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Artificial intelligence

Advancing the service-oriented transition of smart cities: Evaluating the intelligent approval quality

Jiaqi Wang, Yali Wang

Integrative transcriptomic analysis of peripheral blood identifies NETosis-associated programs and a stratified adaptive immune suppression pattern in severe acute pancreatitis

Yaobin He, Kairui Liu, [ ... ], Youxing Huang

Artificial intelligence and machine learning for organizational and system-level functions in primary health care: A scoping review protocol

Pablo Galvez-Hernandez, Li-Anne Audet, Zahra Shakeri, Walter P. Wodchis

Transcriptomic associations and biomarkers in ANCA‑associated Glomerulonephritis and IgG4‑related Disease: A bioinformatics and machine learning study

Xu Li, Yufeng Qiao

Automatic bridge crack detections: A lightweight segmentation model for UAV inspection

Xuwei Dong, Zhibing Yu, Jiashuo Yuan, Jinpeng Dai

Proteomic machine-learning signatures associated with carotid plaque: A population-based hypothesis-generating study

Anna Szpakowicz, Wojciech Lesiński, [ ... ], Karol A. Kamiński

Comparative evaluation of convolutional and transformer-based object detection models for brain tumor localization in MRI

Farhan Naeem, Amara Haroon, [ ... ], Shahi Dost

Online labor trafficking and machine learning: A scoping review and research agenda

Rebaka Pradhan, Onn Shehory

How Generative Artificial Intelligence (GAI) reshapes students’ creativity in higher education: A chain path modeling Study from “Exploration–Exploitation” to “Co-Creation–Reflection”

Jie Xu, Yixuan Zeng

IoT-enabled smart home activity classification using BiLSTM and attention mechanisms

Sasmita Kumari Pradhan, Suryakanth V. Gangashetty

Optimisation of metabolic dysfunction-associated steatotic liver disease (MASLD) screening algorithm for resource-poor settings using machine learning

Chamila Mettananda, Kaveesha Sivasumithran, [ ... ], Anuradha Dassanayake

Cartoon style generation of animated image scenes based on improved GAN and RetNet

Zhenyao Jin, Lei Yang

Predicting student academic performance using TabPFN and SHAP: An interpretable machine learning approach

Qiang Yin

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