This paper provides a comprehensive analysis of recent developments in face recognition, tracking, identification, and person detection technologies, highlighting the benefits and drawbacks of the available techniques. To assess the state-of-art in these domains, we reviewed more than one hundred eminent journal articles focusing on current trends and research gaps in machine learning and deep learning methods. A systematic review using the PRISMA method helped us to generalize the search for the most relevant articles in this area. Based on our screening and evaluation procedures, we found and examined 142 relevant papers, evaluating their reporting compliance, sufficiency, and methodological quality. Our findings highlight essential methods of person detection, tracking and identification, and face recognition tasks, emphasizing current trends and illustrating a clear transition from classical to deep learning methods with existing datasets, divided by task and including statistics for each of them. As a result of this comprehensive review, we agree that the results demonstrate notable improvements. Still, there remain several key challenges like refining model robustness under varying environmental conditions, including diverse lighting and occlusion; adaptation to different camera angles; and ethical and legal issues related to privacy rights.

This study considers the creation of a network of moss-based biotechnological purification filters under the Smart City concept. The extent of the absorption of heavy metals and gases by Sphagnopsida moss under different conditions was investigated. The efficiency of air purification with biotechnological filters was also investigated in the city of Almaty, Republic of Kazakhstan, where an excess of the permissible concentration of harmful substances in the air, according to the WHO air quality guidelines, is recorded throughout the year. Data on the level of pollution recorded with sensors located in the largest Kazakhstani cities from 21 June 2020 to 4 June 2023 were selected as the basis for calculating purification efficiency. In total, there are 220 in 73 settlements of the Republic of Kazakhstan, with 80 such sensors located in the city of Almaty. Since creating a single biotechnological filter is expensive, our task was to calculate the air purification effect in the case of increasing the number of filters placed in polluted areas. We show that 10 filters provide an air purification efficiency of 0.77%, with 100 filters providing an air purification efficiency of 5.72% and 500 filters providing an air purification efficiency of 23.11%. A biotechnological filter for air purification based on moss was designed at Astana IT University by taking into consideration the climatic features, distribution, and types of pollution in the Republic of Kazakhstan. The obtained results are essential for ensuring compliance with the standard for environmental comfort in the Republic of Kazakhstan. Additionally, the research findings and the experience of implementing a moss-based biotechnological filter can be applied to designing similar air purification systems in other cities. This is of great importance for the advancement of the field of urban science.

The paper critically reviews face recognition models that are based on deep learning, specifically security and surveillance. Existing systems are susceptible to pose variation, occlusion, low resolution and even aging, even though they perform quite well under controlled conditions. The authors make a systematic review of four state-of-the-art architectures—FaceNet, ArcFace, OpenFace and SFace—through the use of five benchmark datasets, namely LFW, CPLFW, CALFW, AgeDB-30 and QMUL-SurvFace. The measures of performance are evaluated as the area under the receiver operating characteristic (ROC-AUC), accuracy, precision and F1-score. The results reflect that FaceNet and ArcFace achieve the highest accuracy under well-lit and frontal settings; when comparing SFace, this proved to have better robustness to degraded and low-resolution surveillance images. This shows the weaknesses of traditional embedding methods because bigger data sizes reduce the performance of OpenFace with all of the datasets. These results underscore the main point of this study: a comparative study of the models in difficult real life conditions and the observation of the trade-off between generalization and specialization inherent to any models. Specifically, the ArcFace and FaceNet models are optimized to perform well in constrained settings and SFace in the wild ones. This means that the selection of models must be closely monitored with respect to deployment contexts, and future studies should focus on the study of architectures that maintain performance even with fluctuating conditions in the form of the hybrid architectures.

Accurate crowd density estimation is essential for public safety operations, urban and transportation design and multiple intelligent systems. The research presents an improved Multi-Column Convolutional Neural Network (MC-CNN) structure to predict crowd density. The model uses MobileNet transfer learning capabilities and implements three parallel convolutional columns with different kernel sizes to extract spatial information at different receptive field levels. The design enhances the ability to extract and combine multi-scale features, resulting in better spatial crowd density representation. The model incorporates the output of its columns to improve its understanding of crowd movement patterns. During training, a dynamic collation and zero-padding strategy is introduced to handle variable-sized input images. This approach’s uniform batch shape improves training stability and allows flexible real-world deployment with inconsistent input resolutions. The experimental results show that the proposed architecture performs better, as revealed by its low MAE scores of 12.93 in Part A and 28.56 in Part B of the ShanghaiTech data, outperforming TransCrowd, CSRNet, and SANet. The research provides a functional solution that accurately measures crowd density in different scenarios while overcoming the existing approaches’ restrictions. It fills the academic research gaps in multi-scale feature extraction, efficient data collation and offers new solutions for crowd analysis.

This scientific article presents an innovative approach to optimizing routes of high-altitude unmanned aerial vehicles (UAVs) for effective monitoring of smart cities. The main proposed method is based on the ant colony optimization (ACO) algorithm with the implementation of an inverse pheromone mechanism—a repulsion-based exploration strategy. Unlike standard pheromones that encourage exploitation of frequently visited paths, this approach promotes exploration of unvisited areas by repelling UAVs from undesirable route sections, allowing UAVs to adapt more efficiently to dynamic changes in the urban environment. The authors developed a simulation system in the Webots environment, taking into account numerous factors: atmospheric conditions at high altitudes, potential for improved energy efficiency, urban development features, and priority of observation zones. Simulation results demonstrate that the proposed algorithm using inverse pheromones provides more effective area coverage compared to traditional route-planning methods, which may contribute to reduced UAV energy consumption and optimizing the monitoring process in real time. The research makes a significant contribution to the development of smart city technologies, offering a solution that can be integrated with existing urban monitoring systems to improve the efficiency of urban infrastructure observation, enhance security, and optimize urban resource management.

This study presents an in-depth analysis of air quality in Astana, Kazakhstan, utilizing both mobile and stationary air monitoring systems over a two-year period. The research focuses on tracking key air pollutants, namely carbon monoxide (CO), nitrogen dioxide (NO₂), particulate matter (PM2.5 and PM10), and sulfur dioxide (SO₂), providing a comparative assessment of seasonal trends and the sources of pollution, which include transportation, industrial emissions, and domestic heating during the cold season. The study emphasizes the significance of monitoring systems in urban environments to understand better the impact of air pollution on public health and the effectiveness of sustainable interventions. One of the major insights from this research is the comparison between seasonal variations in pollutant levels and the city's transition toward sustainable energy practices, such as increased gasification and the use of electric transportation, which has already demonstrated a positive impact on reducing emissions during peak heating periods. The results show that while Astana has improved air quality, air pollution remains a concern, especially in winter due to the increased use of solid fuel. This paper emphasizes the importance of real-time data from mobile sensors and suggests their wider use to complement stationary sensors for better monitoring. In addition to pollutant tracking, the study delves into the health implications of prolonged exposure to air pollutants, particularly in urban areas. The study concludes by advocating for expanded use of mobile monitoring systems and advanced data analytics to provide actionable insights for policymakers, urban planners, and public health officials.