: This paper presents an approach for improving the performance of TinyML models through metaheuristic optimization. Due to limited computational resources in IoT environments, achieving high classification accuracy remains a challenge. The proposed method utilizes Particle Swarm Optimization (PSO) for tuning convolutional neural network (CNN) hyperparameters in the task of plant disease classification. Experimental results indicate that optimized models outperform baseline configurations in terms of accuracy and stability, with an improvement of over 20% in MCC compared to the baseline model. This study represents a part of a broader doctoral research focused on efficient and explainable AI models for resource-constrained environments.

: This paper presents a comprehensive study on the development of a traffic sign recognition system based on computer vision, which is crucial for improving road safety and supporting intelligent transport systems (ITS). By using advanced image processing techniques and machine learning algorithms, this research aims to create a reliable system capable of accurately recognizing and classifying various traffic signs. The methodology includes collecting a diverse set of traffic sign images, applying image enhancement techniques, and utilizing deep learning models for precise recognition in different environmental conditions. Experimental results indicate the system’s high accuracy in recognizing traffic signs, even in cases with complex backgrounds, demonstrating its potential for integration into autonomous vehicles and advanced driver assistance systems (ADAS). This research contributes to efforts aimed at enhancing road safety and improving traffic management through automated traffic sign recognition.

Modern lifestyle, the abundance of high-calorie food, and the lack of physical activity have led to a global increase in obesity rates. In this context, the development of smart fitness equipment powered by artificial intelligence (AI) represents significant potential for enhancing physical activity, monitoring health parameters, and providing personalized guidance for weight loss and fitness improvement. This paper explores the functionalities and advantages of AI-equipped fitness devices, including physiological parameter monitoring, motion analysis through computer vision, detection of improper exercise execution, and automated adjustment of training plans. Special attention is given to the role of these technologies in obesity prevention among adults, as well as their potential for early detection of health risks. In addition to the literature review, the paper incorporates a practical case study conducted in Bijeljina, Bosnia and Herzegovina, where AI-integrated smart fitness equipment is already in use. This example illustrates the real-world implementation of such technologies and highlights their relevance for improving public health outcomes. Through a combined methodological approach, the paper examines actual and practical examples of successful applications of AI-powered fitness systems and discusses the challenges of their broader implementation within the context of public health.

This paper explores the transformative impact of digital technologies on the fitness industry, focusing specifically on the role of artificial intelligence (AI) in enhancing modern exercise equipment. By analyzing the needs of recreational users and professional athletes, it examines how AI-driven fitness devices optimize personalization, improve performance tracking, and elevate the overall user experience. Using G&S Premium Gym in Bijeljina—the first fitness center in Bosnia and Herzegovina to integrate AI-powered equipment—as a case study, the paper delves into the technical specifications, functionality, and user interaction with these advanced machines. Key findings reveal that AI technologies significantly enhance training efficiency and customization, contributing to measurable improvements in user satisfaction and physical performance. However, challenges persist, particularly regarding technology accessibility, user digital literacy, and data privacy concerns. The research highlights the potential for AI to redefine standards in recreation while addressing these challenges. Recommendations for future research and implementation emphasize the importance of affordable, user-friendly AI solutions and improved data security measures.

This paper explores the integration of computer vision technologies to enhance traffic safety through the effective detection of car-pedestrian interactions. As urban environments become more congested, pedestrian safety remains a critical concern. The system’s performance was evaluated using real-life footage from vehicle-mounted cameras, as well as images and videos sourced from online platforms. These real-world scenarios enabled a detailed assessment of the system’s accuracy and efficiency in practical conditions. The study highlights the potential for significant improvements in traffic safety, particularly in Bosnia and Herzegovina, where over 38% of registered vehicles are older than 23 years, and nearly 62% exceed 14 years. The aging vehicle fleet heightens the risk of accidents, underscoring the need for advanced detection methods. The proposed system automates the identification of hazardous situations on roads, allowing timely responses from relevant authorities.

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 becoming increasingly important in modern agriculture as it allows farmers to optimize production and increase yields. This includes the use of sensors and technologies to collect and analyze data on soil, crops, weather, and other relevant factors. However, existing technology still has limitations such as accuracy and coverage over large areas. In order to solve this, new sensors and technologies are being developed, especially those based on artificial intelligence and machine learning, which allow for greater accuracy in data collection. In addition, new technologies such as drones and satellite imagery are being used to map crops and optimize agricultural production. This paper analyzes some of the latest developments in precision agriculture, providing insight into the future development and application of this technology. This work is particularly relevant to farmers, researchers, and companies involved in the development of sensors and technologies for precision agriculture.

: 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.