Category
440 papers
An integrated conceptual framework is developed that explains how digital transformation drives Shadow IT adoption, links unauthorised technology use to organisational complexity and cyber governance risks, and identifies governance mechanisms that balance innovation, security, and compliance.
The findings support generationally differentiated HR interventions for strengthening human–AI collaboration in higher education and contribute to SDGs 4, 8, and 9.
This work conducts semi-structured interviews with industry practitioners working with agentic AI systems, indicating that agentic AI systems are mainly explained through organizational and anthropomorphic source domains, such as employees, teams, or assistants, which embed abstract system qualities within familiar social structures.
Major deep learning architectures, including CNNs, residual networks, UNet, attention-based models, Vision Transformers, and hybrid approaches, along with their clinical applications are summarized and emerging directions such as self-supervised learning, Explainable AI, federated learning, and lightweight models are highlighted as promising approaches for more reliable and accessible medical image analysis.
Reach audiences
Advertise in front of researchers, engineers, and readers.
It is concluded that credit unions and community banks can benefit from machine learning only when innovation is balanced with fairness, privacy protection, human oversight, vendor accountability, and community-centered governance.
NeuroCAM-X is presented, a novel explainable hybrid artificial intelligence framework that integrates deep learning-based MRI image analysis with Optical Character Recognition-enabled clinical report interpretation for comprehensive brain tumor diagnosis and addresses critical gaps in medical AI.
The study concludes that strengthening fraud detection and financial reporting integrity requires integrating analytics and internal controls within a unified governance framework supported by continuous monitoring, institutional accountability, and transparent oversight mechanisms.
An Autonomous Decision Assurance Layer (ADAL) is proposed for AI-driven enterprise analytics environments that bridges data governance, multi-agent AI, human-in-the-loop oversight, responsible AI controls, and executive decision intelligence.
This review paper looks at transformer-based unified models that incorporate sentiment analysis and false tweet detection, and stresses how transformer-based models, such as BERT, RoBERTa, and XLNet, outperform traditional machine learning algorithms due to their attention mechanisms and contextual knowledge.
Emerging research directions, such as explainable AI, federated learning, digital twins, edge intelligence, and physics-informed machine learning, are identified as promising strategies for developing resilient, intelligent, and sustainable future power grids.
The results indicate that sustainable performance is achieved not merely through AI adoption but through the organization's ability to sense opportunities, seize strategic initiatives and continuously reconfigure resources, thereby extending RBV and DCT within the sustainability and digital transformation domains.
In light of the pervasive methodological limitations identified, including high analytic risk of bias, absence of external validation, and lack of model interpretability, claims of ML superiority over CHA2DS2-VASc must be interpreted with caution.
MIT affiliates engage with the MIT-IBM Computing Research Lab to bring rigorous theory to production systems.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
Earth AI
Piloting the world's first double-blind AI evaluations
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
