Sugarcane plays an essential role in the economy of the India. During 2018, 79.9% of total sugarcane production of India was used in the manufacture of white sugar, 11.29% was used for jaggery production, and 8.80% was used as seed and feed materials. 840.16 Mt sugarcane was exported in the year 2019. Prediction of production level is basic to effective decision-making for policymakers. The objective of this study is thus to find the suitable models of forecasting for sugarcane production. India and major sugarcane producing states, namely Andhra Pradesh, Karnataka, Maharashtra, Tamil Nadu and Uttar Pradesh were selected. Sugarcane production data from 1950 to 2015 were used for training and 2016 to 2018 was used to test the model. ARIMA method was used to model the production process. Order selection was done using AIC. RMSE, MAPE and Theils' U statistic were used to test the accuracy of the models fitted to the data. ARCH process was found for Karnataka, Tamil Nadu and Uttar Pradesh. Autocorrelation was not present in all the data series analyzed. Forecast accuracy on MAPE criteria ranged from 0.046 to 0.197 percent.

Machine learning (ML) has proved to be a prominent study field while solving complex real-world problems. The whole globe has suffered and continues suffering from Coronavirus disease 2019 (COVID-19), and its projections need to be forecasted. In this article, we propose and derive an autoregressive modeling framework based on ML and statistical methods to predict confirmed cases of COVID-19 in the South Asian Association for Regional Cooperation (SAARC) countries. Automatic forecasting models based on autoregressive integrated moving average (ARIMA) and Prophet time series structures, as well as extreme gradient boosting, generalized linear model elastic net (GLMNet), and random forest ML techniques, are introduced and applied to COVID-19 data from the SAARC countries. Different forecasting models are compared by means of selection criteria. By using evaluation metrics, the best and suitable models are selected. Results prove that the ARIMA model is found to be suitable and ideal for forecasting confirmed infected cases of COVID-19 in these countries. For the confirmed cases in Afghanistan, Bangladesh, India, Maldives, and Sri Lanka, the ARIMA model is superior to the other models. In Bhutan, the Prophet time series model is appropriate for predicting such cases. The GLMNet model is more accurate than other time-series models for Nepal and Pakistan. The random forest model is excluded from forecasting because of its poor fit.

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Background: Knowledge of students is more important as compared to their position, marks, and G.P.A. Learning for students performs a vital role as it helps them achieve what they desire in their educational profession. Semester system is based on a six-month duration, examination at the end of eachsemester. Objectives: This study attempts to find out satisfaction levels of students regarding the semester system in colleges. The study explores various factors like the role of teachers, types of courses, time duration, the medium of learning, management system, college environment, group work factors, all of which have  significant impact on  satisfaction levels of the students. Methods: BS Students were taken as target population to take the research sample. Primary data were collected with the help of questionnaire and then analyzed by using SPSS software. Results: Results show that semester system is perceived to be a most effective way of effectual learning; however, the satisfaction level of students can be enhanced by cooperative efforts of teachers and students. Conclusions and Recommendation: Although there are many factors elaborated in the study that can efficiently enhance student’s satisfaction, the teacher’s efforts and behavior are the main factors which are directly related to the student’s satisfaction.

This chapter mainly explores the significant factors influencing the motivation and productivity of female university teachers in the workplace. This chapter uses primary data and several methods in achieving the objectives. This chapter finds that all the factors considered in the study have a significant impact on the motivation and productivity of female university teachers. The analysis also reveals that almost all the factors of motivation, work environment, and productivity have a relationship with one another. More specifically, this chapter finds that female teachers get motivation and increase their productivity when they are satisfied with their basic needs, job safety, work environment gets support from their families, get self-determination and get supportive behavior from the supervisor. Therefore, this chapter calls for policymakers to take proper initiatives in increasing female university teachers' motivation and productivity by considering the above factors.