A Robust Development of Calorie Prediction Methodology based on Artificial Intelligence Assisted Machine Learning Model

Modern people’s hectic schedules are a direct result of the ways they live and the responsibilities they have at work. On the other hand, maintaining a healthy lifestyle demands consistent physical exertion. Obesity is a result of people not paying attention to what they eat. As a result of modern living, obesity is on the rise. As a result, in order to maintain a healthy weight, people pick their food and exercise habits accordingly. People should know how many calories they consume and how many calories they burn. Calorie counts are easy to get online or on product labels, so it’s not hard to keep track of one’s calorie intake. There aren’t many tools available to help you keep track of the calories you burn. To keep up with the hectic pace of modern life and the constant search for shortcuts, we built a system that uses machine learning to track post-workout calorie burn and development. By taking a few attributes as input, this system can approximate the calories burned, which will show daily growth and motivate people to exercise more. This paper presents an innovative ML model for accurate calorie prediction; it’s called the Improved Learning Model for Calorie Prediction (ILMCP). To test how well it works, the authors cross-validate it with the traditional Extreme Gradient Boosting (XGBoost) algorithm. As a result, it is the quickest approach and may be utilized for object identification in real-time. Next, we’ll apply the ILMCP algorithm to the picture for segmentation. Foreground extraction with little to no user input requires it. Following picture segmentation, we use the probing object’s known volume to determine the food item’s volume. Once the volume has been determined, the food item’s mass may be determined using formulae. Then, the connection between weight and calories can be used to compute the food item’s calories.