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According to recent statistics, breast cancer is one of the most prevalent cancers among women in the world. It represents the majority of new cancer cases and cancer-related deaths. Early diagnosis is very important, as it becomes fatal unless detected and treated in early stages. With the latest advances in artificial intelligence and machine learning (ML), there is a great potential to diagnose breast cancer by using structured data. In this paper, we conduct an empirical comparison of 10 popular machine learning models for the prediction of breast cancer. We used well known Wisconsin Breast Cancer Dataset (WBCD) to train the models and employed advanced accuracy metrics for comparison. Experimental results show that all models demonstrate superior accuracy, while Support Vector Machines (SVM) had slightly better performance than other methods. Logistic Regression, K-Nearest Neighbors and Neural Networks also proved to be strong classifiers for predicting breast cancer.

Equipped with an advanced radar and other electronic systems mounted on its body, Airborne Early Warning and Control System (AWACS) enables the airspace to be monitored from medium to long distances and facilitates effective control of friendly aircraft. To operate the complex equipment and fulfill its critical functions, AWACS has a specialised flight and mission crew, all of whom are extensively trained in their respective roles. For mission accomplishment and effective use of resources, tasks should be scheduled, and individuals should be assigned to missions appropriately. In this paper, we implemented evolutionary algorithms for scheduling aircrew on AWACS and propose a novel approach using Genetic Algorithms (GA) with a special encoding strategy and modified genetic operations tailored to the problem. The objective is to assign aircrew to various AWACS tasks such as flights, simulator sessions, ground training classes and other squadron duties while aiming to maximise combat readiness and minimise operational costs. The presented approach is applied to several test instances consisting notional weekly schedules of Turkish Boeing 737 AEW&C Peace Eagle AWACS Base, generated similar to real-world examples. To test the algorithm and evaluate solution performance, experiments have been conducted on a novel scheduling software called AWACS Crew Scheduling (ACS), developed as a test bed. Computational results reveal that presented GA approach proves to be quite successful in solving the AWACS Crew Scheduling Problem and exhibits superior performance when compared to manual methods.

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