Scientific Research Journal, Volume XII, Issue XI, November 2024 Edition

Scientific Research Journal

Scirj Volume XII, Issue XI, November 2024 Edition
ISSN: 2201-2796

Publication starts: 15th November 2024
Full Paper available from: 15th November 2024

Application of Machine Learning Methods to Enhance the Performance of Big Data Sorting Algorithms
Soli Maya Yacobovitch

Abstract: The use of machine learning methods to optimize big data sorting algorithms has become an urgent research topic due to the growing volume of information and the requirements for their rapid processing. Machine learning provides opportunities to automate and improve traditional sorting methods, allowing you to reduce the cost of computing resources and time. This is achieved by analyzing the characteristics of the data and preprocessing them using classification and regression. The main advantages of using machine learning in sorting big data include improving the accuracy and adaptability of algorithms to different types of data, which is especially important for areas with large amounts of information, such as finance, medicine and logistics. Progressive machine learning algorithms such as supporting vectors, decision trees, and gradient boosting demonstrate high efficiency and potential for further development and integration.

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Pharmacovigilance Challenges: Ensuring safety data integrity and ADR reporting by Investigators in randomized multi-center clinical trials
Tinatin Gogrichiani

Abstract: This study addresses the critical challenges in ensuring data integrity and adverse drug reaction (ADR) reporting within randomized multi-center clinical trials, focusing on the transformation of pharmacovigilance through digital technologies. The research methodology combines analysis of spontaneous reporting systems, electronic medical records (EMR), and mobile applications to evaluate their effectiveness in ADR monitoring. The findings reveal a tenfold increase in ADR reporting through mobile applications and significant improvements in data quality through EMR integration. The study demonstrates that standardization using ICD and ATC coding systems, combined with artificial intelligence methods, substantially enhances the detection of drug safety signals, with over 1,000 previously unknown drug-reaction associations identified. This research contributes to the field by establishing a comprehensive framework for integrating digital solutions in pharmacovigilance, providing evidence-based recommendations for improving ADR reporting in multi-center clinical trials, and highlighting the synergistic effects of combining different monitoring approaches for enhanced patient safety.

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Product Lifecycle Management in the Field of Green Energy: Product Management Approaches
Maryna Silchenko

Abstract: The article examines the approaches of product management in the process of product lifecycle management in the field of green energy. This, in turn, is due to the growth of the global green energy market, accompanied by stricter environmental requirements, actualized by the need to apply product lifecycle management (PLM) as a strategic tool in the product policy of companies. The main purpose of this study is to identify key product management techniques and approaches aimed at increasing sustainability and environmental responsibility at all stages of the product life cycle, including planning, design, production, operation and disposal. The study analyzes the best practices of market leaders such as Ørsted, Siemens Gamesa and Tesla, who actively use PLM to integrate digital solutions and the principles of a circular economy. The methodology is based on the study of data on the use of digital twin technologies, process automation and eco-design, which reduces the carbon footprint of products, minimizes production costs and increases resource efficiency. The results show that PLM not only supports innovative development, but also contributes to the creation of business models that comply with the principles of sustainable development. Thus, effective product lifecycle management allows companies in the green energy sector to achieve competitive advantages by implementing environmentally friendly technologies and ensuring adaptation to current and future challenges of the global market.

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