With the increasing need for efficient traffic surveillance systems, computer vision emerges as a promising technology to address various challenges in this domain. This paper aims to provide an overview of recent advancements in the application of computer vision in traffic surveillance. It commences by discussing fundamental concepts of computer vision and its role in traffic monitoring, followed by an exploration of various techniques and algorithms used in computer vision systems, including object detection, tracking, and classification. Furthermore, it investigates the integration of computer vision with other technologies such as machine learning and deep learning, emphasizing their impact on enhancing the accuracy and efficiency of traffic surveillance systems. The review explores specific applications of computer vision in traffic surveillance, such as traffic flow monitoring, anomaly detection, and license plate recognition. Additionally, it addresses challenges and limitations associated with the implementation of computer vision in real-world traffic scenarios. Synthesizing existing literature, case studies, and research findings, this review offers a comprehensive insight into the current state-of-the-art in the application of computer vision for traffic surveillance systems. The paper concludes with a discussion of future directions and potential research opportunities in this rapidly evolving field.

: Within this research, the focus was on analyzing the effectiveness of Computer Vision (CV) in detecting vehicles and pedestrians in traffic. The YOLOv5 model was utilized for object detection, along with publicly available, unmodified libraries like OpenCV and TensorFlow. The approach involved a careful selection of three different traffic scenarios: a rainy day, daytime, and night-time, with the intention of creating realistic conditions for testing the performance of vehicle and pedestrian detection systems. An algorithm for detecting pedestrians and vehicles was implemented, contributing further to road safety. Through experiments, exploration was conducted into how various factors, such as weather conditions and lighting, influence the accuracy of the system. Following a meticulous analysis of the results, situations in which the system exhibits high detection accuracy, as well as those that pose a challenge to the system were identified, in order to provide a profound understanding of different aspects of pedestrian tracking and vehicle detection. Through the application of image analysis techniques, the focus was on identification of key features of pedestrian crossings, contributing to the recognition of potentially dangerous situations. The objective was to draw accurate conclusions regarding the system's performance under actual traffic conditions, thus enhancing the overall comprehension of how these technologies effectively contribute to improving road safety.

: Precision agriculture is an innovative farming method that aims to maximize crop yields while minimizing waste. One of the key technologies used in precision agriculture is computer vision, which involves using cameras and sensors to collect data on crop growth and health. This data is then analyzed using machine learning algorithms to provide insights on how to optimize farming practices and improve yields. In this article, we provide an in-depth analysis of the role of computer vision in precision agriculture, with a focus on its applications in crop monitoring, the various types of cameras and sensors utilized in computer vision systems, and the diverse machine-learning algorithms employed to analyze the data collected. Through this analysis, we aim to offer a comprehensive overview of the potential of computer vision to revolutionize the way we grow and harvest crops, and the impact it could have on the future of agriculture.

In this paper, we deal with the potential applications of Artificial Intelligence in educational institutions. One of the main goals of applying the mentioned technology is to make the education system more efficient if possible. Based on research in this area in the world, our impression is that there is a long-term plan for introducing AI in schools, as a new kind of learning standard. The idea is not for artificial intelligence to completely take over the domain of education, but for some processes to be better organized or improved, so that the result of learning would be of better quality. New technologies, in the form of modern learning platforms that incorporate AI as one of the most important components of the system, significantly improve the communication and research work of students. It is evident that new generations are coming whose experience will be significantly different from ours, and that the new technologies from the domain of AI that we consider in this paper will significantly affect the design processes in the future.

Artificial intelligence technology is rapidly advancing year by year, and thus the possibilities of using artificial intelligence in every branch of science. The importance of artificial intelligence and its components has been known for a long time. They are seen as tools and techniques that make the world a better place With their simple and everyday techniques, they make the world a mistake-free place. These technologies and applications are not only related to our general and everyday life, but also affect and have significance for other domains as well. The paper will analyze the application of artificial intelligence and its influence today.

Modern technologies, in addition to making people's lives easier, faster and better, enable the combination of different technologies to develop solutions to various problems. Global warming is one of the biggest and most dangerous problems facing planet earth. If global warming were presented as a coin, then air pollution and climate change would be two sides of the same coin. Climate change is primarily a problem that has arisen due to the large amount of carbon dioxide (CO2 ) in the atmosphere. Carbon overload occurs mainly due to the combustion of fossil fuels such as coal, oil and gas, or the cutting and burning of forests. There are other harmful gases but CO2 stands out because it poses the greatest risk of irreversible change if it continues to accumulate in the atmosphere. This paper presents a number of different solutions by combining modern technologies for the problem of CO2 emissions globally, where Bosnia and Herzegovina is particularly singled out as one of the countries with the most polluted air in Europe and with the highest mortality rate associated with air pollution. Smart cities use digital technologies, aim to improve the quality of human life and to enable the protection of the environment. The concept of a smart city is described as a set of modern technologies, examples of leading smart cities in the world are given, as well as the advantages of implementing such a solution.

The Internet of Things (IoT) is integrated and applied in various domains such as Smart Home, Healthcare, Industrial IoT (IIoT), and others. IoT allows physical objects to be able to create, receive and exchange huge amounts of data at any time. The goal of such applications is to automate physical objects that will be able to function without human intervention or with minimal intervention depending on the purpose of the application. Because all domains involved involve the use of sensitive data and that data is usually transmitted over insecure channels, security and privacy issues arise due to potential cyber-attacks. This paper presents various IoT security techniques as well as the main security goals and characteristics. Three types of authentication schemes, one-factor, two-factor, and threefactor are then explained. Since authentication is one of the most important security goals, a special focus is on two-factor authentication schemes.

Processing differences between Serbian prepositions and nouns were investigated in two lexical decision experiments. The outcome of the experiments indicate that processing of nouns presented in their citation form is affected primarily by the amount of information (bits) derived from noun's probability. Processing of prepositions appeared to be more complex because, in addition to probability of preposition, processing latency is also affected by number of syntactic functions/meanings carried by a given preposition and number of congruent noun cases. None of these factors per se is correlated with processing latencies. However, when combined into a single unit (expressed in bits) significant proportion of processing latency variability has been accounted for. It was shown that prepositions are processed faster than nouns of equivalent frequency and equivalent length. We conclude this on the basis of the observed differences in slope from linear regression, but also on the basis of differences in averaged RTs for the two word types.