<p>Presently, the move towards a more complex and multidisciplinary system development is increasingly important in order to understand and strengthen engineering approaches for the systems in the engineering field. This will lead to the effective and successful management of these systems. The scientific developments in computer engineering, simulation and modeling, electromechanical motion tools, power electronics, computers and informatics, micro-electro-mechanical systems (MEMS), microprocessors, and distributed system platforms (DSPs) have brought new challenges to industry and academia. Important aspects of designing advanced mechatronic products include modeling, simulation, analysis, virtual prototyping, and visualization. Competition on a global market includes the adaptation of new technology to produce better, cheaper, and smarter, scalable, multifunctional goods. Since the application area for developing such systems is very broad, including, for example, automotive, aeronautics, robotics or consumer products, and much more, there is also the need for flexible and adaptable methods to develop such systems. These dynamic interdisciplinary systems are called mechatronic systems, which refer to a system that possess synergistic integration of Software, electronic, and mechanical systems. To approach the complexity inherent in the aspects of the discipline, different methods and techniques of development and integration are coming from the disciplines involved. This paper will provide a brief review of the history, current developments and the future trends of mechatronics in general view.</p>

The planet earth has been facing COVID-19 epidemic as a challenge in recent time. It is predictable that the world will be fighting the pandemic by taking precautions steps before an operative vaccine is found. The IoT produces huge data volumes, whether private or public, through the invention of IoT devices in the form of smart devices with an improved rate of IoT data generation. A lot of devices interact with each other in the IoT ecosystem through the cloud or servers. Various techniques have been presented in recent time, using data mining approach have proven help detect possible cases of coronaviruses. Therefore, this study uses machine learning technique (ABC and SVM) to predict COVID-19 for IoT data system. The system used two machine learning techniques which are Artificial Bee Colony algorithm with Support Vector Machine classifier on a San Francisco COVID-19 dataset. The system was evaluated using confusion matrix and had a 95% accuracy, 95% sensitivity, 95% specificity, 97% precision, 96% F1 score, 89% Matthews correlation coefficient for ABC-L-SVM and 97% accuracy, 95% sensitivity, 100% specificity, 100% precision, 97% F1 score, 93.1% Matthews correlation coefficient for ABC-Q-SVM. In conclusion, the system shows that the process of dimensionality reduction utilizing ABC feature extraction techniques can boost the classification production for SVM. It was observed that fetching relevant information from IoT systems before classification is relatively beneficial.

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