Thyroid disease is the general concept for a medical problem that prevents one’s thyroid from producing enough hormones. Thyroid disease can affect everyone—men, women, children, adolescents, and the elderly. Thyroid disorders are detected by blood tests, which are notoriously difficult to interpret due to the enormous amount of data necessary to forecast results. For this reason, this study compares eleven machine learning algorithms to determine which one produces the best accuracy for predicting thyroid risk accurately. This study utilizes the Sick-euthyroid dataset, acquired from the University of California, Irvine’s machine learning repository, for this purpose. Since the target variable classes in this dataset are mostly one, the accuracy score does not accurately indicate the prediction outcome. Thus, the evaluation metric contains accuracy and recall ratings. Additionally, the F1-score produces a single value that balances the precision and recall when an uneven distribution class exists. Finally, the F1-score is utilized to evaluate the performance of the employed machine learning algorithms as it is one of the most effective output measurements for unbalanced classification problems. The experiment shows that the ANN Classifier with an F1-score of 0.957 outperforms the other nine algorithms in terms of accuracy.

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The lighting system takes a dominant part in the building. However, its use is still inefficient because the light provided by the lamp exceeds its needs and often the light stays on when it is not needed. It can be done by creating a lighting system that provide appropriate light for saving the use of energy. A smart lighting system is designed to control light intensity by using a device that is able to detect light intensity and movement. The lighting system will turn on when there is activity in the room, then the system will adjust the intensity in the room. Also, the energy consumption will be saved in the database to ease monitoring process. By using this system, working room’s light intensity will be maintained at standard light intensity value. And also, this system will save power consumption 50.7%.

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robot as electro-mechanical devices is unseparated from our daily life. Many researches has been developed in robotics which one of them is wheeled soccer robot using differential steering method. These robots have advantages in their ai, responses of commands and sensors reading accuracies. In this study, a soccer robot has been developed as integrated systems between a camera, a computer as host and a robot agent. The robot's ability to recognize 3 colors in objects averagely is 3s in starting up, 2.4s in response of 450 commands and 0.4s in response of pid control system with kp=20, kd=1 and ki=10