In external validation, interpretable models substantially outperformed established HF risk scores for short- and long-term mortality and simultaneously localised correctable therapeutic gaps, supporting a prediction-to-action tool deployable with ten bedside variables.
S. M. Jauhari, Wai-Lok Yeung, T. Li et al.· medRxiv· 0 citations
An XAI-based anomaly detection framework tailored for DER networks (ExCYDER) that distinguished between coherent and inconsistent alerts without compromising detection accuracy, demonstrating that integrated verification within XAI-based ADS enhances interpretability, auditability, and operational robustness for DER-fo...
D. Popoola, S. Bhattacharya, M. Govindarasu· IEEE Power & Energy Society...· 1 citation
This paper presents ClimateGuard, a localized, explainable, multi-hazard climate risk prediction system integrating satellite-derived geospatial indicators, real-time meteorological data, and machine learning for climate hazard forecasting. The system combines Random Forest and XGBoost models with environmental and dis...
Zainab Travadi· Zenodo (CERN European Organi...· 0 citations
Machine learning and deep learning approaches may improve efficiency, reproducibility, and pattern recognition in several forensic workflows, particularly postmortem imaging, skeletal age estimation, mass spectrometry-based toxicology, biometric identification, digital pathology, and natural language processing of medi...
Emre Yuluğ· European Journal of Therapeu...· 0 citations
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Correction notice (v1.5). This version demotes a claim. Clause (iii) of Theorem 3.7 -- the match between the CC-channel connection-coefficient ratio and the Riemann--Hilbert datum of P_V/D_5^(1) at the V_quad tau parameter -- was stated as part of a theorem through v1.4. It is not a theorem of this paper and is recorde...
papanokechi papanokechi· Zenodo (CERN European Organi...· 0 citations
Data concepts relevant to AI applications in public health — such as availability, relevance, and representativeness — are usually explained in their general, basic sense for a broad readership of engineers and public health professionals alike, without being organized by what kind of practical failure each one represe...
Min Wu· Zenodo (CERN European Organi...· 0 citations
This collection records a literature-linked research programme on the Riemann zeta function and its explicitly constructed geometric, arithmetic and fluid interfaces. The September 12 calculation edition adds a complete reader for the Split-Zero programme: scalar algebra, support fibres, Rees filtrations, the actual ar...
KokunoYumeto· Zenodo (CERN European Organi...· 0 citations
The expanding use of intelligent and non-intelligent data-driven tools across technology-mediated environments has improved operational efficiency while increasing the legal significance of errors in data acquisition, processing, state detection, and automated execution. Building on the “Theory of Intelligent and Non-I...
Mohammad Mahdi Varposhti Borujeni· Zenodo (CERN European Organi...· 0 citations
The GeoAI Tourism Intelligence Framework (GTIF), a conceptual framework integrating geospatial big data, AI algorithms, and spatial analytics to support evidence-based destination planning and management, is presented.
Huseyin Kaya· International Journal For Mu...· 0 citations
The expanding use of intelligent and non-intelligent data-driven tools across technology-mediated environments has improved operational efficiency while increasing the legal significance of errors in data acquisition, processing, state detection, and automated execution. Building on the “Theory of Intelligent and Non-I...
Mohammad Mahdi Varposhti Borujeni· Zenodo (CERN European Organi...· 0 citations
This paper presents ClimateGuard, a localized, explainable, multi-hazard climate risk prediction system integrating satellite-derived geospatial indicators, real-time meteorological data, and machine learning for climate hazard forecasting. The system combines Random Forest and XGBoost models with environmental and dis...
Zainab Travadi· Zenodo (CERN European Organi...· 0 citations
Purpose. Students’ trust in generative artificial intelligence (GenAI) learning applications is critical to determine the adoption and use of these systems. The purpose of this study is to explore students’ trust in GenAI and how to design for trust in collaborative learning tasks. We prototyped different interface des...
Jacqui McLaughlin, Yujing Zhang, Zachary R. Noel et al.· UNC Libraries· 0 citations
MIT professors Adam Berinsky and Chara Podimata discuss a new project to track how AI chatbots are responding to questions about the 2026 U.S. midterm elections.
Microsoft Research Blog· microsoft.comOct 30, 2026
Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research.
Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.
Microsoft Research Blog· microsoft.comOct 29, 2026
Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.
