[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126073-en":3,"doc-seo-126073-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126073,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","ARTIFICIAL INTELLIGENCE FOR SYSTEMS ENGINEERING COMPLEXITY: A REVIEW ON THE USE OF AI AND MACHINE LEARNING ALGORITHMS","This review examines how Artificial Intelligence (AI) and Machine Learning (ML) address the complexity inherent in systems engineering. It describes AI/ML applications that transform system design, integration, and lifecycle management through automated design optimization, predictive maintenance, and more efficient configuration management. By analyzing large datasets, the approach supports failure prediction and performance improvement, strengthening reliability and sustainability. It also outlines integration challenges, including technical barriers, ethical issues, and the need for ongoing education and training. The paper concludes that AI can redefine systems engineering when opportunities and risks are balanced.","Computer Science & IT Research Journal, Volume 5, Issue 4, April 2024  \nOPEN ACCESS  \nComputer Science & IT Research Journal P-ISSN: 2709-0043, E-ISSN: 2709-0051 Volume 5, Issue 4, P.787-808, April 2024  \nDOI: 10.51594/csitrj.v5i4.1026  \nFair East Publishers [Journal Homepage:](Journal Homepage: www.fepbl.com/index.php/csitrj)[ ](Journal Homepage: www.fepbl.com/index.php/csitrj)[www.fepbl.com/index.php/csitrj](Journal Homepage: www.fepbl.com/index.php/csitrj)  \nARTIFICIAL INTELLIGENCE FOR SYSTEMS ENGINEERING COMPLEXITY: A REVIEW ON THE USE OF AI AND MACHINE  \nLEARNING ALGORITHMS  \nOladele Junior Adeyeye 1 & Ibrahim Akanbi2  \n1George Washington University,  \nWashington DC, USA.  \n2University of Pretoria, South Africa.  \n*Corresponding Author: Ibrahim Akanbi  \nCorresponding Author Email: [Ibrahimiakanbi@gmail.com](Ibrahimiakanbi@gmail.com)  \nArticle Received: 10-01-24 Accepted: 15-03-24 Published: 16-04-24  \nLicensing Details: Author retains the right of this article. The article is distributed under the terms of the  \nCreative Commons Attribution-NonCommercial 4.0 License  \n([http://www.creativecommons.org/licences/by-nc/4.0/](http://www.creativecommons.org/licences/by-nc/4.0/)) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the Journal open access page  \nABSTRACT  \nThis review examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in addressing the complexities of systems engineering. It highlights how AI and ML are revolutionizing system design, integration, and lifecycle management by enabling automated design optimization, predictive maintenance, and efficient configuration management. These technologies allow for the analysis of large datasets to predict system failures and optimize performance, thereby enhancing the reliability and sustainability of engineering systems. Despite the promising applications, the integration of AI into systems engineering presents challenges, including technical hurdles, ethical considerations, and the need for comprehensive education and training. The paper emphasizes the importance of interdisciplinary approaches and the continuous evolution of educational programs to equip engineers with the skills to leverage AI effectively.  \nConcluding thoughts underscore AI's potential to redefine systems engineering, advocating for a balanced approach that addresses both the opportunities and challenges presented by AI advancements.  \nKeywords: Artificial Intelligence, Machine Learning, Systems Engineering, Automated Design, Predictive Maintenance, Configuration Management, Education and Training, Technology Integration.  \nINTRODUCTION  \nOverview of Systems Engineering  \nSystems engineering is a multidisciplinary approach aimed at enabling the successful realization, deployment, and maintenance of complex systems. It integrates various engineering disciplinesand specialty groups into a cohesive effort to create and manage a structured development process that proceeds from concept to production to operation. The importance of systems engineering arises from its role in ensuring that all aspects of a system's lifecycle are considered and integrated, from the initial design and development through to its disposal, ensuring efficiency, reliability, and sustainability.  \nDefinition and importance  \nSystems engineering is defined as an interdisciplinary field of engineering that focuses on designing, integrating, and managing complex systems over their life cycles. At its core, systems engineering utilizes systems thinking principles to organize this body of knowledge. The objective is to achieve a successful system that satisfies the needs of its stakeholders, often within the constraints of cost, time, and technical performance (Blanchard & Fabrycky, 2010) . The importance of systems engineering lies in its comprehensive approach to solving engineering problems, considerin","cbCairt4E1LM0Z0o","https://ap.wps.com/l/cbCairt4E1LM0Z0o","pdf",268566,1,22,"English","en",105,"# Abstract\n# Introduction\n## Overview of Systems Engineering\n## Complexity in Systems Engineering: Sources and Impacts\n## Structural Complexity\n## Dynamic Complexity\n## Socio-technical Complexity\n## Role of Artificial Intelligence (AI)\n## Brief introduction to AI and machine learning (ML)","[{\"question\":\"How do AI and ML support systems engineering complexity according to the review?\",\"answer\":\"AI and ML enable automated design optimization, predictive maintenance, and efficient configuration management, helping systems engineering handle complex lifecycle needs and performance goals.\"},{\"question\":\"What types of complexity in systems engineering are discussed?\",\"answer\":\"The review categorizes complexity into structural, dynamic, and socio-technical complexity, each affecting integration, predictability, and human-technology interactions.\"},{\"question\":\"What challenges arise when integrating AI into systems engineering?\",\"answer\":\"The paper highlights technical hurdles, ethical considerations, and the need for comprehensive education and training so engineers can apply AI effectively.\"}]","ARTIFICIAL INTELLIGENCE FOR SYSTEMS ENGINEERING COMPLEXITY: A REVIEW ON THE USE OF AI AND MACHINE LEARNING ALGORITHMS | 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