Job Shop Scheduling Problem (JSSP) has become one of the key issues in a contemporary manufacturing system in which the task is to optimally schedule jobs to the machines to reduce the time and resources used in production. Good scheduling is critical in enhancing the productivity and competitiveness of manufacturing industries. In this research, Artificial Fish Swarm Optimization (AFSO) algorithm is used to optimize the JSSP in minimizing makespan, total work load and maximum work load in machines. The AFSO strategy models the swarm behaviour of fishes to search and forage the search space in an efficient manner to prevent its early convergence to local optima. The model incorporates a disturbed state in the world to improve the direction in search and the speed of convergence. The effectiveness of the suggested AFSO method is compared and tested with the traditional and sophisticated optimization algorithms like Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) methods. The experimental findings prove that the offered technique provides better results in convergence rate and solution quality. The results prove that AFSO is a useful and promising method of solving complex problems in production system scheduling.

There are remarkable improvements in the healthcare sector particularly in patient care, maintaining and protecting the data, and saving administrative and operating costs, etc. Among the various functions in the healthcare sector, disease diagnosis is considered as the foremost function because it saves a life at the correct time. Early detection of diseases helps in disease prevention, letting the patients get vigorous and effective treatment and saving their lives. Several techniques were suggested by the researchers for disease prediction. Many literatures have been witnessed on disease prediction. This article reviews several articles systematically and compares various machine learning (ML) algorithms for disease prediction, including the Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and Logistic Regression (LR) algorithms. A thorough analysis is presented based on the number of publications year-wise, disease-wise, and also based on the performance metrics. This review thoroughly analyzes and compares various ML techniques applied in disease prediction, focusing on classification algorithms commonly employed in healthcare applications. From the systematic review, a multi objective optimization method named Grey Relational Analysis (GRA) is used to rank the ML algorithms using their performance metrics. The results of this paper help the researchers to have an insight into the disease prediction domain. Also, the performance of various ML algorithms aids the researchers to choose a better methodology to predict a disease.