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explainable ai

2,807 papers

#explainable ai Open access Sep 2026

GoHumanize Open Humanizer: an open text-humanization model, dataset and pipeline built from public-domain data

The GoHumanize Open Humanizer is a public research and educational model: a Qwen3-4B fine-tune (QLoRA) that rewrites AI-styled English prose into more natural human writing. This record archives the complete pipeline (Project Gutenberg sourcing and cleaning, AI-fication of inputs with three generators, training, evalua...

GoHumanize team · 0 citations
#explainable ai Open access Sep 2026

Privacy-preserving intrusion detection in mobile edge networks via federated adaptive probabilistic stacking with clan–flock optimisation and explainable AI

Mobile edge networks require intrusion detection systems (IDS) that are private, accurate and interpretable. We present FedAPS–CFO, a federated IDS coupling four mechanisms: prediction-level federated averaging (FedAvg), so raw traffic never leaves the edge; an Adaptive Probabilistic Stacking (APS) ensemble fusing a st...

Joshitha N., D. P., Helen Sharmila A. · 0 citations
#explainable ai Open access Sep 2026

AnCiR: Analysis of Chronobiological Rhythms

AnCiR is a graphical, no-coding tool for the analysis of chronobiological rhythms and other time-series data. It provides periodicity detection (Sokolove–Bushell χ2, Enright, Lomb–Scargle, FFT), cosinor and harmonic-cosinor fitting, non-parametric circadian rhythm analysis (IS, IV, RA, M10, L5), circular statistics, co...

David Cumin · 0 citations
#explainable ai Open access Sep 2026

Three-Dimensional Integral Balance and Diffusive-Advective Transport: An Executable Code-to-Mathematics Equivalence Audit

Version 2.0.0 of the independent 3D-FIELD technical note. This release translates the sparse-polynomial Python implementation into conventional mathematics over Q[x,y,z,t] and explains the original six rational-arithmetic fixture checks step by step. Four further exact checks cover the Leibniz rule, the fundamental the...

Riccardo Giudici · 0 citations
#explainable ai Open access Sep 2026

Adaptive multidimensional rebalancing for customer churn prediction in marketing analytics

This study proposes the Adaptive Multidimensional Rebalancing (AMR) framework, a framework that dynamically evaluates the local topological structure of the feature space, specifically local density, class overlap, and feature variability, to adap-tively allocate synthetic minority samples.

Ahmad Cahyono Adi, Surya Arafah, Beatrix Ayuwandira Dabur et al. · 0 citations
#explainable ai Open access Sep 2026

Three-Dimensional Integral Balance and Diffusive-Advective Transport: An Executable Code-to-Mathematics Equivalence Audit

Version 2.0.0 of the independent 3D-FIELD technical note. This release translates the sparse-polynomial Python implementation into conventional mathematics over Q[x,y,z,t] and explains the original six rational-arithmetic fixture checks step by step. Four further exact checks cover the Leibniz rule, the fundamental the...

Riccardo Giudici · 0 citations
#generative ai Open access Sep 2026

Artificial Intelligence-Assisted Herbal Formulation Development: From Phytochemical Intelligence to Predictive Nanodelivery and Precision Phytotherapy

Herbal medicines constitute a chemically diverse source of bioactive molecules and remain important components of traditional and complementary healthcare systems. However, the development of reproducible pharmaceutical formulations from herbal materials is complicated by variability in botanical identity, geographical...

Alisha Jabi1*, Deepika Shakya2, Priti Yadav3 · 0 citations
#generative ai Open access Sep 2026

Structured PREreview of "Automating Business Intelligence Requirements with Generative AI and Semantic Search"

This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/22877617. Does the introduction explain the objective of the research presented in the preprint? Yes Yes. The introduction explicitly explains the objective of the resear...

Amarpreet Bassan · 0 citations
#generative ai Open access Sep 2026

Artificial Intelligence-Assisted Herbal Formulation Development: From Phytochemical Intelligence to Predictive Nanodelivery and Precision Phytotherapy

Herbal medicines constitute a chemically diverse source of bioactive molecules and remain important components of traditional and complementary healthcare systems. However, the development of reproducible pharmaceutical formulations from herbal materials is complicated by variability in botanical identity, geographical...

Alisha Jabi1*, Deepika Shakya2, Priti Yadav3 · 0 citations
#explainable ai Open access Sep 2026

From Movement to Mechanism: Understanding a neural network through fly leg movements

From Movement to Mechanism — version 0.4 How can a visible leg movement help us understand a neural network? This illustrated, English-language computational preprint follows two related experiments based on maleCNS, the male fruit-fly central nervous system wiring map. The main text explains the research in accessible...

Tomáš Kovářík · 0 citations
#explainable ai Dataset Open access Sep 2026

P. G. Lejeune Dirichlet: Werke Band II Source-Witnessed Working Drafts and English Translations

This collection preserves P. G. Lejeune Dirichlet’s mathematical works as original-language working transcriptions, English translations, editable LaTeX, source scans and checking records. Its established core is Werke Band II Papers I–XLI, including the cumulative readers, component sources, correspondence, notices an...

P. G. Lejeune Dirichlet · 0 citations

From tech blogs

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MIT News · Artificial Intelligence Oct 30, 2026

Q&A: Hey AI, who should I vote for this year?

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 Oct 30, 2026

From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence

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.

GPT-Lab Oct 30, 2026

Black October? The Week AI Changed the Cybersecurity Conversation

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 Oct 29, 2026

Microsoft Research Focus – Edition #97

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.

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