In recent decades, breast cancer has increased to become the world's second leading cause of death among women. Chronic pain, genetic abnormalities, skin issues, texture of the skin, and color (redness) all appear to be indications of BC. Benign and malignant cancer is the most common binary classifications. Clinicians may discover a method of treatment that is both comprehensive and reliable. Machine Learning (ML) approaches are increasingly being employed in the classification of breast cancer. It supports with highaccuracy classifications and fast calculation skills. The proposed research work examines a supervised learning technique for classifying breast cancer that uses four different classifiers: Boosted Tree, Bagged Tree, Logistic Regression (LR) and Artificial Neural Network (ANN). Also, this research work will compare and contrast the four classifiers, as well as assess the performance. Based on the performance metrics, the above classifiers are analyzed, in which the Artificial Neural Network results with the accuracy of 97.56 % when compared to other classifiers.

The ball and beam system is a piece of laboratory equipment with a lot of nonlinear dynamics. The main ideas are to exhibit Ball and Beam System (BBS) with integrating nonlinear aspects and coupling impact, and to develop a Corresponding Indispensable Subordinate (PID) controller to regulate the ball position. An Arduino microcontroller is used in the system. It compares the ball location to the optimal separation, which can be chosen by the client, using an ultrasonic separation sensor. PID calculation was used in Arduino to convert the difference in signal between the desired and actual situation by controlling the signal. The Arduino delivers the control signal to DC servomotor, which revolves and adjust the ball position to reach set point. MATLAB programming were used to depict the moment system reaction by connecting Arduino to a PC and determining system attributes using various controller parameter estimations in order to select parameter values that gave the system the greatest performance.