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Air pollution is a growing concern due to severe threats to public health and the environment. The need for reliable air quality monitoring solutions has never been more critical. This research paper introduces an innovative approach to addressing this challenge by deploying a low-cost Internet of Things (IoT) air monitoring station and providing a blockchain technology solution to enhance environmental data transparency, reliability, and accessibility. Our paper adopts a concept of merging IoT and blockchain technologies and collecting some parameters that help to assess air quality by using three sensors, DHT11, MQ7, and MQ135, to collect temperature, humidity, carbon monoxide, and carbon dioxide parameters, respectively, to measure the gases and thus indicate the air quality within the surrounding area. Collecting and sharing these types of valuable data will be very important for various stakeholders, such as governmental bodies, researchers, and the public. This approach is consistent with the principles of sustainable development, facilitating informed decision-making and promoting eco-friendly policies. This research explores the technical architecture of the IoT air monitoring stations, offering a promising solution for addressing air pollution concerns while promoting sustainable development goals. The proposed system is a model for leveraging emerging technologies to advance environmental monitoring and create smarter, livable cities. This approach aligns with the principles of sustainable development and eco-friendly initiatives. This research offers a promising model for enhancing environmental monitoring efforts and advancing the creation of smarter, more sustainable urban environments. The proposed IoT, cloud platform and blockchain-based system not only addresses pressing air pollution challenges but also sets a benchmark for leveraging emerging technologies in environmental science. [ABSTRACT FROM AUTHOR]

Introduction: In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures. Methodology: In this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO. Results: The proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. Conclusions: The results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods. [ABSTRACT FROM AUTHOR]

Sports video analysis has received much attention as it turned to be a hot research area in the field of image processing. This motivation offers opportunities that develop fascinating applications supported by analysis of different sports, especially soccer. Ball identification, in soccer images, is an essential task not only for goal‐scoring but also for performance evaluation. However, ball detection suffers from several hurdles such as occlusions, fast‐moving objects, shadows, poor lighting, color contrast, and other static background objects. Although several ball detection techniques have been introduced such as Frame Difference, Mixture of Gaussian (MoG), Optical Flow, and so forth; ball detection in soccer games is still an open research area. In this paper, a new Fuzzy Based Ball Detection (FB2D) strategy is proposed for identifying the ball through a set of image sequences extracted from a soccer match video. FB2D can accurately identify the ball even if it is attached to the white lines drawn on the playground or partially occluded behind players. FB2D is compared to recent ball detection techniques. Experimental results show that FB2D outperforms recent detection techniques as it introduces both the highest level of detection accuracy in the testing stage and the lowest possible error. [ABSTRACT FROM AUTHOR]

Soccer is the most popular group game; it has a large number of fans all over the world. Although each complete soccer match lasts around one hour and a half, only have very few scenes attracting audiences been perfectly filmed. This filming has usually been done using static (fixed) cameras, and they cannot provide the same level of accuracy or entertainment given by mobile devices. Taking into our consideration that there are no schemes using mobile devices have been proposed to solve this problem till now. As periodic communication generates a huge amount of data; typical storage, computation, and communication resources are required. Hence, this paper aims at introducing a Fog computing mechanism that takes into consideration the requitrements of special mobility, low latency and location awareness. Our solution based on controlling the movement of mobile cameras mounted on a fleet of mobile bees. A bee is an autonomous camera-drone, which gives the audience the feeling of being a part of such sports competition. It is a special type of Unmanned Aerial Vehicle (UAV) equipped with a built-in processing unit, memory, high-speed camera and transceiver. The bee has the ability to film the movement of the ball, which has to be followed and filmed via object tracking principle. In fact, detecting and tracking the ball from the broadcast soccer video constitute a major challenge. In soccer matches, the ball moves most of the time and it is frequently occluded while its size and shape appearance vary over the time and between cameras. Moreover, the feature-based tracking methods are used to judge whether or not a sole object is the target as the features of the ball might be changed fast over frames and then we cannot manage to distinguish the ball from other objects by using these methods. Thus, the current study demonstrates an innovative technique for tracking a soccer ball from mobile cameras fixed on multiple ball bees. [ABSTRACT FROM AUTHOR]