artificial intelligence

AI-POWERED PREDICTIVE ANALYTICS FOR INTELLIGENT DECISION SUPPORT SYSTEMS

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.

A Stability-Theoretic Foundation of Artificial General Intelligence

AbstractThis work proposes a formal foundation for Artificial General Intelligence (AGI) basedon stability theory and geometric control. We introduce a framework in whichintelligence is defined not as optimization of reward, imitation of cognition, or pursuitof stimulus variation, but as the capacity of a system to preserve viable trajectoriesunder conditions of dynamic instability and environmental uncertainty.The central object of the theory is an instability functional (\zeta(S)) defined over astate manifold (\mathcal{M}), together with a dimensionless stability invariant:[G(S) = \frac{\gamma}{\alpha C + D},]where (\gamma) denotes adaptive capacity, (C) structural complexity, (\alpha) systemload, and (D) accumulated structural deviation. The condition (G(S) \ge 1) defines theadmissible domain of operation, while (G(S) < 1) characterizes collapse-prone regimes.Within this framework, intelligence is formalized as a control process:[u(t) = -k \nabla \zeta(S(t)),]which enables the system to maintain trajectories inside the stability domain despiteperturbations. We demonstrate that behaviors commonly associated with intelligentsystems—adaptation, exploration, learning, and generation of new structuredstates—emerge as consequences of stability-constrained motion in the state space,rather than as primary objectives.In particular, we show that the drive toward state-space expansion and variation is notfundamental, but arises as a secondary effect of navigating instability gradients underbounded control resources. This result unifies previously disconnected paradigms,including reward-based learning, unsupervised adaptation, and stimulus-drivenbehavioral variation, as restricted regimes within a broader stability-theoretic structure. The proposed framework provides a rigorous definition of AGI as a system capable ofsustaining admissible trajectories across open-ended environments. It furtherestablishes explicit conditions for collapse, limits of controllability, and measurableindicators of system viability. These results position stability-preserving control as thefundamental principle underlying general intelligence across artificial, biological, andsocio-technical systems.

A Stability-Theoretic Foundation of Artificial General Intelligence

AbstractThis work proposes a formal foundation for Artificial General Intelligence (AGI) basedon stability theory and geometric control. We introduce a framework in whichintelligence is defined not as optimization of reward, imitation of cognition, or pursuitof stimulus variation, but as the capacity of a system to preserve viable trajectoriesunder conditions of dynamic instability and environmental uncertainty.The central object of the theory is an instability functional (\zeta(S)) defined over astate manifold (\mathcal{M}), together with a dimensionless stability invariant:[G(S) = \frac{\gamma}{\alpha C + D},]where (\gamma) denotes adaptive capacity, (C) structural complexity, (\alpha) systemload, and (D) accumulated structural deviation. The condition (G(S) \ge 1) defines theadmissible domain of operation, while (G(S) < 1) characterizes collapse-prone regimes.Within this framework, intelligence is formalized as a control process:[u(t) = -k \nabla \zeta(S(t)),]which enables the system to maintain trajectories inside the stability domain despiteperturbations. We demonstrate that behaviors commonly associated with intelligentsystems—adaptation, exploration, learning, and generation of new structuredstates—emerge as consequences of stability-constrained motion in the state space,rather than as primary objectives.In particular, we show that the drive toward state-space expansion and variation is notfundamental, but arises as a secondary effect of navigating instability gradients underbounded control resources. This result unifies previously disconnected paradigms,including reward-based learning, unsupervised adaptation, and stimulus-drivenbehavioral variation, as restricted regimes within a broader stability-theoretic structure. The proposed framework provides a rigorous definition of AGI as a system capable ofsustaining admissible trajectories across open-ended environments. It furtherestablishes explicit conditions for collapse, limits of controllability, and measurableindicators of system viability. These results position stability-preserving control as thefundamental principle underlying general intelligence across artificial, biological, andsocio-technical systems.

Explainable artificial intelligence for plant disease detection: a scoping review of methods, datasets, and evaluation practices

How generative artificial intelligence reshapes students’ interdisciplinary cognitive structures: Implications for management education

Training Without Gathering the Data: A Interdisciplinary Mapping Review of Federated Learning and Privacy-Preserving AI

This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of federated learning and privacy as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.

Training Without Gathering the Data: A Interdisciplinary Mapping Review of Federated Learning and Privacy-Preserving AI

This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of federated learning and privacy as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.

MATHEMATICAL FOUNDATIONS OF AI-ASSISTED AUTOMATED TEXTURING OF 3D OBJECTS

Abstract The article explores the mathematical foundations and architectural solutions for developing an automated texturing system for three-dimensional objects using artificial intelligence. It analyzes the limitations of classical UV mapping and justifies the use of a projection mapping method combined with the developed "Smart Stencil" algorithm to eliminate visual artifacts and texture stretching. Particular attention is given to the integration of latent diffusion models and the ControlNet architecture, which utilizes normalized depth maps to accurately control the spatial structure of textures. The proposed approach enables efficient generation of high-quality seamless textures on consumer-grade hardware in alignment with the actual geometric shape of 3D models.

