Intensive-care monitoring remains dominated by single-parameter threshold alarms, of which the large majority are non-actionable and which contribute to alarm fatigue and delayed response. Machine-learning early-warning systems trained on retrospective ICU databases have demonstrated strong discrimination for sepsis and circulatory failure, yet independent evaluations of widely deployed proprietary models show that discrimination in one cohort does not translate into calibrated, trustworthy alerts in another. Building on the authors' earlier IoT/Edge-AI Smart ICU Monitoring System, this paper proposes TAEIR (Trust-Aware Edge Intelligence for ICU Risk), a nine-layer framework addressing predictive uncertainty, calibration, explanation, drift, and the governance mechanisms clinicians need in order to trust an alert. This is a framework, methodology, and benchmark-design paper reporting no experimental results, hardware measurements, or clinical outcomes.

As enterprises move toward autonomous AI systems, security must evolve beyond traditional application protection. This edition explores how organizations can secure AI agents, models, data, prompts, tools, and autonomous workflows through a comprehensive Enterprise AI Security framework.

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A reference framework linking strategic intent, control architecture, operating models, and platform delivery for enterprise intelligence programs.

A governance reference model connecting policy controls, review, escalation, and lifecycle accountability in enterprise AI programs.

A maturity model for operationalizing LLM systems from experimentation to governed, observable, and scalable enterprise operations.

A benchmark-focused publication introducing methodology and evidence pathways for evaluating smart-grid forecasting models using BQEB-Data.

This publication introduces BQEB-Data as an open benchmark dataset supporting autonomous energy intelligence and smart-grid analytics research.

This report presents a federated graph learning approach for intrusion detection and adaptive cyber defense in V2X vehicular systems.

This technical report proposes a wearable IoT and edge AI framework for non-invasive health monitoring through gastrointestinal gas-based biomarkers.

A real-time ICU monitoring architecture using IoT and edge AI to support patient risk prediction and operational responsiveness.

A white paper outlining an AI-augmented enterprise system for knowledge organization, institutional memory, and cognitive productivity.

An AI-augmented cognitive framework for incident intelligence that connects IT operations telemetry, contextual reasoning, and observability-led action pathways.

This work introduces an explainable federated AI approach for multi-omics disease risk prediction, balancing collaborative learning with interpretability requirements in life science settings.

Hospitals worldwide face increasing operational complexity due to emergency department congestion, fluctuating patient demand, limited resources, and the need for real-time decision support. This paper proposes a Trustworthy AI-Driven Hospital Digital Twin Framework for intelligent resource allocation and patient flow optimization, integrating artificial intelligence forecasting, optimization algorithms, reinforcement learning, digital twin simulation, and AI assurance mechanisms into a unified operational intelligence architecture. The framework consists of five major layers: Hospital Data Integration, AI Prediction, Hospital Digital Twin, Decision Intelligence, and AI Assurance, establishing a research framework for trustworthy AI-enabled hospital operations.

Cloud Computing allows one to scale resources up or down on demand, yet the practice-oriented resource management methods currently used are not always capable of responding promptly to the changing needs. The emerging concept of Artificial Intelligence (AI) may offer a solution due to its unique ability to predict future demands. The present paper explores the concept of using AI in Cloud Computing and how it can address the current issues related to resource management, workload, and response time, scalability, security, and cost. A conceptual framework was created and evaluated within the experiment to identify whether an AI-based approach could be better than the status quo. A comparison was made between the traditional threshold-based provisioning and a newly designed predictive model through a simulated workload. On average, the experimental results showed a 32% decrease in response time, an increase in utilization from 64% to 81%, a reduction in scaling reaction delay by over 56%, and a reduction in operational cost by approximately 17%. Altogether, the experiment proved that predictive AI-based resource management can offer significant advantages over conventional methods in Cloud Computing, although there are factors that need to be considered for practical applications.