explainable ai

An Adaptive Explainable Hybrid Data Mining Framework for Zero-Day Cyber Threat Detection Using Network Traffic Analysis

The research demonstrates that using adaptive model with dossier mining approach, along with effective network signatures and model interpretability, a resistant and flexible protection measure may be implemented.

An explainable AI framework for multi-stressor climate risk screening of road networks using XGBoost and SHAP

AI-Assisted Design of a Post-Quantum Cryptographic Accelerator: A Deployed-Silicon Case Study

A byte-exact golden-reference oracle paired with randomized adversarial soak drives the rejection loop past any fixed vector, closing the gap: 301,343 data-dependent signings, zero escapes.

ARTIFICIAL INTELLIGENCE-DRIVEN QUALITY CONTROL IN MODERN MANUFACTURING SYSTEMS

AI should not be viewed simply as a replacement for human inspection but as an intelligent layer that augments engineering knowledge and enables manufacturing systems to anticipate, diagnose and correct quality problems, a critical review of recent literature concludes.

Neural Symbolic Hybrid Systems for Robust Reasoning

This paper proposes a novel approach to artificial intelligence by integrating neural networks and symbolic reasoning, resulting in Neural Symbolic Hybrid Systems. The core claim is that combining the strengths of both paradigms—the pattern recognition capabilities of neural networks and the logical inference capabilit...

INTELLIGENT SOLUTIONS AND MACHINE LEARNING IN MECHANICAL ENGINEERING: APPLICATION OPPORTUNITIES AND PROMISING DIRECTIONS

This article analyzes the current state, practical applications, and promising development directions of artificial intelligence (AI) and machine learning (ML) technologies in mechanical engineering. The study examines the application of AI and ML methods in predictive maintenance, design optimization, manufacturing an...

From Black-Box to Glass-Box: The Impact of Procedural Transparency and Explainability on AI Literacy in K-12 Education

As artificial intelligence (AI) becomes ubiquitous, moving learners beyond foundational awareness toward higher-order AI literacy is increasingly essential. Automated AI tools prioritize high-level abstraction, risking “illusory competence” in which operational success masks conceptual misunderstanding. This study eval...

Assessing the Impact of Layer Selection on CAM-Based Explainability for YOLOv8: A Study on Hand-Sketched Digital Logic Circuits

Object detection has recently become a cornerstone task in computer vision. It allows the machine to identify and localise multiple objects in an image. With the development of deep learning, object detection systems have achieved remarkable performance in various domains such as autonomous driving, surveillance, healt...

可解释人工智能的因果推断引擎

This paper proposes a novel engine for Explainable Artificial Intelligence (XAI) focused on causal inference. The core claim is to construct an AI engine capable of performing causal reasoning and providing more reliable explanations for decision-making. The engine leverages a mechanism based on structural causal model...

Ensemble learning with explainable AI for improved heart disease prediction based on multiple datasets

An ensemble learning-based framework for improved heart disease prediction using multiple datasets and Explainable Artificial Intelligence (XAI) to evaluate model performance in terms of the clinical features that are most relevant in predicting heart disease.

Machine Learning and Explainable AI for Predicting Survival and Mortality in People Living With HIV on Antiretroviral Therapy: A Secondary Analysis of the ACTG-175 Trial Dataset.

Machine learning models, particularly Elastic Net and Cox Proportional Hazards, effectively predict mortality and survival in PLWH, with immunological response, treatment-related factors, and clinical presentation emerging as the dominant predictors.

The Trust Paradox in AI–Blockchain Systems: Exploring Transparency, Immutability, and Human Trust

The study argues that trustworthy AI–Blockchain systems require more than technological guarantees: they require understandable explanations, appropriate human oversight, data governance, accountability mechanisms, and context-sensitive transparency.