Messages Passed Along the Edges: A Interdisciplinary Mapping Review of Graph Neural Networks for Relational Data

This article presents a narrative review of Graph Neural Networks for Relational Data in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of GNN and graph learning as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.

Messages Passed Along the Edges: A Interdisciplinary Mapping Review of Graph Neural Networks for Relational Data

This article presents a narrative review of Graph Neural Networks for Relational Data in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of GNN and graph learning as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.

Generative Systems Theory

Generative Systems Theory is a foundational inquiry and metaphysics concerning systems theory and complexity science. It focuses on how concepts regarded as basic elements—such as nodes, relations, compatibility, attractors, information, and so on—come into being in the first place, and on what grounds they can be said to exist. If we do not simply assume that they already exist, can they instead be derived from fewer premises and more parsimonious conditions? It is also committed to integrating different, scattered domains into a single generative genealogy: how influence generates constraints; how constraints generate interactions and coupling; how uneven influence generates differences in state; how states and constraints generate evolutionary trajectories; how evolutionary trajectories generate compatibility and attractors; how compatibility and attractors serve as preconditions for nodes and systems; how nodes are represented; what hidden coupling conditions lie behind representation; how synchronicity should be explained; what distinguishes a system from a node; whether relations can be divided into fundamentally different types at the most basic level; why some systems possess robustness; into how many types robustness can be further classified; how a form of information that does not depend on bits can be derived and defined purely from systemic logic; how cognitive systems maximize information; what the most fundamental difference is between living systems and other systems; why gene-centered theories in biology are difficult to sustain; what two opposite extremes animals and artificial intelligence occupy, and why humans lie in the intermediate zone; what the core cognitive functions of human beings are besides embodiment; how the most distinctive and difficult-to-articulate human cognitive functions can be connected with neural networks; what common information-theoretic foundation underlies theories such as Archetype, predictive processing, and generative grammar; how that information-theoretic foundation can be used to derive the optimal forms of human–computer interaction and human–machine symbiosis; how modern Pythagoreanism relates to academic institutions and historical change; and, rather than dividing explanations into top-down and bottom-up, what kind of general explanation can come closer to the underlying logic of predictive processing, and so on and so forth. All of these are derived from the foundational theory of Generative Systems Theory. The theoretical extensions beyond the core framework are as follows: Reinterpret “prediction” from a specific cognitive function into a universal mechanism of state-space convergence. Derive the cognitive system’s “sample space” from state-space theory, and use it to provide a unified explanation of memory, prediction, perception, intuition, and archetypes. Derive self-reference paradoxes from node robustness, and transform the problem of self-reference from a logical problem into a problem of generative conditions. Further derive a theory of judgment concerning subjectivity, objectivity, and authenticity from the problem of self-reference. Place logic, reason, and the a priori within an evolutionary genealogy, and propose the concept of “relative first-order status.” Distinguish output diversity from semantic freedom, and propose “cognitive friction” as a metric for evaluating the compatibility of human–AI coupling. Reinterpret the broad problem of AI overfitting through the concept of “natural attractors.” Propose “constraint isomorphism,” freeing understanding from content similarity and representational replication. Reinterpret “structure” from traditional positive-space morphology as negative-space constraints within state space.

Digital Financial Inclusion and Its Impact on Economic Growth

The rapid growth of Generative Artificial Intelligence (GenAI) has made AI literacy an essential skill for students, yet conventional classroom and e-learning platforms provide limited support for helping learners understand, question, and critically evaluate AI-generated information. This paper presents an AI-Driven Instructional Support System with Dynamic Learning Analytics for Classroom Evaluation, a role-based, web-based educational platform branded EduMentor that integrates academic and assessment management with a Generative AI instructional-support layer. The system employs Retrieval-Augmented Generation (RAG) and web-scraping-based verification to ground AI responses in course material and trusted external sources, assigns a confidence score to each generated response, and classifies learning content using Bloom's Taxonomy to support progressive cognitive development from Remember and Understand to Apply, Analyze, Evaluate, and Create. A Django backend, a relational database, and a Flutter-based client provide role-specific interfaces for Administrator, HOD, Staff/Teacher, and Student users, along with assessment management, student-performance analysis, and a learning-analytics dashboard. The system was evaluated using 100 functional test cases spanning authentication, assessment management, performance analysis, AI-generated recommendations, and application communication. The platform achieved 94% overall functional accuracy, 100% authentication accuracy, 89% AI-recommendation relevance accuracy, a usability score of 4.4/5 (88%), and an average API response time of 1.8 seconds. The results indicate that an integrated role-based platform combining assessment data with AI-assisted instructional feedback is a feasible and practical approach for data-driven classroom evaluation, provided that AI-generated recommendations continue to be treated as decision-support output requiring teacher verification.