Artificial Intelligence for Digital Transformations
Description
This open access book constitutes the proceedings of the First International Conference on Artificial Intelligence for Digital Transformations, AIDT 2026, which took place in Baku, Azerbaijan, during June 22-24, 2026 .
The 28 full papers included in this book were carefully reviewed and selected from 167 submissions. They were organized in topical sections as follows: AI in Emerging Digital Infrastructures; AI-Driven Automation and Optimization; Core AI Models and Learning Paradigms; Governance, Strategy, and Human-Organization Interaction; and Sustainability and Responsible Innovation.
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Content
.- AI in Emerging Digital Infrastructures.
.- From Detection to Explanation: Combining Stream Reasoning and Concept Induction in Smart Farming.
.- Improving Taboo-Word Generation in Turkic language with Morphology-Aware Filtering and Diversity Selection.
.- A domain ontology for cooperative perception in the Internet of Mobile Things.
.- AI-Driven Automation and Optimization.
.- Cognitive Load Estimation through Eye-Tracking in Industrial Tasks: A Pilot Study.
.- A Financial-Risk Aware Machine Learning Framework for Industrial Convergence.
.- Hybrid Wavelet-LSTM and Weibull Risk Modeling for Early Fault Prediction Using Accelerometer-Based Vibration Data in Rotating Machinery.
.- AI-Driven Digital Twin Framework for Risk-Aware Predictive Maintenance in Electrical Control Systems.
.- AI-Driven Decision Support for Wind-BESS Dispatch Under Export Caps: A Behavioral Cloning Approach.
.- Core AI Models and Learning Paradigms.
.- Relational Verification, Feature Sensitivity, and Cross-City Transfer in Heterogeneous Graph Learning for Urban Functional Zone Classification.
.- Bridging Prompting and Semantic Grounding: A Unified Evaluation Framework for Document-Level Relation Extraction.
.- A Divide-and-Conquer Framework for Query-Efficient Causal Discovery with Large Language Models.
.- Towards a non-monotonic neuro-symbolic framework: application to outlier mitigation during training.
.- Lexical ambiguity in French: linguistic issues and interpretation by artificial intelligence models.
.- Improving Named Entity Recognition for the Azerbaijani Language Using Neural and Conditional Random Fields-based Models.
.- An approach for evaluating recommender systems: assessing usefulness and coherence through item relationships.
.- Deep Features, Classical Models: Binary vs. Multiclass Deepfake Detection.
.- Can We Trust LLMs for Mental Health-Based Decisions? A Causality Aware Reliability Analysis.
.- Real-Time Azerbaijani Sign Language Sentence Recognition: Comparative Evaluation of Cosine Retrieval and Seq2Seq Approaches.
.- Adapting Large-Scale Neural TTS Models to Low-Resource Languages: A Comparative Study.
.- Federated Learning Approach to Autoencoder-Based Feature Extraction for Pneumonia Detection.
.- Benchmarking Algorithmic Fairness in AI Recruitment: A Comparative Study of Mitigation Strategies and the FIS Metric.
.- Handoff Hallucinations: Taxonomy, Benchmark, and Mitigation for Multi-Agent Pipeline Failures.
.- AzText: Curating Web-Scale Pretraining Data for a Low-Resource Language.
.- Governance, Strategy, and Human-Organization Interaction.
.- An Ontology-Driven Recommender System for Enhancing Workplace Practices.
.- Statutory AI: Aligning Large Language Models With Legal Norms.
.- Explainability of Tourism Risk Assessment by Leveraging Fuzzy Ontology Properties.
.- Sustainability and Responsible Innovation.
.- Efficiency Driven Digital Transformation for Sustainable Real Time Monitoring of High Pressure South Caspian Wells.
.- A Community-Driven Dataset and Protocol for Azerbaijani Sign Language Recognition: Advancing Digital Inclusion through Human-Centered AI.