explainable ai

younesassini1998-ai/global-renewable-energy-forecasting-cnn-lstm: First stable research release

🚀 Release v1.0.0 — Official Research Implementation This is the initial official release providing the complete, reproducible source code and experimental pipeline for the research paper: "A Hybrid CNN-LSTM Framework for Global Renewable Energy Forecasting: Integrating Socio-Economic Drivers, Explainability, and a Cas...

Reconfiguration of Responsible Subjects: Transformation of Responsibility Attribution through the Participation of Non-Human Reactive Entities

This paper develops a theoretical model of how responsibility structures may come under pressure for reconfiguration as non-human reactive entities—including generative AI, autonomous control systems, recommendation algorithms, and other computational systems—participate continuously in social operations. Rather than a...

AI Fundamentals – Unit IV: AI in Research, Generative AI and Prompt Engineering

Unit IV – AI in Research, Generative AI and Prompt Engineering is an Open Educational Resource (OER) presentation developed for the undergraduate AI Fundamentals course. This presentation introduces the use of Artificial Intelligence in experimentation and multidisciplinary research, followed by the fundamentals of Gen...

Remembering or monitoring? Contextual fit of simulated AI memory cues and consumer trust

AI assistants increasingly refer to information they ostensibly retain from earlier sessions. A remembered preference can signal attentiveness, whereas a reference to stress or late-night behavior can feel intrusive. Existing accounts of personalization and privacy do not fully explain why these cues elicit opposing re...

Early Identification of PCOD Through Artificial Intelligence Leveraging Lifestyle and Clinical Indicators

Polycystic Ovarian Disease (PCOD) has remained one of the most common endocrine diseases in women of childbearing age in the world. Although it is common, the heterogeneous symptoms, subjective clinical criteria, and sociocultural barriers to care impede the early diagnosis of the disorder. The convergence of AI techno...

AI-Driven Anti-Money Laundering Systems

Artificial Intelligence is transforming Anti-Money Laundering enforcement by enabling proactive detection, real-time surveillance, and data-driven decision-making across financial & criminal justice institutions.The present study examines how AI capabilities in anomaly detection, risk scoring, network analysis, and aut...

Trustworthy and explainable artificial intelligence for reliability in advanced manufacturing: a critical review with applications in semiconductor packaging

Unit 1 AI and Its Subfields PPT

Unit I – AI and Its Subfields is an Open Educational Resource (OER) presentation developed for the AI Fundamentals course for undergraduate students. This presentation provides a structured introduction to Artificial Intelligence and its major concepts. It covers the definition, objectives, characteristics, history and...

Integrating Artificial Intelligence into Digital Transformation Strategy: A Framework for Sustainable Enterprise Growth

Digital transformation and artificial intelligence are reshaping how organizations develop capabilities, make decisions, innovate, and pursue sustainable performance. However, existing research largely examines digital transformation, Dynamic Capabilities, AI capability, organizational intelligence, and sustainability...

A tiered deep learning and symbolic reasoning framework for safe, explainable disease diagnosis

This study introduces a modular, AI-powered disease diagnosis system that combines deep learning, neuro-symbolic reasoning, and structured automation to support early, scalable, and explainable clinical decision-making. Built upon the large-scale DDXPlus dataset, the system processes over one million patient records in...

AnExplainable AI Engineering Framework for Claims-Only First-Stage Provider Audit Triage Using SHAP-Guided Hybrid Retrieval-Augmented Generation

This study proposes an explainable artificial intelligence (XAI) engineering workflow for provider-level healthcare claim audit prioritization using SHAP-guided hybrid retrieval-augmented generation (RAG). The framework integrates provider-level claim aggregation, tree-based risk screening, SHAP explanation, explorator...

AI Fundamentals – Unit II: Applications of Artificial Intelligence

Unit II – Applications of Artificial Intelligence is an Open Educational Resource (OER) presentation developed for the undergraduate AI Fundamentals course. This presentation introduces the applications of Artificial Intelligence across six important domains: Healthcare, Finance, Retail, Agriculture, Education, and Tra...