhi, R.
    India is the fastest growing and the world's third-largest economy in terms of GDP in PPP terms. Sustainable development of India will ensure the welfare of the inhabitants of this most populated country. This book assesses trends of natural capital and areas of improvement through climate resilient agricultural adaptation in India. The book looks at how the agricultural sector can become more climate resilient to ensure food security and human capital development. It also suggests a policy framework towards climate-resilient agricultural development. It outlines determinants of climate-smart agricultural practices and their impact on agricultural yield, biodiversity, and food security, and as well as outreach activities for wider collaboration from around the world. This book will interest those who are researching accounting natural capital impacts of climate-resilient agriculture and 2030 SDGs. © 2025 Pradyot Ranjan Jena, Shunsuke Managi, and Ritanjali Majhi. All rights reserved.
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    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.
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    Classifying behavioural traits of small-scale farmers: Use of a novel artificial neural network (ANN) classifier
    (Institute of Electrical and Electronics Engineers Inc., 2016) Jena, P.R.; Majhi, R.
    This paper develops and employs a novel artificial neural network (ANN) model to study farmers' behaviour towards decision making on maize production in Kenya. The paper has compared the accuracy level of ANN based model and the statistical model and found out that the ANN model has achieved higher accuracy and efficiency. The findings from the study reveal that the farmers are mostly influenced by their demographic and food security for decision making. Further to examine the relative importance of different demographic and food security characteristics, an ANOVA test is undertaken. The results found that education and food security indices are instrumental in influencing farmers' decision making. © 2016 IEEE.
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    Deep Learning for Stock Index Tracking: Bank Sector Case
    (Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Arjun, R.; Suprabha, K.R.; Majhi, R.
    The current study explores the efficacy of deep learning models in stock market prediction specific to banking sector. The secondary data of major fundamental indicators and technical variables during 2004–2019 periods of two banking indices, BSE BANKEX and NIFTY Bank of Bombay stock exchange and National stock exchange, respectively, are collected. The factors impacting market index prices were analyzed using nonlinear autoregressive neural network. Preliminary findings contradict the general random walk hypothesis theory and model improvement over previous studies. The implications from practical and theoretical perspective for stakeholders are discussed. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    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).
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    Robust Machine Learning Methods for Prediction of Childhood Anemia - A Case of the Empowered Action Group States of India
    (Institute of Electrical and Electronics Engineers Inc., 2024) Gurudatha, S.; Majhi, R.
    Anemia is a major undernutrition concern in developing countries. Anemia in early childhood leads to lower immunity and diminished cognitive development and is one of the major causes of early childhood mortality. In India, the major burden of anemia is seen in the Empowered Action Group (EAG) states. Concerted efforts are needed to reduce the burden of anemia. This study uses machine learning (ML) models to predict anemia among children aged six to fifty-nine months using data from the fifth round of the Indian Demographic and Health Survey (DHS), also known as the National Family Health Survey - 5 (NFHS - 5) in the EAG states. The dataset had 85,189 rows. The random oversampling method was used to balance the dataset as there was a class-imbalance issue. Four ML models, namely conditional inference (CI) tree, random forest (RF), extreme gradient boosting (XGB), and k-nearest neighbors (KNN), were developed for prediction. The models were compared based on metrics such as accuracy, sensitivity, specificity, precision, and F1 score. The RF model had the best overall accuracy of 64%. The RF and XGB models had the best sensitivity of 0.75 and 0.7, respectively. The CI tree model had the highest specificity of 0.59. The RF and XGB models had the best F1 scores of 0.74 and 0.72, respectively. The RF model pointed out that the mother's nutritional status is the most important factor in predicting childhood anemia. Children were more likely to be anemic if their mothers had low Body Mass Index (BMI). This study contributes to the body of literature using ML techniques to study anemia in children. © 2024 IEEE.
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    Segmenting Indian Alcohol Consumers: Analyzing Sustainable Trends Using K-Means Clustering
    (Institute of Electrical and Electronics Engineers Inc., 2024) Sukumaran, L.; Majhi, R.
    Wine has become increasingly popular in India, with projections indicating an annual growth rate of 7% through 2029. This rise in popularity is partly attributed to the perception of wine as a healthier alternative to stronger spirits such as rum and brandy. This paper employs K-Means clustering to segment Indian millennial alcohol consumers and explores whether the increase in wine consumption is mainly driven by health and sustainability considerations. The findings reveal a correlation between wine consumption and the health and sustainability attributes valued by consumers. Four distinct clusters of Indian alcohol consumers were identified. Among these, two clusters exhibit an intention-behavior gap. One cluster primarily consumes strong spirits but is actively seeking healthier options, while the other shows little interest in strong spirits yet remains open to considering wine. Thus wine presents an opportunity for strong spirit drinkers to make healthier choices and may serve as a potential entry point for non-drinkers to begin consuming alcohol. © 2024 IEEE.
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    Sustainability Performance Assessment Framework for Major Seaports in India
    (International Information and Engineering Technology Association, 2022) Narasimha, P.T.; Jena, P.R.; Majhi, R.
    In performing seaport operations, triple bottom dimensions and its related key performance indicators play a significant role in improving overall aspects of seaport sustainability. This research paper intends to examine key seaport practices that form sustainable seaport development in the Indian major seaports context from stakeholder collaboration and seaport internal sustainable management decision framework. Firstly, the key practices of sustainable seaport development were examined through a broad literature review considering sustainable seaport development and related management and stakeholder-based theories. Sustainability thematic analysis is carried out based on the identification of various dimensions and key performance indicators from various literary works. Based on the theoretical framework seaport sustainability conceptual model was developed. Semi-structured interviews were conducted with 87 seaport professionals and FAHP was performed on an input basis by 23 seaport authorities to analyze the prominence of the proposed sustainable seaport development dimensions. This study also indicated that the economic dimension is the most important, while the social dimension is the least vital dimension perceived by Indian seaport managers. This research paper will conclude with a few policy insights for seaport managers in sustainable development decisions to discover areas for improvements in maritime sustainability and enhance the seaport competitiveness © 2022 WITPress. All rights reserved.
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    Disposal of obsolete mobile phones: A review on replacement, disposal methods, in-use lifespan, reuse and recycling
    (SAGE Publications Ltd, 2023) Prabhu N, S.; Majhi, R.
    Usage/consumption of mobile phones has increased rapidly around the world. As of April 2021, there were 5.27 billion mobile phone users. Meanwhile, the generation of obsolete mobile phones/mobile phone wastes is also increased mainly due to the replacement of mobile phones. The in-use lifespan of mobile phones is correspondingly getting decreased. The inappropriate disposal of obsolete mobile phones leads to adverse consequences on the environment, human health and on metal recovery. This review article provides an insight on findings from various articles on disposal of obsolete mobile phones by users/consumers. The various aspects, such as reasons for replacement, disposal methods adopted by users/consumers, impact due to the adoption of improper disposal methods such as handing them over to the informal recycling sector and storage/hibernation after its in-use lifespan, were covered. Along with this, the study even focuses on reduce, reuse and recycle (3Rs) of sustainability. Reduce means reduction of mobile phone replacement frequency. Storage of mobile phones post-in-use lifespan is the most opted disposal method, and it is one of the significant barriers to reuse, recycling and metal recovery. When it comes to recycling, the research undertaken on the recycling of obsolete mobile phones is not as in-depth when compared to the research done on recycling of e-waste in general. This article identifies future directions for sustainable end-of-life management of obsolete mobile phones. © The Author(s) 2022.
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    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.