explainable ai

Cognitive and Neural Interfaces in Bridging Brain, Body, and Virtual Worlds

The neural interface, coupled with wearable artificial intelligence (AI) and cognitive computing, is reshaping the way human beings interact with digital ecosystems, especially in immersive metaverse worlds. This chapter explains that cognitive and neural interfaces may be an epochal nexus among the human brain, physio...

Evaluation and screening of rice (Oryza sativa L.) genotypes for drought tolerance under reproductive-stage stress

Drought stress at the reproductive-stage is a major constraint limiting rice productivity in rainfed ecosystems. The present study evaluated 450 rice (Oryza sativa L.) genotypes along with 8 checks under normal and reproductive-stage drought stress conditions during kharif 2020 at Bihar Agricultural University, Sabour,...

Enhancing Financial Decision Quality in Smart Organizations through Explainable AI and Real Time Analytics with AI Governance Maturity

This study investigates the links among explainable AI capability, real time financial analytics capability, and financial decision quality within smart organizations while assessing how AI governance maturity moderates those links. Drawing on organizational information processing theory and dynamic capabilities theory...

Explainable AI (XAI) for Credit Scoring Model Validation

Artificial Intelligence-Assisted FT-IR Spectral Sensing: From Molecular Fingerprints to Quantitative and Interpretable Chemical Information

Overall, AI can extend FT-IR from qualitative fingerprinting to quantitative and predictive spectral sensing, provided that future studies emphasize method-centered validation, reproducibility, transferability, and chemically meaningful explanation rather than relying solely on classification accuracy and regression er...

What a Crossref record shows when a paper is retracted: one month of retraction deposits (v2)

**A field-level audit of one month of retraction deposits (v2)** Author: Trafalgar Law (independent). Data pulled from the public Crossref REST API on 2026-09-23. Sample and reproduction query included. This is a metadata audit, not an accusation against any publisher or author. ## Why On 2026-09-19 the record for a fi...

GiriNeoSeizure™ CDSS — Neonatal Seizures: Stabilise, Confirm, Treat & Reassess (Edition 1, Version 2.0)

GiriNeoSeizure™ CDSS is a single-file, offline, interactive clinical decision-support system for neonatal seizures. It is part of the NeonatAIlogy™ series in the GAIR Innovation Registry and is governed by the GIRISH™ Prompting Architecture. The tool turns the 2023 ILAE Task Force guideline, Cochrane evidence on antise...

m-mdy-m/cdin: cdin v0.1.0-beta.7

[0.1.0-beta.7] — 2026-09-23 Largest release so far: 102 commits, 218 files changed, +21,760 / −149. Headline features are full UTF-8/RTL/Arabic shaping support, a theme system with 10 built-in themes, an optional-plugin system, a Lua test suite, Docker images, and a complete documentation website. The beta cycle contin...

Attitudes towards AI and the Demand for eXplainable AI: Does Spirituality Matter?

Conditions of Admission and Participation for People Testing and Raising AI Models. Specification v0.1

An open specification of conditions of admission and participation for people who test, evaluate and raise AI models. It covers declaration instead of prohibition of AI tools, a field for observations that cannot yet be named, deferred reports on what came of a decision, payment for agreed participation including negat...

Deteksi Cyberbullying pada Twitter menggunakan Distilbert dan (Explainable Ai) Shap

The prevalence of cyberbullying on Twitter demands a fast and transparent automated moderation system. This study designs an efficient and explainable binary cyberbullying detection system using DistilBERT and the SHAP-based Explainable AI (XAI) method. The dataset, sourced from Kaggle, consists of 13,169 raw data filt...

Designing a human-in-the-loop AI iterative process for research writing: a framework for preserving productive struggle

This Hypothesis and Theory paper addresses a design problem at the center of AI-assisted research writing: which cognitive demands should AI reduce, and which must remain the writer’s own work. Generative AI can absorb the effort of drafting, organizing, and synthesizing prose, but that same effort is where understandi...