explainable ai

AI-Assisted rational enzyme engineering: advancing biocatalysis for industrial applications

The emergence of artificial intelligence (AI) has initiated a paradigm shift in enzyme engineering. While traditional methods like directed evolution and rational design are proven, they often suffer from limited predictive power, scalability, and efficiency. The integration of AI, particularly machine learning and dee...

Development of an AI-Based Model for Predicting Cyber Attacks Using ML and Generative Techniques

Across the datasets tested, this hybrid, explainability-aware approach consistently outperformed conventional intrusion detection baselines on prediction accuracy, detection capability and adaptability to new attack types, positioning it as a scalable model for real-time attack prediction and security analysis in moder...

A Concrete Vision of the MMO Research Platform: A Knowledge-Generation Space Built around Research Islands, Discoverers, and a Permanent Conference

This paper builds on the preceding paper, "MMO Research Platform: A Concept for a Knowledge-Generation Space through Anonymous Collaboration and LLM Moderation," and develops its concrete spatial design, modes of participation, role structure, and operating principles. The previous paper identified a problem in modern...

Machine Learning and Explainable AI for Pain Assessment and Clinical Decision Support

This doctorate investigates the application of Virtual Reality (VR), Machine Learning (ML), and Explainable Artificial Intelligence (XAI) in pain management, pain assessment, and clinical decision support. Pain is a complex and multidimensional phenomenon that remains difficult to assess and manage effectively, particu...

AI Consciousness and Existential Risk

In AI, existential risk denotes the hypothetical threat posed by an artificial system that would possess both the capability and the objective, either directly or indirectly, to eradicate humanity. This issue is gaining prominence in scientific debate due to recent technical advancements and increased media coverage. I...

NV-Reason-CT: 3D Visual Language Model for CT Analysis

The NV-Reason-CT model, a generative vision--language model for chest and abdominal CT combining native 3D visual encoding with radiologist-guided reasoning, and the model and training code are released to support reproducible research on explainable AI for volumetric medical imaging.

Toward Clinically Interpretable Perioperative Decision Support: Explainable Machine Learning for 30-Day Postoperative Mortality Prediction.

This study developed and evaluated machine learning models using the novel Informative Surgical Patient dataset for Innovative Research Environment, a comprehensive perioperative dataset, to predict 30-day postoperative mortality and demonstrated the relevance of explainable machine learning for the identification of c...

Parkinson’s disease assessment with selfie video via feature engineering and explainable AI

The findings indicate that facial dynamics carry information relevant to PD assessment and support further development of non-invasive, accessible screening tools, although the accuracy achieved here is not yet sufficient to substitute for clinical evaluation.

An Integrated Social Media, AI, and Data Analytics Marketing Capability Model for Enhancing MSME Performance and SDG Achievement

An integrated marketing capability model is developed and empirically tests to explain how Social Media Marketing Capability, Artificial Intelligence Marketing Capability, and Data Analytics Capability jointly influence the marketing performance of MSMEs.

Explainable AI as a diagnostic tool for analyzing spatiotemporal variability in simulated groundwater recharge: Application to a semi-arid river basin

Field-validated explainable AI-based UAV remote sensing for variable-rate fertilization in precision sugarcane nutrient management

Affective AI ethics in tourism: Extending the SOR framework with indigenous relational constructs

An indigenous relational construct-based culturally informed framework on the ethics of affective AI in tourism that incorporates indigenous constructs into the stimulus–organism–response framework is presented, demonstrating explanatory power beyond universalist ethics and fast trust models.