[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126414-en":3,"doc-seo-126414-105":31,"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126414,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Early Detection of Emerging Technologies Using Machine Learning and Burst Detection - Thesis","Emerging technologies reshape the future but their early detection remains difficult and costly in a landscape of continuous investment. Stakeholders often struggle to identify promising trends efficiently with limited manual effort. This thesis presents a predictive method for potential emerging technologies in the Artificial Intelligence (AI) case study field, combining burst detection with machine learning and deep learning to estimate future sustaining technologies. Four models (Random Forest, Gradient Boosting, XGBoost, and MLP) achieve strong performance, with AUC above 75%, and MLP improving key metrics such as AUC and recall.","Early Detection of Emerging Technologies Using Machine Learning and  \nBurst Detection  \nAli Ghaemmaghami  \nA Thesis  \nIn the Department of  \nConcordia Institute for Information Systems Engineering  \nPresented in Partial Fulfillment of the Requirements For the Degree of Master of Applied Science in Quality Systems Engineering  \nat Concordia University  \nMontréal, Quebec, Canada  \nNovember 2024  \n© Ali Ghaemmaghami, 2024  \nCONCORDIA UNIVERSITY SCHOOL OF GRADUATE STUDIES  \nThis is to certify that the thesis prepared  \nBy: Ali Ghaemmaghami  \nEntitled: Early Detection of Emerging Technologies Using Machine Learning and Burst  \nDetection  \nand submitted in partial fulfillment of the requirements for the degree of  \nMaster of Applied Science (Quality Systems Engineering)  \ncomplies with the regulations of the University and meets the accepted standards with respect to originality and quality.  \nSigned by the final examining committee:  \n  Chair  \nDr. Yong Zeng  \n  Examiner  \nDr. Yong Zeng  \n  Examiner  \nDr. Arash Mohammadi  \n  Thesis Supervisor Dr. Andrea Schiffauerova  \n  Thesis co-supervisor Dr. Ashkan Ebadi  \nApproved by  \nDr. Farnoosh Naderkhani, Graduate Program Director  \nDepartment of Concordia Institute for Information Systems Engineering  \nYear 2024  \nDr. Mourad Debbabi, Dean  \nFaculty of Engineering and Computer Science  \nABSTRACT  \nEarly Detection of Emerging Technologies Using Machine Learning and Burst Detection  \nAli Ghaemmaghami  \nCertainly, the impact of emerging technologies is changing our world and how we live, shaping our future significantly. In the constantly evolving landscape of these technologies, which attracts substantial yearly investments, spotting these trends early on is both challenging and expensive. However, applying an emerging technology detection method in an effective and efficient way is considered a challenging task for many stakeholders. In this thesis, we address these problems through applying a method to predict potential emerging technologies in the case study field of Artificial Intelligence (AI). Using this method may help policymakers to identify potential emerging technologies early in amore systematic way with little manual intervention. In the proposed method, using burst detection, machine learning, and deep learning, we attempt to predict the future sustaining emerging technologies. We applied the methodology by four methods, namely Random Forest, Gradient Boosting, XGBoost, and Multi-Layer Perceptron (MLP) . Results showed that the method was successful in its tasks. The method had the Area under the Curve (AUC) rate of more than 75% to accurately predict the sustainability of the potential emerging technologies. More specifically, applying the MLP method showed the ability to increase the AUC rate and recall metric as the most important metrics ofour work. In summary, this approach carries both theoretical and practical significance. Theoretically, the exploration of novel combinations, such as integrating deep learning and burst detection methods or employing transformers, offers researchers fresh insights into the challenge of detecting emergence. On the practical front, the application of methods providing high accuracy rates in machine learning methods empowers stakeholders to implement these methods effectively in practical scenarios.  \nACKNOWLEDGMENT  \nI am immensely grateful to all those who have contributed to the completion of this Master's thesis, and I would like to take this opportunity to express my heartfelt appreciation to the following individuals:  \nFirst and foremost, I express my deepest gratitude to my supervisor Dr. Ashkan Ebadi and Professor Andrea Schiffauerova. Their unwavering support, guidance, and encouragement throughout this research journey have been invaluable. Their expertise, insightful feedback, and constructive criticism have played a pivotal role in shaping this thesis and refining my understanding of the subject matter.  \nTo my loving parents,","cbCaihrOnRKcvsaW","https://ap.wps.com/l/cbCaihrOnRKcvsaW","pdf",1412625,2,1,66,"English","en",105,"# 1 Introduction\n## 1.1 Background and Motivation\n## 1.2 Research Objectives\n# 2 Literature Review\n## 2.1 Definitions of Emerging Technologies\n## 2.2 Approaches to Detec","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"It addresses the challenge of spotting emerging technologies early in an effective and efficient way despite high cost and difficulty for stakeholders.\"},{\"question\":\"How does the proposed method detect emerging technologies?\",\"answer\":\"It uses burst detection together with machine learning and deep learning to predict future sustaining emerging technologies in an AI case study field.\"},{\"question\":\"Which models are evaluated and what performance is achieved?\",\"answer\":\"The work evaluates Random Forest, Gradient Boosting, XGBoost, and Multi-Layer Perceptron (MLP). Results show AUC rates above 75%, and MLP improves AUC and recall as key metrics.\"}]","Early Detection of Emerging Technologies Using Machine Learning and Burst Detection - Thesis | PDF",1785904931,166,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"early-detection-of-emerging-technologies-using-machine-learning-and-burst-detection-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/early-detection-of-emerging-technologies-using-machine-learning-and-burst-detection-thesis/126414/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the thesis address?","Question",{"text":76,"@type":77},"It addresses the challenge of spotting emerging technologies early in an effective and efficient way despite high cost and difficulty for stakeholders.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed method detect emerging technologies?",{"text":81,"@type":77},"It uses burst detection together with machine learning and deep learning to predict future sustaining emerging technologies in an AI case study field.",{"name":83,"@type":74,"acceptedAnswer":84},"Which models are evaluated and what performance is achieved?",{"text":85,"@type":77},"The work evaluates Random Forest, Gradient Boosting, XGBoost, and Multi-Layer Perceptron (MLP). 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