Computers are used everywhere from home to work these days, thus the effects on the visual system will be more noticeable every day. Problems associated with Computer usage are generally known as the Computer Vision syndrome (CVS).These problems include eye fatigue, irritation, redness, blurred Vision and dry eye. All of these problems have adverse effects on individuals in the long term and reduce the efficiency and quality of workers. CVS is due to various display characteristics such as screen brightness, high brightness in the workplace, screen refresh rate, radiation, reduced rate of blinking, large eye surface prone to dryness and accommodation effort for close work. Treatment requires a multidirectional approach such as adjustment of the workstation. Proper lighting, anti-glare filters, ergonomic positioning of the monitor and regular work breaks may help improve visual comfort. Lubricating eye drops and special Computer glasses help relieve ocular surface related symptoms. Also the treatment of this syndrome especially in the early stages has successful result, but users that do not know about this problems, tolerate a lot of complications.

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.