Pradeep Mishra
@scopus.com
Department of Statistics
JNKVV,Jabalpur,India
EDUCATION
Ph.D (Agril. Statistics)
RESEARCH, TEACHING, or OTHER INTERESTS
Agricultural and Biological Sciences, Statistics and Probability, Social Sciences, Animal Science and Zoology
Scopus Publications
Scopus Publications
Ali J. Ramadhan, S. R. Krishna Priya, V. Pavithra, Pradeep Mishra, Abhiram Dash, Mostafa Abotaleb, Hussein Alkattan, and Zainalabideen Albadran
EDP Sciences
Weather has a profound influence on crop growth, development and yield. The present study deals with the use of weather parameters for sugarcane yield forecasting. Machine learning techniques like K- Nearest Neighbors (KNN) and Random Forest model have been used for sugarcane yield forecasting. Weather parameters namely maximum temperature and minimum temperature, rainfall, relative humidity in the morning and evening, sunshine hours, evaporation along with sugarcane yield have been used as inputs variables. The performance metrics like R2, Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) have been used to select the best model for predicting the yield of the crop. Among the models, Random Forest algorithm is selected as the best fit based on the high R2 and minimum error values. The results indicate that among the weather variables, rainfall and relative humidity in the evening have significant influence on sugarcane yield.
Ali J. Ramadhan, Soumik Ray, Mostafa Abotaleb, Hussein Alkattan, Garima Tiwari, Deepa Rawat, Pradeep Mishra, Shikha Yadav, Pushpika Tiwari, Adelaja Oluwaseun Adebayo,et al.
EDP Sciences
To model and forecast complex time series data, machine learning has become a major field. This machine learning study examined Moscow rainfall data's future performance. The dataset is split into 65% training and 35% test sets to build and validate the model. We compared these deep learning models using the Root Mean Square Error (RMSE) statistic. The LSTM model outperforms the BILSTM and GRU models in this data series. These three models forecast similarly. This information could aid the creation of a complete Moscow weather forecast book. This material would benefit policymakers and scholars. We also believe this study can be used to apply machine learning to complex time series data, transcending statistical approaches.
Ali J. Ramadhan, S. R. Krishna Priya, Noor Razzaq Abbas, N. Kausalya, Shikha Yadav, Pradeep Mishra, Mostafa Abotaleb, and Hussein Alkattan
EDP Sciences
Sugarcane is the primary agricultural industry that sustains and promotes economic growth in India. In 2018, the majority of India's sugarcane production, specifically 79.9%, was allocated for the manufacturing of white sugar. A smaller portion, 11.29%, was used to produce jaggery, while 8.80% was utilized as seed and feed components. A total of 840.16 million metric tonnes of cane sugar was shipped in the year 2019. The primary objective of this research is to determine the most suitable forecasting model for predicting the monthly export price of sugarcane in India. The input consists of a time series with 240 monthly observations of the export price of sugarcane in India, spanning from January 1993 to December 2013. The SARIMA approach was employed to predict the monthly export price of sugarcane and it is concluded that the SARIMA (0, 1, 1), (0, 0, 0)12 model is the best-fitted one by the expert modeler method. As a result, the fitted model appears to be adequate. The RMSE and MAPE statistics are used to analyze the precision of the model.
Ali J. Ramadhan, S. R. Krishna Priya, N. Naranammal, S. Pavishya, K. Naveena, Soumik Ray, P. Mishra, Mostafa Abotaleb, Hussein Alkattan, and Zainalabideen Albadran
EDP Sciences
Sugarcane is the largest crop in the world in terms of production. We use sugarcane and its byproducts more and more frequently in our daily lives, which elevates it to the status of a unique crop. As a result, the assessment of sugarcane production is critical since it has a direct impact on a wide range of lives. The yield of sugarcane is predicted using ARIMA and ANN models in this study. The models are based on sugarcane yield data collected over a period of 56 years (1951-2017). Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) have been used to analyze and compare the performance of different models to obtain the best-fit model. The results show that the RMSE and MAPE values of the ANN model are lower than those of the ARIMA model and that the ANN model matches best to this data set.
Ali J. Ramadhan, Tufleuddin Biswas, Soumik Ray, S. R. Anjanawe, Deepa Rawat, Binita Kumari, Shikha Yadav, Pradeep Mishra, Mostafa Abotaleb, Hussein Alkattan,et al.
