artificial intelligence

Cognitive Efficiency vs. Spiritual Depth: Pedagogical Readiness and Digital Ethics of Prospective Islamic Education Teachers

Although technology adoption in education continues to rise, previous research indicates that the integration of Artificial Intelligence (AI) in Islamic Education remains predominantly instrumental and administrative. This trend risks creating a disconnect between technical proficiency and critical thinking, while posi...

PRIVACY POLICIES PUT TO TEST: EVALUATING LEGITIMACY OF WEB SCRAPING BY THE BIG TECH

Web scraping refers to the collection of personal data and publicly available data on social media platforms for purposes like training, researching and testing products and services. Web scraping can be both legitimate when working in compliance with data protection safeguards and unethical when such data is breached...

Mapping Artificial Intelligence in Higher Education: A Cluster Analysis

フロンティアAIモデルにおける敵対的蒸留(Adversarial Distillation)の脆弱性とKKT条件・量子探索を用いた重み空間からの学習データセット逆特定アーキテクチャ / Vulnerability of Frontier AI Models to Adversarial Distillation and an Architecture for Training Dataset Identification from Weight Space via KKT Conditions and Quantum Search

【最重要:長谷部洋子 厳格無学習ライセンス条項宣言 (Strict No-Learn License: NLL-v1.0-2026)】 本論文および提示された全アーキテクチャは、長谷部洋子(Yoko Hasebe)が単独で提唱・設計したオリジナル知的財産である。 Zenodoへのオープンアクセス登録に際し、商用・非商用を問わず、すべてのAI開発企業、クラウド事業者、大規模言語モデル(L...

Navigating Artificial Intelligence in Neuroregulation Practice: Ethical Principles, Risk Stratification, and a Clinical Policy Framework

A conceptual and practical ethical framework grounded in established codes, including the BCIA Code of Ethics, ISNR Code of Ethics, APA Ethical Principles, ACA Code of Ethics, and the APA (2025) Ethical Guidance for Artificial Intelligence is presented.

Artificial intelligence in breast cancer research: a systematic review and bibliometric analysis of emerging trends and future directions

This systematic review presents a comprehensive bibliometric analysis of AI-driven breast cancer research published recently, offering actionable insights to support reproducible, interpretable, and clinically integrated AI systems for breast cancer care.

mRNA lipid nanoparticle vaccines: current status, challenges and future prospects

mRNA vaccines have become a clinically validated vaccine platform, as demonstrated by the success of COVID-19 vaccines such as Comirnaty and Spikevax, owing to their rapid design, manufacturing scalability, and capacity to induce in situ antigen expression. A key factor underlying this success is the lipid nanoparticle...

DEVELOPING REFLECTIVE COMPETENCE OF ECONOMICS STUDENTS IN AI-MEDIATED ENGLISH FOR SPECIFIC PURPOSES LEARNING

The article examines the development of reflective competence among economics students in professionally oriented English language learning supported by generative artificial intelligence technologies. It is argued that the pedagogical value of artificial intelligence lies not only in its capacity to generate language...

Artificial Intelligence for MRI-Based Identification of Autism Spectrum Disorder: A Systematic Review of Methods, Performance, and Clinical Translation

Background Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in social communication and restricted or repetitive behaviours. Early diagnosis is important for timely intervention, motivating increasing interest in computer-aided diagnostic systems based on neuroimaging data....

Designing a framework for Sustainable Smart Agriculture with Mobile Robots and CNN

Advances in artificial intelligence, robotics, and computer vision enable intelligent gardening systems that reduce manual monitoring. This study presents a smart gardening framework integrating a mobile robot with convolutional neural network (CNN) flower recognition. The robot captures garden images, while the CNN cl...

Development and internal temporal validation of an explainable XGBoost model for predicting clinical pregnancy after frozen embryo transfer

Key message An explainable machine learning model was developed using comprehensive clinical variables to predict clinical pregnancy outcomes following frozen embryo transfer. Compared with conventional statistical models, the extreme gradient boosting–based model showed numerically higher discrimination and provided i...

A study on Energy Saving Methods for Sewage Treatment Systems using Artificial Intelligence