In recent years, the demand for gesture recognition has increased enormously due to many applications such as computer games, human-robot interaction, assistance systems, sports, sign language interpreters, and e-commerce. The recognition of hand gestures is one of the most important gesture recognition methods. With simple hand gestures, devices in the smart home area (TV, radio, vacuum cleaner robots, etc.) should be easier to operate. Our method is based on a convolutional neural network, or more precisely on MobileNetV2. With this lean and fast network, we have been able to achieve an accuracy of 99.96 percent in recognition of hand gestures, so that in the future, we will be able to offer an application in the field of Human-Computer Interaction to interact more easily with the ever-increasing number of technologies in everyday life.

In the production of screws, manual methods are often still used to detect defects. This paper aims to use a convolutional neural network-based technique to detect whether defects in screws are caused during production. Our experimental results show that a detection accuracy of 96.67% can be achieved with the proposed technique. Among the defects considered are defects on the objects' surface (e.g., scratches, dents), structural defects like distorted object parts, or defects that manifest themselves by the absence of certain object parts. Our more efficient method can be used in the future for quality control in the manufacture of screws.