Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
The digital landscape is undergoing a seismic shift, moving beyond the era of simple data storage into an age where the true value of information lies in its ability to forecast the future. AI-Powered Predictive Analytics for Intelligent Decision Support Systems is designed as a comprehensive guide to navigating this transition, blending the technical rigor of machine learning with the pragmatic needs of modern industrial and academic decision-making. In an increasingly complex world, the scope of global systems—spanning healthcare, finance, and manufacturing—has begun to exceed the limits of unassisted human intuition. This book explores the vital synergy between Artificial Intelligence (AI) and Decision Support Systems (DSS), illustrating how predictive modeling transforms raw, historical data into a strategic asset that anticipates trends, mitigates risks, and optimizes outcomes in real-time. While many existing texts focus solely on the mathematical algorithms of machine learning or the administrative management of information systems, this work bridges the gap by covering the entire system lifecycle. It takes the reader on a structured journey from the foundational theories of predictive analytics to the cutting edge of autonomous decision systems. Throughout these chapters, we delve into the intricate nuances of feature engineering, data governance, and the deployment of models within modern MLOps frameworks. Furthermore, the book provides deep technical explorations of supervised learning, ensemble methods, and deep learning architectures like CNNs and LSTMs, while placing a heavy emphasis on Explainable AI (XAI) to ensure that automated decisions remain transparent and trustworthy. The theory presented in these pages is anchored by extensive case studies that reflect both global and regional perspectives, providing a balanced view of how these technologies are implemented across diverse economic and regulatory environments. As we move toward a future of fully autonomous systems, the book addresses the critical challenges of algorithmic bias, data privacy, and the indispensable role of human-AI collaboration. This text is intended for researchers, data scientists, and business leaders alike—anyone who seeks to understand the strategic implications and technical requirements of building systems that are not only intelligent and efficient but also ethical and transparent. It is our hope that this roadmap serves as a vital resource for those looking to harness the analytical power of machines to solve the most pressing challenges of our time.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
Various recent Artificial Intelligence (AI) system failures, some of which have made the global headlines, have highlighted issues in these systems. These failures have resulted in calls for more ethical AI systems that better take into account their effects on various stakeholders. However, implementing AI ethics into practice is still an on-going challenge. High-level guidelines for doing so exist, devised by governments and private organizations alike, but lack practicality for developers. To address this issue, in this paper, we present a method for implementing AI ethics. The method, ECCOLA, has been iteratively developed using a cyclical action design research approach. The method aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
In this paper, we present a novel approach to improving software quality and efficiency through a Large Language Model (LLM)-based model designed to review code and identify potential issues. Our proposed LLM-based AI agent model is trained on large code repositories. This training includes code reviews, bug reports, and documentation of best practices. It aims to detect code smells, identify potential bugs, provide suggestions for improvement, and optimize the code. Unlike traditional static code analysis tools, our LLM-based AI agent has the ability to predict future potential risks in the code. This supports a dual goal of improving code quality and enhancing developer education by encouraging a deeper understanding of best practices and efficient coding techniques. Furthermore, we explore the model's effectiveness in suggesting improvements that significantly reduce post-release bugs and enhance code review processes, as evidenced by an analysis of developer sentiment toward LLM feedback. For future work, we aim to assess the accuracy and efficiency of LLM-generated documentation updates in comparison to manual methods. This will involve an empirical study focusing on manually conducted code reviews to identify code smells and bugs, alongside an evaluation of best practice documentation, augmented by insights from developer discussions and code reviews. Our goal is to not only refine the accuracy of our LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
Progress in the field of artificial intelligence has been accelerating rapidly in the past two decades. Various autonomous systems from purely digital ones to autonomous vehicles are being developed and deployed out on the field. As these systems exert a growing impact on society, ethics in relation to artificial intelligence and autonomous systems have recently seen growing attention among the academia. However, the current literature on the topic has focused almost exclusively on theory and more specifically on conceptualization in the area. To widen the body of knowledge in the area, we conduct an empirical study on the current state of practice in artificial intelligence ethics. We do so by means of a multiple case study of five case companies, the results of which indicate a gap between research and practice in the area. Based on our findings we propose ways to tackle the gap.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6
In agile software development, maintaining high-quality user stories is crucial, but also challenging. This study explores the use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams. We developed a reference model for an Autonomous LLM-based Agent System and implemented it at the company. The quality of user stories in the study and the effectiveness of these agents for user story quality improvement was assessed by 11 participants across six agile teams. Our findings demonstrate the potential of LLMs in improving user story quality, contributing to the research on AI role in agile development, and providing a practical example of the transformative impact of AI in an industry setting.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.
