[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116889-en":3,"doc-seo-116889-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},116889,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Generic Architecture for Predictive Computational Modelling - Integration of Semantic Approach and Machine Learning - PhD Thesis","The PhD thesis presents a Generic Architecture for Predictive Computational Modelling that automates analytical conclusions for quantitative data structured as a data frame. The approach combines heterogeneous data mining using a semantic perspective, graph-based techniques such as ontologies and knowledge graphs, and advanced machine learning. Research efforts concentrate on data pre-processing and efficient input feature selection. Practical evaluations use financial and market datasets, including UK corporate risk analysis and FTSE100 forecasting, where results outperform stand-alone traditional machine learning methods.","WestminsterResearch  \n[http://www.westminster.ac.uk/westminsterresearch](http://www.westminster.ac.uk/westminsterresearch)  \nGeneric Architecture for Predictive Computational Modelling with Application to Financial Data Analysis: Integration of Semantic Approach and Machine Learning  \nYerashenia, Natalia  \nThis is a PhD thesis awarded by the University of Westminster.  \n© Miss Natalia Yerashenia, 2023.  \n[https://doi.org/10.34737/w4037](https://doi.org/10.34737/w4037)  \nThe WestminsterResearch online digital archive at the University of Westminster aims to make the research output of the University available to a wider audience. Copyright and Moral Rights remain with the authors and/or copyright owners.  \nPhD Thesis  \nGeneric Architecture for Predictive Computational Modelling with Application to Financial Data Analysis: Integration of Semantic Approach and Machine Learning  \nAuthor  \nNatalia Yerashenia  \nSupervisors Dr. Alexander Bolotov  \nDr. Fang He  \nJune 27, 2023  \nSchool of Computer Science and Engineering  \nABSTRACT  \nThe PhD thesis introduces a Generic Architecture for Predictive Computational Modelling capable of automating analytical conclusions regarding quantitative data structured as a data frame. The model involves heterogeneous data mining based on a semantic approach, graph-based methods (ontology, knowledge graphs, graph databases) and advanced machine learning methods. The main focus of my research is data pre-processing aimed at a more efficient selection of input features to the computational model.  \nSince the model I propose is generic, it can be applied for data mining of all quantitative datasets (containing two-dimensional, size-mutable, heterogeneous tabular data); however, it is best suitable for highly interconnected data.  \nTo adapt this generic model to a specific use case, an Ontology as the formal conceptual representation for the relevant domain knowledge is needed.  \nI have determined to use financial/market data for my use cases. In the course of practical experiments, the effectiveness of the PCM model application for the UK companies’ financial risk analysis and the FTSE100 market index forecasting was evaluated. The tests confirmed that the PCM model has more accurate outcomes than stand-alone traditional machine learning methods.  \nBy critically evaluating this architecture, I proved its validity and suggested directions for future research.  \nTo my dearest grandpa, Anatoly Shevelev  \nAcknowledgements  \nReflecting upon this doctoral journey, I realise it was not a solitary pursuit. It was enriched and made possible by a collective of exceptional individuals with unwavering support and encouragement.  \nFirst and foremost, I owe a debt of gratitude to my primary supervisor, Dr Alexander Bolotov. His unwavering faith in my abilities and his readiness to guide me at any time, often beyond conventional working hours, has been a beacon throughout this journey. His wisdom, patience, and moral support have been the driving force behind this work; I am forever grateful for this.  \nI am equally thankful for the insightful guidance provided by my secondary supervisor, Dr Fang He, during the early stages of my project. Her valuable inputs shaped the trajectory of my research.  \nSpecial thanks to David Chan You Fee, whose technical expertise and patience have been invaluable during our numerous online meetings. His dedication and generosity in sharing his coding skills during the COVID-19 lockdowns have been nothing short of exemplary.  \nMy heartfelt appreciation extends to Dr Gabriele Pierantoni, who has been a professional colleague and a dear friend. His valuable advice and steadfast support throughout the project were invaluable. His camaraderie has made the journey all the more pleasant and enriching.  \nI express my warmest gratitude to all my colleagues at the School of Computer Science and Engineering. You’ve provided a nurturing environment fostering my professional growth and well-being. Your","cbCaieIG5kpwsVYb","https://ap.wps.com/l/cbCaieIG5kpwsVYb","pdf",31120814,1,136,"English","en",105,"# Introduction\n## Research Aims\n## Research Objectives\n## Original Contribution to Knowledge\n## Publications Resulting from the Thesis\n# Literature Review\n## Data-Preprocessing in Data Mining\n## Feature Selection Methods Overview\n## Semantic Approach to Data Pre-processing\n## Ontologies as a part of Semantic Approach to Data Analysis\n## Graph Databases Overview\n## Computational Methods in Financial Analysis and Forecasting\n## Python as a Machine Learning Engine Development Environment\n# A Generic Architecture in Software Engineering and the Process of its Development","[{\"question\":\"What does the proposed Generic Architecture aim to automate?\",\"answer\":\"It automates analytical conclusions for quantitative data structured as a data frame through predictive computational modelling.\"},{\"question\":\"Which techniques are integrated in the model?\",\"answer\":\"The architecture integrates semantic-based heterogeneous data mining, graph-based methods (ontologies, knowledge graphs, graph databases), and advanced machine learning.\"},{\"question\":\"How was the architecture validated in the thesis?\",\"answer\":\"Financial and market data were used to evaluate UK companies’ financial risk analysis and FTSE100 index forecasting, showing more accurate results than stand-alone traditional machine learning methods.\"}]","Generic Architecture for Predictive Computational Modelling - 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