Prerequisites for developing the computer vision system for drowning detection

This article delves into the cutting-edge methodologies revolutionizing database management systems (DBMS) through the lens of SQL query optimization, parallel processing, and the integration of graphics processing units (GPUs). As the digital world grapples with ever-increasing volumes of data, the efficiency, speed, and scalability of database systems have never been more critical. The first section of the article focuses on SQL query optimization, highlighting strategies to refine query performance and reduce resource consumption, thus enhancing application responsiveness and efficiency. The discourse then transitions to parallel processing in databases, an approach that leverages multiple processors or distributed systems to significantly boost data processing capabilities. This segment explores the advantages of parallelism in managing large datasets and complex operations, addressing the challenges and the impact on system scalability and fault tolerance. Furthermore, the article examines the innovative application of GPUs in database management, a development that offers profound speedups for analytical and machine learning tasks within DBMS. Despite the complexities and the initial investment required, the utilization of GPUs is portrayed as a game-changer in processing largescale data, thanks to their highly parallel architecture and computational prowess. Together, these advancements signify a transformative shift in database technologies, promising to address the challenges of modern data management with unprecedented efficiency and scalability. This article not only elucidates these sophisticated technologies but also provides a glimpse into the future of database systems, where optimization, parallel processing, and GPU integration play pivotal roles in navigating the data-driven demands of the contemporary digital landscape.

Enhancing database performance through SQL optimization, parallel processing and GPU integration

Nuriev,

Zaripova,

Sinicin

et al. 2024

Evolving network systems through blockchain innovation for smart agriculture IoT networks

Nuriev,

Aygumov,

Zaripova

et al. 2024

This article provides an in-depth analysis of how blockchain technology is driving innovation in network systems for smart agriculture IoT networks. It begins by exploring the foundational principles of blockchain, focusing on its decentralized nature, cryptographic security, and consensus mechanisms, which ensure data integrity and transparency in agricultural IoT ecosystems. The article then delves into the specific applications of blockchain within smart agriculture, such as enhancing supply chain transparency, improving data exchange between IoT devices, and optimizing resource distribution on farms. Challenges to blockchain adoption in this sector are also addressed, including scalability, performance issues, security concerns, and regulatory challenges unique to agriculture. To overcome these hurdles, the article discusses cutting-edge solutions like Layer 2 scaling, advanced cryptographic methods, and sustainable consensus models that can support large-scale IoT networks. Ultimately, the article envisions blockchain as a key enabler for evolving smart agriculture networks, driving increased efficiency, reliability, and security in IoTenabled farming operations, and paving the way for a more connected and sustainable agricultural future.

Machine learning insights into log files with ML.NET for real-time farm monitoring

Nuriev,

Zaripova,

Filimonova

et al. 2024

This article explores the application of machine learning principles for log file analysis in the context of real-time farm monitoring using the ML.NET framework. It begins with an overview of the significance of log files in agricultural systems, where monitoring data is critical for optimizing farm performance, detecting anomalies, and ensuring system reliability. The challenges posed by manual log analysis due to the increasing volume and complexity of farm data are addressed by introducing machine learning through ML.NET, which automates the process, enhancing farm security, operational efficiency, and predictive maintenance. The article outlines a structured approach starting with data preparation, where agricultural log data is selected and transformed for machine learning algorithms. It discusses the importance of model selection based on farmspecific tasks and data characteristics, followed by a detailed look at the training process to enhance model accuracy and effectiveness. Model evaluation is emphasized using metrics like accuracy, recall, and the F1 score to ensure its practical application in real-time farm monitoring. The article culminates in the deployment of the trained model for real-time analysis of farm logs, showcasing its benefits in anomaly detection, system optimization, and early error diagnosis in agricultural operations. This work highlights the iterative nature of machine learning projects and the continuous need for adaptation, offering a roadmap for applying ML.NET to revolutionize farm monitoring and management.