explainable ai

AI-Driven Threat Detection Architecture

The rapid proliferation of digital infrastructure, spanning cloud computing, the Internet of Things (IoT), edge networks, and mobile platforms, has dramatically expanded the attack surface available to cybercriminals. Traditional signature-based intrusion detection systems, while effective against known threats, are fu...

EcoTrust-Net: A Causal, Explainable, and Energy-Aware Graph Transformer and Multi-Agent Reinforcement Learning Framework for Intelligent Network Traffic Prediction and Optimization

The increasing complexity of sixth-generation (6G) communication systems, Software-Defined Networking (SDN), edge intelligence, cloud-native infrastructures, and Internet of Things (IoT) environments has intensified the need for intelligent network traffic prediction and optimization capable of supporting low latency,...

From Human-AI Task Allocation to Accountable Process Design:

Governing the Privacy-Personalization Tension in AI-Driven Marketing Platforms: An Integrative Framework for Customer Data Platforms

Customer data platforms (CDPs) are increasingly positioned as the data and decision infrastructure of artificial intelligence (AI)-driven marketing. Yet the academic literature has not adequately explained when a CDP can reconcile privacy compliance with hyper-personalization, nor how this reconciliation depends on pla...

Reconceptualizing outdoor geoscience learning for the generative AI era: a human-centered instructional model

Generative artificial intelligence (GenAI) creates a distinctive problem for outdoor geoscience learning: it can broaden questioning and explanation, yet it can also pre-structure what learners notice and displace their interpretation of field evidence. Existing field-learning frameworks explain how conceptual, geograp...

Multi-Crop Leaf Disease Detection with LLM-Assisted Diagnosis and Explainable AI

Early identification of plant diseases is crucial for maintaining agricultural productivity and ensuring food security. Manually inspecting crop leaves takes a lot of time and requires expertise, making automated disease detection systems very helpful. This paper presents a framework for detecting leaf diseases in mult...

Thriving with AI: How Employee AI Readiness Translates into Sustainable Work Behavior Through Service Innovation in Hospitality and Tourism Industry

Artificial intelligence is fundamentally reshaping work in hospitality and tourism, yet limited research has examined how employees’ AI readiness is associated with psychological and behavioral outcomes with sustainability implications. Drawing on Conservation of Resources Theory, this study investigates the relationsh...

Cybernetic resilience in human–AI crisis response networks: a theory of adaptive emergence with empirical validation framework

This research addresses the following specific theoretical deficiency: while existing resilience frameworks do indeed consider the process of recovery or adaptation of crisis systems at the level of individuals, they fail to explain the ways in which human–artificial intelligence (AI) hybrid networks create emergen...

Project decision-making through artificial intelligence: a mixed-methods study of public projects in the transition economies

Artificial intelligence (AI) is increasingly embedded in organizational processes, challenging established assumptions about decision-making in project environments. Prior research has predominantly conceptualized AI either as a decision-support tool or as a substitute for human judgment, thereby overlooking the so...

Sound Of Porosity: Unlocking Local Acoustic Signatures Of Porosity In LPBF Via Low-Cost Sensing And Explainable AI

A low-cost, off-the-shelf ultrasonic microphone installed inside the build chamber is demonstrated as a practical alternative for capturing high-resolution acoustic signals during LPBF, and acoustic-based models achieve low-latency inference, suitable for in-process monitoring, paving the way for adaptive corrective co...

Artificial Intelligence in Medicine: diagnostic, therapeutic and ethical impact

AI offers considerable potential to reduce costs, increase diagnostic accuracy, personalize care and expand access to quality services, provided its deployment overcomes technical, ethical and regulatory barriers through proactive, adaptive and patient-centered governance.

Agentic AI Systems: A Review of Multi-Agent Reasoning, Trust, Orchestration, and Autonomous Data Science

This review synthesizes four recent studies spanning these strands, together with the broader literature on multi-agent orchestration, retrieval-augmented generation (RAG), large-language-model (LLM)-based knowledge-graph construction, and explainable AI, to build a unified picture of agentic AI as applied to intellige...