Different Perspective of Machine Learning Technique to Better Predict Breast Cancer Survival

Machine learning (ML) plays a key job in the guide of cancer diagnosis and identification. The researcher has implemented different algorithms of ML for the prediction of breast cancer. Some researchers recommend their algorithms are more accurate, faster, and easier than others. My study relies on recently developed machine learning algorithms like genetic algorithms and deep belief nets. I’m interested to build a framework to precisely separate among benign and malignant tumors. We’ve optimized the training algorithm. During this unique circumstance, we applied the genetic algorithm procedure to settle on the main genuine highlights and perfect boundary estimations of the AI classifiers. The examinations rely upon affectability, cross-validation, precision, and ROC curve. Among all the varying kinds of classifiers used in this paper genetic programming is the premier viable model for highlight determination and classifier.

Policy Planning Using Genetic Algorithms Combined with Simulation: The Case of Municipal Solid Waste

Previous research had introduced a genetic algorithm procedure for creating alternative policy options for municipal solid waste (MSW) management planning. These alternatives were generated during the design phase of planning, with the final policy determined in subsequent comparative analysis. However, because of the many uncertain factors that exist within MSW systems, this earlier procedure cannot be applied to situations containing such stochastic components. In this paper, it is shown that a generic algorithm approach can be simultaneously combined with simulation to incorporate these stochastic elements in the policy option generation phase; thereby permitting uncertainty to be directly integrated into the construction of the alternatives during the planning-design phase. This procedure is applied to case data taken from the Regional Municipality of Hamilton–Wentworth in the Province of Ontario, Canada. It can be shown that this procedure extends the earlier approach and provides many practical planning benefits for problems when uncertain conditions are present.

Manufacturing cell scheduling using genetic algorithms

This paper presents a new approach to the scheduling of manufacturing cells which have flow-shop configuration. The approach is based on the genetic algorithm, which is a meta-heuristic for solving combinatorial optimization problems. The performance measure demonstrated in this paper is the optimization of the mean flow time. The procedure developed automatically computes the make-span. A flexible manufacturing cell schedule is used as a case study. The genetic algorithm procedure was used to solve a published data set for simple scheduling problems. The genetic algorithm procedure was further used to solve large flow-shop scheduling problems having machine sizes of up to 30 and job sizes of up to 100 in very reasonable computation time. The results show that the genetic-algorithm-based heuristic is promising for scheduling manufacturing cells.