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explainable ai

2,181 papers

#explainable ai Open access Oct 2026

Biomass-LOSO-XAI: Multi-Seed LOSO Validation and Explainable AI for Agricultural Deep Learning

Reproducibility companion repository for the study "Evaluating Domain Generalization in Agricultural Deep Learning through Multi-Seed LOSO Validation and Explainable AI". The repository provides source code, experimental configurations, machine-readable results, and computational workflows for evaluating spatial genera...

Hechavarria-Hernandez, Jesus R. · 0 citations
#explainable ai Oct 2026

UniConEt: A novel Hybrid Deep architecture for fine grained Land Use and Land Cover Classification Using Hyperspectral Imagery

In the rapidly developing field of Remote Sensing (RS), Land Use Land Cover (LULC) classification of Hyperspectral Images (HSI) is a fundamental yet challenging task. HSI is also challenging because of its high spectral dimensionality. Therefore, this research develops a comprehensive spectral and spatial classificatio...

Pilla Sri Lekha, B. Sirisha · 0 citations
#explainable ai Open access Oct 2026

An AI agent-based smart campus management platform

The continuing digital transformation of higher education has produced large volumes of heterogeneous campus data, including network access records, wireless access-point mappings, academic information, security alerts, and institutional documents. These resources are often isolated across operational systems and there...

Xin Yue, Zhuo Ma, Jian-Qing Liu et al. · 0 citations
#artificial intelligence Open access Oct 2026

Global trends and knowledge structure of cardiovascular magnetocardiography research: a bibliometric analysis

Background Cardiovascular diseases remain a leading global health threat. Recent advances in room-temperature sensing and artificial intelligence (AI) have renewed research interest in the clinical translation of magnetocardiography (MCG) for cardiovascular diagnosis, yet the global research evolution of MCG has yet to...

Yaxing He, Xu Zhang, Xiaole Han et al. · 0 citations
#explainable ai Open access Oct 2026

Space as a Growing Mesh of Entanglement: Hypotheses, Tests and a Falsified Prediction

This paper explores an idea I've been developing: that space is a web of quantum entanglement, and that the universe expands because the links in that web gradually weaken as new pieces of space are added.I set out two hypotheses. First, distance between points in space depends on how strongly they're entangled. Second...

Kieran Hearne · 0 citations
#artificial intelligence Review Open access Oct 2026

Associations of metacognition, self-directed learning ability, and AI literacy with academic achievement among university students predominantly enrolled in health-related majors: a cross-sectional study

The expanding integration of digital technologies and artificial intelligence (AI) in higher education has heightened the need to identify learner competencies associated with academic achievement. This study examined the associations of metacognition, self-directed learning ability, and AI literacy with self-reporte...

Han-Mook Choi, Ha-Kyeong Jo, Younghee Kim et al. · 0 citations
#explainable ai Open access Oct 2026

Space as a Growing Mesh of Entanglement: Hypotheses, Tests and a Falsified Prediction

This paper explores an idea I've been developing: that space is a web of quantum entanglement, and that the universe expands because the links in that web gradually weaken as new pieces of space are added.I set out two hypotheses. First, distance between points in space depends on how strongly they're entangled. Second...

Kieran Hearne · 0 citations
#federated learning Review Oct 2026

Enhancing the manufacturing processes and productivity in Industry 5.0 with the integration of machine learning and cyber-physical systems

This paper aims to examine the synergistic integration of Cyber-Physical Systems (CPS) and Machine Learning (ML) as a foundational enabler for Industry 5.0, focusing on creating human-centric, sustainable and resilient manufacturing ecosystems. The study utilizes a synthesis and review approach, analyzing re...

Venkatesh Naik, Soma Das, M. N. Vinay et al. · 0 citations
#reinforcement learning Open access Oct 2026

Human-centric digital twins in industry 5.0: technologies, applications, and future directions

Industry 5.0 (I5.0) emphasizes industrial technologies that are human-centric, sustainable, and resilient. Human-Centric Digital Twins (HCDTs) support this transition by integrating worker data with robots, machines, tasks, and industrial environments. However, the relevant research remains fragmented across human mode...

Sagheer Khan, Saifullah Tumrani, Nguyen Van Nam et al. · 0 citations
#generative ai Book Open access Oct 2026

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR BUSINESS

artificial intelligence (AI) and machine learning (ML) have become general-purpose technologies that are reshaping how organisations make decisions, serve customers, design products and run operations. This chapter provides a management-oriented treatment of the principal AI and ML techniques and of the organisational...

Shashank Saroop, Sunil Singarapu · 0 citations
#generative ai Book Open access Oct 2026

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR BUSINESS

artificial intelligence (AI) and machine learning (ML) have become general-purpose technologies that are reshaping how organisations make decisions, serve customers, design products and run operations. This chapter provides a management-oriented treatment of the principal AI and ML techniques and of the organisational...

Shashank Saroop, Sunil Singarapu · 0 citations

From tech blogs

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Microsoft Research Blog Oct 7, 2026

Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.

Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

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