EDP Sciences
The study aimed to compare ARIMA and Holt's models for predicting coconut metrics in Kerala. The coconut data series was collected from the period 1957 to 2019. Of this, 80% of the data (from 1957 to 2007) is treated as training data, and the rest (20% from 2008 to 2019) is treated as testing data. Ideal models were selected based on lower AIC and BIC values. Their accuracy was evaluated through error estimation on testing data, revealing Holt's exponential, linear, and ARIMA (0,1,0) models as the bestfit choices for predicting coconut area, production, and productivity respectively. After using the testing data, we tried for the forecasting for 2020-2024 using these models, and the DM test confirmed their significant forecasting accuracy. This comprehensive analysis provides valuable insights into effective prediction models for coconut-related metrics, offering a foundation for informed decision-making and future projections.
Ali J. Ramadhan, S. R. Krishna Priya, R. Keerti Balambiga, Ali J. Othman, Shikha Yadav, Pradeep Mishra, Mostafa Abotaleb, Hussein Alkattan, and Zainalabideen Albadran
EDP Sciences
The present study aims to develop yield forecast models for the Sugarcane crop of the Coimbatore district in Tamilnadu using two different techniques namely Variables and Months in Discriminant function analysis. For this, the Sugarcane yield data for 57 years along with the monthly data on seven weather variables have been taken. For applying discriminant analysis, the yield data of sugarcane has been divided into two categories namely two groups and three groups. The discriminant scores from the two and three-group discriminant functions were employed as independent variables in the development of yield forecast models. The yield forecast models for both strategies were created utilizing scores and trend values as independent variables. The first 52 years of yield data (1960-2012) were used to create the model, and the last five years of data (2012-2016) were used for validation. The comparison has been made between two and three groups for both techniques. The results indicate the technique using the variable-wise method gives better results based on goodness of fit. Among the two categories in the variable-wise method, three groups performed better.
Ali J. Ramadhan, S. R. Krishna Priya, N. Naranammal, Suman, Priyanka Lal, Pradeep Mishra, Mostafa Abotaleb, and Hussein Alkattan
EDP Sciences
Agriculture is the backbone of Indian Economy. Proper forecast of food crops and cash crops are necessary for the government in policy making decisions. The present paper aims to forecast Wheat and Sugarcane yield using Random Forest Regression. For the development of Random Forest models, Yield has been taken as dependent variable and variables like Gross Cropped Area, Maximum Temperature, Minimum Temperature, Rainfall, Nitrogen, Phosphorous Oxide, Potassium Oxide, Minimum Support Price and Area under Irrigation are taken as independent variables for both Wheat and Sugarcane crop. Values of R2 for Wheat and Sugarcane is 0.995 and 0.981 which indicates that the model is a good fit and other performance measures are calculated and results are satisfactory.
Ali J. Ramadhan, S. R. Krishna Priya, N. Naranammal, Rajani Gautam, Pradeep Mishra, Soumik Ray, Mostafa Abotaleb, Hussein Alkattan, and Zainalabideen Albadran
EDP Sciences
Online shopping can be done from our convenient places like home, office, etc., and the product will be delivered to the respective places. There are many factors influencing online shopping. The purpose of this study is to develop a statistical model that is used to determine the factors that influence online shopping. In this study, using factor analysis five main factors have been obtained from 15 variables that influence online shopping. These five factors have significant effects on satisfaction of customers and accounted up to 56% of total variation. Using the factor scores as independent variables, multiple regression model has been developed for predicting customers satisfaction in online shopping. Customer satisfaction has been used as dependent variable in the regression model. The five main factors that contribute online shopping are: preference of consumers towards online shopping, the risk involved in purchasing products through online, time effectiveness in online shopping, difficulties faced during online shopping and getting products from trustworthy websites.
