With the rapid emergence of 3D applications and virtual environments in Computer systems; the need for a new type of interaction device arises. This is because the traditional devices such as mouse, keyboard, and joystick become inefficient and cumbersome within these virtual environments. In other words, evolution of user interfaces shapes the change in the Human-Computer Interaction (HCI). Intuitive and naturalness characteristics of “Hand Gestures” in the HCI have been the driving force and motivation to develop an interaction device which can replace current unwieldy tools. A survey on the methods of analyzing, modeling and recognizing hand gestures in the context of the HCI is provided in this paper. Taxonomy of the different algorithms based on the applications that they have been developed for and the approaches that they have used to represent gestures is presented. In addition, direction of future developments is discussed.

Self-sufficient unmanned systems like Computer Vision products increasingly become essential in managing the data and supporting producers in making decisions in the livestock environment. Image classification is one of the most famous missions for machine learning (ML) methods and its goal is to detect the objects in the frames. The main objective of the research was to investigate whether the classification of two breeds, Hereford and Simmental, often confused with each other due to their morphological similarities, via image processing, is helpful in the case of livestock production. 600 images of different individuals from Hereford (300) and Simental (300) cattle breeds were included in the study. The Fully Connected Neural Networks (FCNN) were established estimate the breeds. Modelling of artificial neural networks, image processing and all other analyses was conducted with EBimage and Keras packages in R language on a PC with 11. Gen. i7 CPU and CUDA supported GPU model of RTX 3060. The results show only 17 were inaccurate in 600 images in total with an accuracy greater than 97%. The training process of the model was executed in 69 seconds. At the end of the investigation, it was clarified that the use of FCNN in livestock would be beneficial in terms of breed classification via image recognition.