explainable ai

Types of Plant Leaf Disease Detection Techniques Using Machine Learning Classification Algorithms

Plant diseases are one of the most serious threats to agricultural production worldwide — shortening harvests and, in turn, threatening our food supply. Recognizing these illnesses in an early, high-accuracy manner is vital for their management and mitigation. Traditional approaches to plant disease diagnosis are norma...

Explainable Multimodal Artificial Intelligence Framework for Real-Time Cognitive Engagement Assessment in Smart Learning Environments

An explainable, multimodal AI framework for assessing cognitive engagement in smart learning environments is presented in this paper. This framework integrates five modalities of sensing: facial expression, gaze, speech, interaction, and physiological sensing. The framework uses a preprocessing pipeline consisting of d...

Explainable AI for Safety of Life Applications in GNSS

Exploring the potential of AI-driven brain–computer interfaces for Alzheimer’s disease: an integrated framework for diagnosis and therapeutic intervention

Early diagnosis and timely intervention are critical in Alzheimer's disease (AD), yet conventional assessments fail to capture longitudinal neural dynamics. Although traditional biomarker frameworks offer essential pathological insights, they remain constrained by high costs and restricted accessibility. Recent advance...

AI behaves more like a brain than a database – cognitive science’s role in its origin story helps explain why

Toward Trustworthy AI for Post-stroke Suicidal Risk: Explainable Interaction Modeling and External Validation

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...