Machine Learning (ML), which is one of the most prominent applications of Artificial Intelligence, is doing wonders in the research field of study. In this paper machine learning is used in detecting if a person has a heart disease or not. A lot of people suffer from cardiovascular diseases (CVDs), which even cost people their lives all around the world. Machine learning can be used to detect whether a person is suffering from a cardiovascular disease by considering certain attributes like chest pain, cholesterol level, age of the person and some other attributes. Classification algorithms based on supervised learning which is a type of machine learning can make diagnoses of cardiovascular diseases easy. Algorithms like K-Nearest Neighbor (KNN), Random Forest are used to classify people who have a heart disease from people who do not. Two supervised machine learning algorithms are used in this paper which are, K-Nearest Neighbor (K-NN) and Random Forest. The prediction accuracy obtained by K-Nearest Neighbor (K-NN) is 86.885% and the prediction accuracy obtained by Random Forest algorithm is 81.967%.

Artificial intelligence (AI), or AI/machine vision, is assuming an overwhelming part in the realm of food handling and quality affirmation. As indicated by Mordor Intelligence, AI in the food and refreshments market is required to enlist a CAGR of 28.64%, during the conjecture time frame 2018–2023. Artificial intelligence makes it workable for PCs to gain as a matter of fact, investigate information from the two data sources and yields, and perform most human assignments with an improved level of accuracy and proficiency. Here is a concise gander at how AI is expanding sanitation and quality activities. This exploration has along these lines tried to furnish policymakers with a way to assess new and existing strategies, while likewise offering a reasonable premise through which food chains orders can be made stronger through the thought of the executive’s practices and strategy choices. This survey centers on the AI applications according to four mainstays of food security that is food accessibility, food availability, food use, and strength.

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.

This paper enlightens the use of artificial intelligence (AI) for distribution of fresh foods by searching more viable route to keep intact the food attributes. In recent years, very hard-hitting competition is for food industries because of the individuals living standards and their responsiveness for fresh food products demand within stipulated time period. Food industry deals with the extensive kind of activities such as food processing, food packaging and distribution, and instrumentation and control. To meet market demand, customer satisfaction, and maintaining its own brand and ranking on global scale, artificial intelligence can play a vibrant role in decision-making by providing analytical solutions with adjusting available resources. Therefore, by integrating innovative technologies for fresh food distribution, potential benefits have been increased, and simultaneously risk associated with the food quality is reduced. Time is a major factor upon which food quality depends; hence, time required to complete the task must be minimized, and it is achieved by reducing the distance travelled; so, path optimization is the key for the overall task. Swarm intelligence (SI) is a subfield of artificial intelligence and consists of many algorithms. SI is a branch of nature-inspired algorithm, having a capability of global search, and gives optimized solution for real-time problems adaptive in nature. An artificial bee colony (ABC) optimization and cuckoo search (CS) algorithm also come into the category of SI algorithm. Researchers have implemented ABC algorithm and CS algorithm to optimize the distribution route for fresh food delivery in time window along with considering other factors: fixed number of delivery vehicles and fixed cost and fuel by covering all service locations. Results show that this research provides an efficient approach, i.e., artificial bee colony algorithm for fresh food distribution in time window without penalty and food quality loss.

Food safety refers to preparing, transporting, and storing food to avoid foodborne sickness and harm. From farm to factory and factory to fork, food items may meet various health dangers. Therefore, food safety is crucial both monetarily and morally. The implications of failing to comply with food safety requirements are varied. The requirement for accurate, quick, and nonpartisan quality assessments of these features in food products continues to rise with increased demands for dietary materials and high-quality requirements. Computer vision provides an automatic, nondestructive, and economic approach to achieving these aims. A substantial research has demonstrated its effectiveness for fruit and vegetable assessment and classification. It stresses the critical components of image processing technology and a survey of the most current advances across the food sector. This article outlines the essential parts of a computer vision system. In order to avoid foodborne disease and ensure food security, fast and effective detection of pathogenic microorganisms is crucial for public safety biomonitoring. Over the years, microorganism detection techniques have evolved.