Pradeep Mishra, Abdullah Mohammad Ghazi Al Khatib, Shikha Yadav, Soumik Ray, Achal Lama, Binita Kumari, Divya Sharma, and Ramesh Yadav
Springer Science and Business Media LLC
Pradeep Mishra, Amel Ali Alhussan, Doaa Sami Khafaga, Priyanka Lal, Soumik Ray, Mostafa Abotaleb, Khder Alakkari, Marwa M. Eid, and El-Sayed M. El-kenawy
Springer Science and Business Media LLC
Shikha Yadav, Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib, Sushmita Ranjan, Binita Kumari, Naief Alabed Alkader, Pradeep Mishra, and Promil Kapoor
Springer Science and Business Media LLC
P. K. Sahu, Mrittika Das, Bankim Sarkar, Adarsh VS, Soumik Dey, Lakshmi Narasimhaiah, Pradeep Mishra, R. K.Tiwari, and Yashpal Singh Raghav
Springer Science and Business Media LLC
Pradeep Mishra, Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib, Binita Kuamri, Shiwani Tiwari, Aditya Pratap Singh, Shikha Yadav, Divya Sharma, and Prity Kumari
Springer Science and Business Media LLC
Muhammad Shahbaz, Alina Christina Nuta, Pradeep Mishra, and Hicham Ayad
Elsevier BV
Soumik Ray, Achal Lama, Pradeep Mishra, Tufleuddin Biswas, Soumitra Sankar Das, and Bishal Gurung
Elsevier BV
Abul Abrar Masrur Ahmed, Nadjem Bailek, Laith Abualigah, Kada Bouchouicha, Alban Kuriqi, Alireza Sharifi, Pooya Sareh, Abdullah Mohammad Ghazi Al khatib, Pradeep Mishra, Ilhami Colak,et al.
Elsevier BV
Bayan Mohamad Alshaib, Abdullah Mohammad Ghazi Al khatib, Alina Cristina Nuta, Mohamad Hamra, Pradeep Mishra, Rajani Gautam, Sarfraz Hussain, and Cristina Gabriela Zamfir
SAGE Publications
This study examines the association between fiscal sustainability indicators and Egypt’s economic growth from 1980 to 2018. Fiscal sustainability refers to a government’s ability to generate sufficient revenue to cover its costs and debt obligations in the long run without excessive borrowing or money creation. Egypt’s economic growth has slowed, raising questions about fiscal sustainability. This study aimed to analyze the dynamic relationship between fiscal sustainability indicators (government revenue, expenditure, external debt) and economic growth in Egypt. The autoregressive distributed lag (ARDL) bounds testing approach and unrestricted error correction model were applied to annual data from 1980 to 2018. A dynamic link was found between fiscal sustainability indicators and economic growth. Government expenditure and external debt significantly impacted economic expansion in the long term, while government revenue did not. Fiscal sustainability, measured by growth in total government expenses, external debt obligations, and revenue, significantly influences Egypt’s economic growth. Prudent fiscal management is crucial for sustained economic development. Policymakers should focus on controlling government spending, limiting external debt, and improving revenue generation to promote long-term economic growth in Egypt. Fiscal sustainability must balance critical investments in public services. Carefully managing fiscal deficits is key to unleashing Egypt’s economic potential. This study provides valuable insights into the connection between fiscal policy and economic growth in Egypt, informing policymakers’ decisions.
CHANDRAN S., SELVAN P., NAMITHA M. R., PRADEEP MISHRA, and KUMAR V.
India Meteorological Department
A 34-year rainfall data from 1976 to 2009 of ten sub-basins of the Vaigai River in Tamil Nadu were collected and analysed statistically using various probability distribution functions. The best-fit probability distributions for the annual, monthly and seasonal rainfall for the study area were found using two goodness-of-fit tests. The Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) methodology has been adopted for model identification, diagnostic checking and forecasting the study area's annual rainfall. The best ARIMA models were selected for each sub-basin and the average annual precipitation for 2010, 2015, 2020 and 2025 has been forecasted. The forecasted result compared well with observed dataup to 2020, which indicates the appropriateness of the model.
