This paper helps to understand the student's insightful ideas of how stress affects their day-to-day lives because many students use the word stress frequently in their daily lives. The majority of academics and researchers have started looking into the causes of stress in adults, college students, and school-aged children. This case study, the author wants to find out what students think about different aspects of stress in their daily lives. At Bishop Heber College in Trichy, India, data was obtained from under-graduate and Post-graduate mathematics students. The data was gathered from the students using Google forms. The gathered data was statistically analyzed. Descriptive statistics are mostly used to describe the information on various variables that was obtained from the questionnaire (frequency, percentages, pie, and line charts). To ascertain whether there was a significant difference in the students' perceptions of how stress affects their daily lives, Pearson's Correlation and Chi-Square tests were also performed. Based on the data, suggestions and conclusions were drawn.
{"title":"Statistical approach to understanding students' opinions on how stress impacts their day to day life","authors":"Antony Martin, Parthiban Saminathan","doi":"10.59461/ijitra.v2i1.52","DOIUrl":"https://doi.org/10.59461/ijitra.v2i1.52","url":null,"abstract":"This paper helps to understand the student's insightful ideas of how stress affects their day-to-day lives because many students use the word stress frequently in their daily lives. The majority of academics and researchers have started looking into the causes of stress in adults, college students, and school-aged children. This case study, the author wants to find out what students think about different aspects of stress in their daily lives. At Bishop Heber College in Trichy, India, data was obtained from under-graduate and Post-graduate mathematics students. The data was gathered from the students using Google forms. The gathered data was statistically analyzed. Descriptive statistics are mostly used to describe the information on various variables that was obtained from the questionnaire (frequency, percentages, pie, and line charts). To ascertain whether there was a significant difference in the students' perceptions of how stress affects their daily lives, Pearson's Correlation and Chi-Square tests were also performed. Based on the data, suggestions and conclusions were drawn.","PeriodicalId":187267,"journal":{"name":"International Journal of Information Technology, Research and Applications","volume":"368 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116449687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Agriculture planning is playing an important role as the economic growth is very high day by day in our daily life. There is lot of research study going on as there are few important issues like soil nutrients, crop prediction, farming system, crop monitoring in agriculture with modern farming system. Based on various parameters, farming issues and farming system, there is lot of change in production rate and market prices. Crop prediction and crop monitoring is main factor to produce good quality of crops for farmers to predict crop yield based on soil moisture. Prediction of crop yield includes forecasting factors like temperature, humidity, rainfall, etc., and crop yield based on soil moisture includes few measures like pH, NPK (Nitrogen, Phosphorous and potassium) values using various sensors. Farmers can predict or come to a decision the type of soil moisture values, farmers can decide the type of crop to be planted. In this paper, we proposed decision tree supervised machine learning algorithm to improve our results for the prediction of crop yield based on soil moisture parameters to achieve economic growth for achievement of better results.
{"title":"Prediction of Crop Yield Based-on Soil Moisture using Machine Learning Algorithms","authors":"Mahesh T R, Sindhu Madhuri G","doi":"10.59461/ijitra.v2i1.30","DOIUrl":"https://doi.org/10.59461/ijitra.v2i1.30","url":null,"abstract":"Agriculture planning is playing an important role as the economic growth is very high day by day in our daily life. There is lot of research study going on as there are few important issues like soil nutrients, crop prediction, farming system, crop monitoring in agriculture with modern farming system. Based on various parameters, farming issues and farming system, there is lot of change in production rate and market prices. Crop prediction and crop monitoring is main factor to produce good quality of crops for farmers to predict crop yield based on soil moisture. Prediction of crop yield includes forecasting factors like temperature, humidity, rainfall, etc., and crop yield based on soil moisture includes few measures like pH, NPK (Nitrogen, Phosphorous and potassium) values using various sensors. Farmers can predict or come to a decision the type of soil moisture values, farmers can decide the type of crop to be planted. In this paper, we proposed decision tree supervised machine learning algorithm to improve our results for the prediction of crop yield based on soil moisture parameters to achieve economic growth for achievement of better results.","PeriodicalId":187267,"journal":{"name":"International Journal of Information Technology, Research and Applications","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116171210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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