explainable ai

Facilitating structure-based drug discovery with an artificial intelligence-driven virtual screening platform.

The Comprehensive VS Platform with AI Engine (CVSP-AIE) for drug discovery from compound libraries integrates three AI models: KarmaDock, a fast docking model that directly updates atomic coordinates; CarsiDock, an accurate docking model that predicts protein-ligand distances and reconstructs binding poses; and RTMScor...

From Digital Literacy to Responsible AI-Driven Entrepreneurship: Institutional Readiness and Policy Implications for Higher Education

Purpose: This paper develops a theory-informed conceptual framework for responsible AI entrepreneurship ecosystems in higher education. It addresses the limited integration of AI capabilities, entrepreneurship, institutional readiness, and governance in existing scholarship, particularly amid increasing AI adoption by...

Explainable AI-based hybrid GNN-MLP model for strawberry fruit disease detection using hyperspectral imaging

An enhanced hybrid deep-learning method by combining graph neural networks (GNNs) and multi-layer perceptrons (MLPs) for effective strawberry disease detection in real environments of fields offers an accurate and explainable solution that has a computationally efficient commitment for real-time monitoring of disease i...

Integrating climate change, land-use dynamics, and explainable AI for future landslide susceptibility assessment in the Western Ghats

AI health assistant combining transformers and XGBoost for multilingual care

CIMAS HealthMate is proposed, a hybrid multilingual VHA that integrates transformer-based natural language processing (NLP) with an explainable extreme gradient boosting (XGBoost) decision model to provide accurate and transparent symptom triage.

ARTIFICIAL INTELLIGENCE ADOPTION AND STRATEGIC PLANNING EFFECTIVENESS: AN EMPIRICAL STUDY OF ORGANIZATIONAL DECISION-MAKING

The study concludes that AI adoption serves as a strategic organizational capability that significantly enhances strategic planning effectiveness and suggests that organizations leveraging AI technologies are more likely to develop effective strategies, improve decision quality, enhance forecasting accuracy, and streng...

DEVELOPMENT OF A HYBRID NN–CNN DEEP LEARNING FRAMEWORK FOR INTELLIGENT MALWARE DETECTION, FAMILY CLASSIFICATION, AND VARIANT IDENTIFICATION

A Hybrid Neural Network–Convolutional Neural Network (NN–CNN) Deep Learning Framework for malware detection, malware-family classification, and malware-variant identification and considers two important issues in practical malware detection: model explainability and generalization to previously unseen malware.

Physics-guided neural network modeling of molten carbonate fuel cell polarization curves with explainable AI analysis

AI-driven agricultural productivity: detecting potato leaf diseases with multi-scale feature extraction

A unique, computationally efficient triple-feature block network capable of highly accurate plant disease classification across diverse species and complex imaging environments is proposed.

AI and neuroimaging in autism spectrum disorder: advances in diagnosis, methodological challenges and future directions

A comprehensive review of recent advancements in ASD research, with particular emphasis on neuroimaging, artificial intelligence (AI), and machine learning (ML)-based diagnostic approaches, highlights the growing potential of AI-driven tools for supporting early ASD diagnosis and emphasizes the need for standardized pr...

AI and Bullshit

It is argued that both AI and bullshitters are untrustworthy informants, and for similar reasons, it is natural to describe AI’s informational outputs as bullshit, as it signals their distinctive kind of epistemic deficiencies, which they share with bullshit.

Unlocking research output with ChatGPT- 4 and SciSpace Ai through the mediating and moderating roles of research orientation

The novel AI–Research Output (AI-RO) Model is developed and tested, which integrates the Technology Acceptance Model (TAM) and Socio-Technical Systems Theory to explain both the direct and conditional relationships between AI use and research output.