Pradeep Mishra, Abdullah Mohammad Ghazi Al Khatib, Priyanka Lal, Ayesha Anwar, Korakot Nganvongpanit, Mostafa Abotaleb, Soumik Ray, and Veerasak Punyapornwithaya
Springer Science and Business Media LLC
Fozia Homa, Mukti Khetan, Mohd. Arshad, and Pradeep Mishra
Apple Academic Press
P. Mishra
Agricultural Economics and Social Science Research Association (AESSRA)
"This present study was carried out to examine and analyse the factors determining the labour absorption in agriculture in different agro-climatic regions of Rajasthan with state as a whole. Both primary and secondary data were used for this study. 200 respondents from 10 villages were collected for primary data during year of 2018-2019 and secondary data were used of published data from different reports and publications. The findings were showed that farm size has a significant negative relationship with total labour absorption in all agro-climatic regions at the state level except the transitional plain region. The cropping intensity showed a positive association with the total labour utilization in arid western and northern region, transitional plain regions, semi-arid and flood-prone eastern plain and humid south and eastern plain region. The per hectare absorption of tractor hours was displayed a significant negative relationship with the total labour utilization in all the agro-climatic regions with the state level. Expenditure on animal feeds showed a significant positive association with the total human labour utilization in all the agro-climatic regions with the state level except semi-arid and flood-prone eastern plain region. It was observed that the total labour utilization showed a significantly positive relationship with irrigation intensity in arid western and northern region and humid south and eastern plain region. Unemployment of agricultural labourers has negative impact on their income, consumption expenditure and savings. So, there is need to create additional income opportunities for agricultural labourers."
Soumik Ray, Pradeep Mishra, Hicham Ayad, Prity Kumari, Rajnee Sharma, Binita Kumari, Abdullah Mohammad Ghazi Al Khatib, Anant Tamang, and Tufleuddin Biswas
Walter de Gruyter GmbH
Abstract Forecasting is valuable to countries because it enables them to make informed business decisions and develop data-driven strategies. Fruit production offers promising economic opportunities to reduce rural poverty and unemployment in developing countries and is a crucial component of farm diversification strategies. After vegetables, fruits are the most affordable source of essential vitamins and minerals for human health. India's fruit production strategies should be developed based on accurate predictions and the best forecasting models. This study focused on the forecasting behavior of production of apples, bananas, grapes, mangoes, guavas, and pineapples in India using data from 1961 to 2015 (modelling set) and 2016–2020 (predicting set). Two unit root tests were used, the Ng–Perron (2001) test, and the Dickey–Fuller test with bootstrapping critical values depending on the Park (2003) technique. The results show that all variables are stationary at first differences. Autoregressive integrated moving average (ARIMA) and exponential smoothing (ETS) models were used and compared based on goodness of fit. The results indicated that the ETS model was the best in all the cases, as the predictions using ETS had the smallest errors and deviations between forecasting and actual values. This result was confirmed using three tests: Diebold–Mariano, Giacomini–White, and Clark–West. According to the best models, forecasts for production during 2021–2027 were obtained. In terms of production, an increase is expected for apples, bananas, grapes, mangoes, mangosteens, guavas, and pineapples in India during this period. The current outcomes of the forecasts could enable policymakers to create an enabling environment for farmers, exporters, and other stakeholders, leading to stable markets and enhanced economic growth. Policymakers can use the insights from forecasting to design strategies that ensure a diverse and nutritious fruit supply for the population. This can include initiatives like promoting small-scale farming, improving postharvest storage and processing facilities, and establishing effective distribution networks to reach vulnerable communities.
Sarfraz Hussain, Rosalan Ali, Walid Emam, Yusra Tashkandy, Pradeep Mishra, Mochammad Fahlevi, and Adelajda Matuka
MDPI AG
This study seeks to determine how economic policy uncertainty (EPU) influences investment decisions and the market value of the Pakistan Stock Exchange. This study examines investment and operational data from 249 energy and petroleum companies between 2015 and 2020 and macroeconomic variables such as EPU. This study investigates the moderating effects of EPU on investments in fixed and intangible assets and its effect on Tobin’s Q and the market price per share. The outcomes demonstrate that EPU reduces the costs of both tangible and intangible assets for businesses. In addition, companies with a higher Tobin’s Q and market price per share are more impacted by uncertain corporate investment policies. However, financial leverage is negatively correlated with share price and positively correlated with earnings per share and earnings per unit. Tobin’s Q positively correlates with financial leverage, indicating that firms that raise capital through debt are more likely to create value for investors. The research indicates that market-dependent enterprises are more susceptible to the unpredictability of monetary policy. According to this study, consistent application and open communication of economic policies are likely to increase the efficacy of company investments, resulting in more effective resource allocation and business decision-making.