Journal of Intelligent Decision Technologies and Applications (e-ISSN: 3049-0219)

Neuro-Symbolic AI: Foundations and Frontiers

2025-09-24T10:06:22+00:00

The field of Artificial Intelligence (AI) is currently in its "third AI summer," marked by significant advancements and the emergence of Neuro-Symbolic AI (NSAI). Traditional AI paradigms, such as Symbolic AI and Sub-Symbolic (deep learning), face distinct limitations: Symbolic AI struggles with real-world noise, while deep learning often lacks reasoning, interpretability, and robustness, leading to concerns about computational sustainability and limited human-AI collaboration. NSAI aims to bridge these divides by integrating logical reasoning with neural networks, fostering systems with enhanced explainability, trustworthiness, and data efficiency, ultimately striving for human-like cognitive capabilities.

This systematic review synthesizes NSAI progress from 2020-2024, highlighting key developments, methodologies, and applications, with a particular focus on healthcare. Findings indicate research concentration in learning and inference, logic and reasoning, and knowledge representation. However, critical gaps persist in explainability, trustworthiness, and especially Meta-Cognition, which is essential for self-monitoring and adaptation. Addressing these challenges, including knowledge representation complexity and the absence of standardized benchmarks, will be crucial. Advancing NSAI promises more autonomous, adaptable, reliable, and context-aware AI systems, with profound implications for areas like drug discovery and patient care.

Survey on Integrating AI and Data Mining Functionalities into a Single System using DSS

2025-10-01T10:36:37+00:00

In this contemporary modality of dynamic information, companies are generating information in mammoth quantities each and every day. To be able to convert this raw data into something useful, we must be able to convert it to actionable items. Decision-makers increasingly use intelligent systems with information. The Decision Support Systems (DSS) have an extended history of assistance in complex decisions, however the traditional DSS tend to be a fixed program that is incapable of learning to cope with the dynamic scenario. The difference lies in the gapfilled with the integration of Artificial Intelligence (AI) and Data Mining into a single DSS framework. AIbrings in strong reasoning, forecasting, and automation as opposed to Data Mining which exposesthe unclear patterns and trends. All of them, taken together, lead to more responsive, self-developingand foresighted decision support. The present paper is a review of the literature available on the fusion of such technologies in contemporary DSS. It discusses the lead integration models, methods, and patterns, which have been proposed in the new sources. Such use cases includesuch aspects as healthcare, finance, and supply chain management are mentioned. The review has been as data quality, scalability and user trust is critically commentedhelpful in guiding the future direction through the identification of open problems and future trends.