Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Climatic effects on sugarcane productivity in India: A stochastic production function application
    (Inderscience Publishers, 2015) Singh, A.; Sharma, P.; Ambrammal, S.K.
    The present study estimates the influence of climatic and non-climatic factors on mean yield and yield variability of sugarcane crop in different weather seasons (e.g., rainy, winter and summer) in India. Sugarcane mean-yield for fourteen major sugarcane growing states from different agro-ecological zones are delimitated in panel data during 1971-2009. Regression coefficient for mean yield and yield variability production function (i.e. risk increasing or decreasing inputs) has been estimated through log-linear regression model with the help of Just and Pope (stochastic) production function specification. Empirical results based on feasible generalise least square (FGLS) estimations shows a significant effect of rainfall, maximum and minimum temperatures on sugarcane mean yield and yield variability. Whereas, average maximum temperature in summer and average minimum temperature in rainy season have a negative and statistically significant impact on sugarcane mean yield. Sugarcane mean yield positively gets affected with average maximum temperature during rainy and winter season. © © 2015 Inderscience Enterprises Ltd.
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    Energy efficient quality of service aware virtual machine migration in cloud computing
    (Institute of Electrical and Electronics Engineers Inc., 2018) Sharma, N.; Sharma, P.; Guddeti, R.M.
    This paper deals with mulit-objective (network aware, energy efficient, and Service Level Agreement (SLA) aware) Virtual Machines (VMs) migration at the cloud data center. The proposed VMs migration technique migrate the VMs from the underutilized PMs to the energy efficient Physical Machines (PMs) at the cloud data center. Further, the multi-objective VMs migration technique not only reduces the power consumption of PMs and switches but also guarantees the quality of service by maintaining the SLA at the cloud data center. Our proposed VMs migration approach can find the good balance between three conflict objectives as compared to other algorithms. Further, the cloudsim based experimental results demonstrate the superiority of our proposed multi-objective VMs migration technique in terms of energy efficiency and also reduces the SLA violation over state-of-the-art VMs migration techniques such as Interquartile Range (IQR), and Random VMs migration techniques at the cloud data center. © 2018 IEEE.
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    Machine Learning Solutions for Predicting Bankruptcy in Indian Firms
    (Springer Science and Business Media Deutschland GmbH, 2025) Chaithra; Sharma, P.; Mohan, R.
    The growing demand to identify potential bankrupt companies has prompted more research into bankruptcy prediction, assisting stakeholders in determining the worthiness of an investment. The Indian stock market offers investment opportunities, but it also involves risk. As a result, it is critical to invest in fundamentally sound companies for long-term investment. To address this need, we created a machine learning-based model for identifying a healthy and distressed firm in the Indian scenario. We created a dataset consisting of 118 bankrupt and 310 healthy firms. The dataset contains three labels: bankrupt, healthy, and financial distress. The addition of the financial distress category improves our ability to recognize and identify firms that are more likely to declare bankruptcy. Recognizing the shortcomings of limited data in the Indian scenario in previous research, our study aimed to include more data instances for training. The dataset included widely recognized financial ratios and macroeconomic data that recognize the interconnectedness of broader economic trends with the company’s financial health. Advanced machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Categorical Boosting (CatBoost), Gradient Boost (GB), and K-Nearest Neighbors (KNN) were applied. The XGBoost and LGBM demonstrated the highest level of classification accuracy and also performed well on real-world data, demonstrating their potential use in supporting investors with decision-making processes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.