Applications of Machine Learning in Biomedical Text Processing and Food Industry

The evolutions of the World Wide Web (WWW) has witnessed proliferation of data and boom in technologies for extracting information out of the big data for marketing strategy and adds value to products, services and personalize the customer experience. Recently there has been a dramatic surge of interest in the era of Artificial Intelligence and Machine Learning (AI and ML), and more people become aware of the scope of new applications enabled by the ML approaches. The applications of ML ranging from household to healthcare, domestic applications to enterprise applications, agriculture to military applications, encompass all walks of life. In this book chapter, the main focus would be on applications of ML approaches in two different sub-domains which are connected to healthcare sector. The first application is on Sentiment Analysis (SA) of user narrated drug reviews and the second one is about engineering in food technology. As AI and ML techniques push the limits of scientific drug discovery, ML approaches are preferred over other approaches for two important reasons. The first one is that ML comes with distinctive learning strategies and its viability for many NLP tasks. The second is that its inherent ability to model many features which capture the aspects of sentiments in text. However, despite the fact that the results ML approaches produce are no human understandable, they may help us to achieve high accuracy. Food Engineering is an advanced branch of Engineering, which deals with production, Evaluation of quality of food, innovation in new recipes, nutritional level in the food and management of food. Most of the food engineering mechanisms involve classification and prediction algorithms. In this chapter two facets of food engineering technologies which use machine learning techniques are depicted using some case studies. Nowadays, malnutrition of a child is a major problem. The analysis of children’s health by classifying malnutrition and nutrition using classification algorithms is also depicted in this chapter.