Journal Articles

Permanent URI for this collectionhttp://10.0.100.92:4000/handle/123456789/21

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An Evaluation Framework for Machine Learning and Data Science (ML/DS) Based Financial Strategies: A Case Study Driven Decision Model.
(IEEE Transactions on Engineering Management, Engineering Management, 2025) Saadatmand, M.; Daim, T. Mena,; C. Yalcin, H.; Bolatan, G.; Chatterjee, M
Big data and computational technologies are increasingly important worldwide in asset and investment management. Many investment management firms are adopting these data science (DS) methods and technologies to improve performance across all investment processes. A good question is whether we can make better decisions in developing quantitative strategies. Therefore, the main objective of this research was to develop a multicriteria assessment framework and scoring decision support system to evaluate quantitative investment strategies that apply machine learning (ML) and DS techniques in their research and development. Subject matter experts will assess all framework perspectives from a systematic literature review to approve their reliability. The perspectives consist of economic and financial foundations , data perspective , features perspective , modeling perspective , and performance perspective . The research methodology applied was the hierarchical decision model (HDM) to provide a 360° view of the quantitative investment strategy and improve and generalize the concept to other asset classes and regions. This study accomplished a rigorous integration of an extensive literature review connecting DS, ML, and investment decision-making in developing quantitative investment strategies. As a result, the major contribution of this study is the comprehensive examination, which included identifying and quantifying perspectives and criteria. The results, while limited indicated significant gaps in strategies examined and therefore generated critical knowledge to improve ML/DS-driven investment strategies, which are valuable for financial companies and policymakers.
Artificial Intelligence-Based Cybersecurity for the Metaverse: Research Challenges and Opportunities
(2025-04) Awadallah, Abeer; Eledlebi, Khouloud; Zemerly, Mohamed Jamal; Puthal, Deepak; Damiani, Ernesto; Taha, Kamal
The metaverse, known as the next-generation 3D Internet, represents virtual environments that mirror the physical world. It is supported by innovative technologies such as digital twins and extended reality (XR), which elevate user experiences across various fields. However, the metaverse also introduces significant cybersecurity and privacy challenges that remain underexplored. Due to its complex multi-tech infrastructure, the metaverse requires sophisticated, automated, and intelligent cybersecurity measures to mitigate emerging threats effectively. Therefore, this paper is the first to explore Artificial Intelligence (AI)-driven cybersecurity techniques for the metaverse, examining academic and industrial perspectives. First, we provide an overview of the metaverse, presenting a detailed system model, diverse use cases, and insights into its current industrial status. We then present attack models and cybersecurity threats derived from the unique characteristics and technologies of the metaverse. Next, we review AI-driven cybersecurity solutions based on three critical aspects: User authentication, intrusion detection systems (IDS), and the security of digital assets, specifically for Blockchain and Non-fungible Tokens (NFTs). Finally, we highlight challenges and suggest future research opportunities to enhance metaverse security, privacy, and digital asset transactions
Technology Landscape Analysis: Metaverse
(IEEE Engineering Management Review, 2025-06) Chatterjee, M; Saadatmand, M; Daim, T.
The metaverse is emerging as a focal point of interest for technology enthusiasts, which is seamlessly integrating various technologies to create immersive augmented reality experiences and establish a seamless connection between the physical and virtual worlds. Diverse industries and sectors are increasingly adopting the metaverse, while a growing interest among academic scholars across different fields reflects a strong enthusiasm for exploring its potential. This study examines the metaverse technology landscape to illuminate its trends and active networks involving countries, institutions, researchers, and key domains. By following a methodical procedure, we have drawn a sample of 2451 papers from the Scopus database. Employing social network analysis integrated with domain-specific cluster-based assessments, the study provides a nuanced understanding of the dynamic evolution within metaverse research. Taking a multidisciplinary approach, the literature review offers a comprehensive overview of the latest trends, covering technological advancements and diverse dimensions within social sciences and management studies associated with the evolving metaverse technology landscape. Ultimately, this study serves as a roadmap to comprehend the latest trends in the metaverse landscape that help researchers and practitioners in navigating this evolving digital frontier with insight and clarity.