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Gupta, Archana
- Quality Evaluation of Carrot, Spinach and Tomato Dried by Different Methods for the Preparation of Soups
Abstract Views :206 |
PDF Views:3
Authors
Affiliations
1 Dept. of Foods and Nutrition, College of Home Science, Rajasthan Agri. University Campus, Udaipur, Rajasthan, IN
1 Dept. of Foods and Nutrition, College of Home Science, Rajasthan Agri. University Campus, Udaipur, Rajasthan, IN
Source
The Indian Journal of Nutrition and Dietetics, Vol 28, No 12 (1991), Pagination: 328-333Abstract
The importance of soup in the diet is well known as it acts as a appetizer. Generally soups ore prepared with fresh vegetables, which require peeling, cutting, boiling, straining and seasoning. This makes the task tedious and time consuming. Therefore, availabiiity of dried vegetable powders for the preparation of soups can be a boon against the time consuming culinary work. But information about the nutrient content and organoleptic characteristics of vegetables dried by different drying methods is lacking in the literature. Therefore, present investigation has been undertaken to find out the best method of drying for the preparation of vegetable soup powders.
- Machine Learning Based Heart Disease Prediction Model with GUI Interface
Abstract Views :136 |
PDF Views:0
Authors
Affiliations
1 Student, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
2 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
1 Student, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
2 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 261-276Abstract
One of the top causes of death around the globe is a heart attack. According to current statistics, one person dies from heart disease every minute, making it one of the primary problems in everyday modern life. The ability to predict the onset of illness at an early stage is extremely difficult nowadays. When used in the healthcare industry, machine learning has the potential to accurately and quickly diagnose diseases. The circumstances under which heart disease may arise are estimated in this study. Medical parameters are characteristics of the datasets utilized. The datasets are analyzed using the Random Forest Algorithm, a machine learning algorithm, in Python. This method makes use of historical patient data from the past to forecast future ones at an early stage, saving lives. In this study, a trustworthy system for predicting heart disease is put into place utilizing a powerful machine learning algorithm called the Random Forest method. This reads a CSV file containing patient record data. After gaining access to the dataset, the procedure is carried out, and a useful heart attack level is generated. The suggested system's benefits include High success rates are attained, along with excellent performance and accuracy rates, flexibility, and adaptability.Keywords
Machine Learning, Artificial Intelligence, Heart Disease.Full Text
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