Machine Intelligence Based Assessment of Nutritional Olfactory Features for Controlling Health Hazards Using Electronic Nose

Abstract

Purpose: Purpose of this research is to carry out a machine learning intelligence based innovative method to determine quality of food which may be hazards to health if consumed by humans. This article detects human sickness by sensing nutrition that causes smells.   Theoretical framework: In developing nations, people just focus on basic need of food rather than focusing on the quality and the nutritional values of food which are exhibiting hazards impact of unhealthy food on the lives of people. Many people are suffering from diabetics, cancers, cardiac problem, liver problems and stomach related health issues which are originated due to consumption of bad food. Consumers are satisfied with food quality, and more individuals are assessing it.   Method/design/approach: As a methodology, an electronic nose uses chemical sensors to identify complicated odors. Standard technologies can detect gases from households, industries, and explosive materials. It cannot fulfill freshness requirements. Electronic noses, computer vision, and other sensory approaches may imitate human olfactory, taste, visual, and sensory qualities, both pleasantly and unpleasantly. Neural networks organize innovative artificial/mechanical intelligence systems to interpret fragrance recordings for human brain recognition. Inspired by human brain processing, we offer optimized feedback, centroid clustering, and self-organizing maps for machine learning systems to identify smell data. This work proposes a simulation technique based on benchmark datasets to achieve high type accuracy, precision, and recall for diverse scented records where additional information may be artificially/mechanically found. The centroid SOM research of olfaction involves investigating more physiologically and nutritionally feasible methods for mapping, understanding, and interpreting massive scent datasets for real-world applications.   Results and conclusion: In all analyzed result and conclusion, the accuracy, precision, and recall of the clustering centroid with optimized feedback SOM are superior to the existing clustering approach. By simulating the data on different set of test and train data it has observed that Proposed (Cluster Centroid with SOM ) method is effective than the existing (Centroid) method. For example, 10% of test data existing method has 67.55% of accuracy and proposed method has 86.75% which is shown in result and conclusion section in details.   Research implications: The research makes an effective contribution by demonstrating the potential and the need to adopt sustainable practices in the management of contemporary companies.   Originality/value: The results and conclusion obtained in this research are unprecedented, innovative and relevant to the medico health community to avoid health diseases, in the context of reliability in social community suggest eating a fresh and pleasant food to avoid health diseases.