Medicolite-Machine Learning-Based Patient Care Model.

This paper discusses the machine learning effect on healthcare and the development of an application named "Medicolite" in which various modules have been developed for convenience with health-related problems like issues with diet. It also provides online doctor appointments from home and medication through the phone. A healthcare system is "Smart" when it can decide on its own and can prescribe patients life-saving drugs. Machine learning helps in capturing data that are large and contain sensitive information about the patients, so data security is one of the important aspects of this system. It is a health system that uses trending technologies and mobile internet to connect people and healthcare institutions to make them aware of their health condition by intelligently responding to their questions. It perceives information through machine learning and processes this information using cloud computing. With the new technologies, the system decreases the manual intervention in healthcare. Every single piece of information has been saved in the system and the user can access it any time. Furthermore, users can take appointments at any time without standing in a queue. In this paper, the authors proposed a CNN-based classifier. This CNN-based classifier is faster than SVM-based classifier. When these two classifiers are compared based on training and testing sessions, it has been found that the CNN has taken less time (30 seconds) compared to SVM (58 seconds).

Machine Learning and IoT-Based Waste Management Model.

A rapid rise in inhabitants across the globe has led to the inadmissible management of waste in various countries, giving rise to various health issues and environmental pollution. The waste-collecting trucks collect waste just once or twice in seven days. Due to improper waste collection practices, the waste in the dustbin is spread on the streets. Thus, to defeat this situation, an efficient solution for smart and effective waste management using machine learning (ML) and the Internet of Things (IoT) is proposed in this paper. In the proposed solution, the authors have used an Arduino UNO microcontroller, ultrasonic sensor, and moisture sensor. Using image processing, one can measure the waste index of a particular dumping ground. A hardware prototype is also developed for the proposed framework. Thus, the presented solution for the efficient management of waste accomplishes the aim of establishing clean and pollution-free cities.