explainable ai

Artificial intelligence and anti-cancer drugs: advancing discovery, personalization, and precision oncology

Artificial intelligence (AI) is changing the way we discover anticancer drugs. It tackles the problems of traditional methods, such as prolonged timelines for drug development, higher costs, and substantial inter-patient heterogeneity. This review explains how AI is applied throughout the cancer drug pipeline. This inc...

HUMAN-CENTERED EXPLAINABLE AI FOR ADAPTIVE RESOURCE ALLOCATION AND DECISION GOVERNANCE IN SERVICE ORGANIZATIONS

Service organizations increasingly employ artificial intelligence and predictive analytics to allocate financial, technological, workforce, and operational resources. Although algorithmic systems can process complex organizational data, purely prediction-driven approaches may generate recommendations that are difficult...

The Observational Incompleteness Framework

The Observational Incompleteness Framework is an AI-based research programme deriving quantum-mechanical structure from the premise that observation is a proper subsystem of a deterministic whole. The archive contains three kinds of content: Technical papers (papers/) — the authoritative technical content, including th...

An AI Integration Framework for Social Innovation: A Cross-Case Analysis of AI System Configurations for the Triple-Zero Agenda

Artificial Intelligence (AI) systems are increasingly being applied to complex social and environmental challenges, but their implementation across different application domains requires appropriate combinations of computational techniques, data sources, deployment environments, user interfaces, and responsible-AI mech...

"EDU VISION: An Intelligent AI-Based Academic Support System"

In contemporary higher technical education, academic institutions affiliated with large engineering universities face significant hurdles in monitoring student learning trajectories, diagnosing root conceptual deficiencies, and providing timely faculty interventions. Traditional examination systems rely predominantly o...

Approaching the Memory-Bandwidth Ceiling: Three As-Deployed LLM Inference Stacks on a 128 GB Unified-Memory APU

Single-node measurement note. Two-day campaign measuring three as-deployed LLM inference stacks (model + runtime + quantization: Qwen3.8-Flash-Next on halogen, GLM-5.3 on ExLlamaV3, DeepSeek-V4-Flash on ember) on one 128 GB unified-memory AMD Ryzen AI Max+ 395 node, under one measurement contract and one frozen corpus....

Peer Review Report For: Designing an AI-Responsive Early Childhood Curriculum: A Conceptual Framework for Supporting Developmental Learning Trajectories in Preschool Education [version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved]

Background The rapid integration of artificial intelligence (AI) and deep learning technologies into education creates new opportunities for personalized learning in early childhood education (ECE). Yet limited research has examined how educators interpret and enact AI-responsive curriculum decisions in preschool setti...

Explainable AI-integrated multi-objective optimisation for circular material routing in modular construction

A Two-Tier Blockchain Architecture for Verifiable Explainable-AI Audit Trails: Individual and Merkle-Batched Commitment of SHAP-Based Fraud-Detection Decisions

Machine learning systems that make automated decisions, including fraud flags, loan denials, and safety interlocks, typically generate an explanation (for example via SHAP) in the same trust domain as the model itself, leaving no independent proof that the explanation shown to an auditor is the one the model actually p...

raefalizadehnima-droid/Sustainable-Pathways-to-Trustworthy-AI-in-Pharmaceutical-Manufacturing: XAII

The use of artificial intelligence (AI) is growing in pharmaceutical manufacturing, but regulatory assimilation has been slow due to difficulties in interpretability, uncertainty calibration and sustainability assessment. This work proposes a quantitative framework for assessing trustworthy AI through a multi-objective...

AI-Driven Recruitment Analytics for Intelligent Candidate–Job Matching

The explosion of data from candidate-job interactions due to digitization in recruiting has exceeded the ability of manual curation and exposed the inadequacies of lexicon-based applicant tracking systems. In this paper, we develop a new AI-driven recruitment analytics paradigm that reimagines the candidate-job matchin...

Explainable AI for Network Intrusion Detection and Software Vulnerability Prediction: Improving Threat Transparency and Security Decision-Making