explainable ai

Quantum Vision Ai For Pediatric Urological Screening Using Hybrid Quantum Machine Learning

Pediatric urological disorders require timely recognition to reduce the risk of persistent functional, reproductive, and psychosocial consequences, yet screening still depends heavily on clinician-led visual examination. This study develops and evaluates Quantum Vision AI, a child-safe intelligent screening platform th...

University students’ readiness for ChatGPT-assisted English learning: an explanatory mixed-methods study

The growing integration of generative artificial intelligence (AI), particularly ChatGPT, in English as a Foreign Language (EFL) learning has raised increasing interest in learners' readiness to engage with such tools effectively. However, existing research has largely focused on isolated constructs such as technology...

The Promise and Perils of Smart (City) Bots as Educational Tools

Written from an educator’s perspective, the paper aims to examine the increasing use of chatbot technology capable of answering user queries in various fields such as commerce, public service delivery, urban areas, higher education etc. by simulating human conversation through text messages or voice commands. We analys...

Class balanced diabetic retinopathy image synthesis using a latent diffusion framework

This study establishes the Diabetic Retinopathy Latent Diffusion Synthesizer (DR-LDS) as a highly resource-efficient solution to the medical data bottleneck by leveraging a fine-tuned Variational Autoencoder for domain-adapted latent space compression, alongside an optimized U-Net.

AI-Powered Self-Healing Databases: The Future of Intelligent Database Management

The concept, working process, key features, benefits, challenges, applications, and future scope of AI-powered self-healing databases in simple and easy-to-understand language are explained.

Enhancing AI-powered Malware Detection Against Adversarial Attacks Using Architecture-aware Robustness and Explainability Analysis

Artificial intelligence, judicial precedent and responsible judicial AI governance in Uganda: ethical, constitutional and institutional implications of AI-assisted legal research

Examination of AI-assisted legal research in Uganda’s Superior Courts through doctrinal, jurisprudential and normative analysis argues that precedent presents an ethical governance problem and requires governance of both the research output and the legal-information infrastructure that makes the output possible.

From principlism to practice: a five-principle safeguard framework for workplace AI

The article develops a Five-Principle Workplace AI Safeguard Matrix that links each principle to design evidence, organisational evidence and worker-facing evidence, and proposes Occupational AI Safeguard Rounds as a recurring interdisciplinary review practice connecting ethics, engineering, participation and redress.

U-NetL2O: An explainable model for low-dose CT image reconstruction

Developing nonviolent artificial intelligence: nonviolence as a principle of ethical AI

An interpretable and adaptive AI governance framework

This study proposes an explainable and adaptive AI governance approach from the perspective of time to balance AI governance and AI innovation.

Diagnosing Inconsistencies in 4d N=1 Gauge Theories with Explainable AI

Brane tilings are bipartite graphs on a 2-torus that encode the Lagrangians of 4d N=1 supersymmetric gauge theories arising on D3-branes probing toric Calabi-Yau 3-folds. Among these bipartite graphs, only those that satisfy geometric consistency conditions correspond to well-behaved quantum field theories. We train a...