Spatial–Temporal Transformer for Optimizing Human Health Through Skeleton-Based Body Sports Action Recognition by Faze Liang, Lejia Ou, Zujun Lei, Xiaohong Tu, Kai Xin

“…These components collectively enable the framework to handle complex co-movement patterns, occlusions, and variability in execution styles. We evaluate FG-STTrans on two diverse datasets: the Workout Action Video Dataset (WAVd) and the YogaVid dataset. …”
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A Graphite Ore Grade Recognition Method Based on Improved Inception-ResNet-v2 Model by Xueyu Huang, Renjie Pan, Jionghui Wang

“…With the rapid advancement of technology, intelligent identification of graphite ore grade in graphite mines has emerged as an essential requirement. To address the variability and low timeliness of traditional manual methods and the limited accuracy of deep learning due to image complexity and feature similarity, we propose an improved Inception-ResNet-v2 model for graphite ore grade recognition. …”
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Enabling Automated Device Size Selection for Transcatheter Aortic Valve Implantation by Patricio Astudillo, Peter Mortier, Johan Bosmans, Ole De Backer, Peter de Jaegere, Matthieu De Beule, Joni Dambre

“…We propose a method combining two deep convolutional neural networks followed by a postprocessing step. …”
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AI-Based Forecasting in Renewable-Rich Microgrids: Challenges and Comparative Insights by Martins Osifeko, Josiah Lange Munda

“…Amid the accelerating global transition to renewable energy, accurate forecasting has become the cornerstone for unlocking the full potential of solar and wind power in modern power grids, especially in regions with high resource variability. This study begins with a review of forecasting challenges in microgrids located in developing areas where issues related to data sparsity, model limitations, environmental variability, and operational limitations are prevalent. …”
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Linking European Temperature Variations to Atmospheric Circulation With a Neural Network: A Pilot Study in a Climate Model by Enora Cariou, Julien Cattiaux, Saïd Qasmi, Aurélien Ribes, Christophe Cassou, Antoine Doury

“…This exploratory work opens up promising prospects for estimating the contribution of atmospheric variability to observed temperature variations.…”
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Unlocking chickpea flour potential: AI-powered prediction for quality assessment and compositional characterisation by Ali Zia, Muhammad Husnain, Sally Buck, Jonathan Richetti, Elizabeth Hulm, Jean-Philippe Ral, Vivien Rolland, Xavier Sirault

“…However, the inherent variability in the composition of chickpea flour, influenced by genetic diversity, environmental conditions, and processing techniques, poses significant challenges to standardisation and quality control. …”
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Surface water mapping from remote sensing in Egypt’s dry season using an improved U-Net model with multi-scale information and attention mechanism by Yong Li, Xiuhui Liu, Vagner Ferreira, Heiko Balzter, Huiyu Zhou, Ying Ge, Meiyun Lai, Simin Chu, Han Ding, Zhenrong Gu

“…However, existing water detection methods face challenges in accurately identifying water bodies with high spatial and spectral variability, especially in arid regions during dry seasons. …”
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Heterogeneous transfer learning model for improving the classification performance of fNIRS signals in motor imagery among cross-subject stroke patients by Jin Feng, YunDe Li, ZiJun Huang, Yehang Chen, SenLiang Lu, RongLiang Hu, QingHui Hu, YuYao Chen, XiMiao Wang, Yong Fan, Jing He

“…CHTLM advances MI-fNIRS-based brain-computer interfaces in stroke rehabilitation by mitigating data scarcity and variability challenges.…”
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Classification of Structural and Functional Development Stage of Cardiomyocytes Using Machine Learning Techniques by V. R. Bondarev, K. O. Ivanko, N. G. Ivanushkina

“…But since cardiomyocytes are objects with a high level of complexity and have significant morphological variability, automatic classification is complicated by the lack of implemented methods. …”
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Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic–Random Forest by Gulfem Ozlu Ucan, Omar Abboosh Hussein Gwassi, Burak Kerem Apaydin, Bahadir Ucan

“…However, its effectiveness is challenged by methodological variability and biological differences between individuals. …”
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Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism by Fayez Saud Alreshidi, Mohammad Alsaffar, Rajeswari Chengoden, Naif Khalaf Alshammari

“…In addition, the article explores the importance of analysing mean heart rate variability to differentiate between healthy and abnormal heart rhythms. …”
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Approaches to Proxy Modeling of Gas Reservoirs by Alexander Perepelkin, Anar Sharifov, Daniil Titov, Zakhar Shandrygolov, Denis Derkach, Shamil Islamov

“…On average, the ST-GNN method reduces computational time by a factor of 4.3 compared to traditional hydrodynamic models, with a median predictive error not exceeding 10% across diverse datasets, despite variability in specific scenarios. The ST-GNN framework demonstrates promising potential as a tool for operational and strategic planning.…”
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Deep Learning Techniques for Lung Cancer Diagnosis with Computed Tomography Imaging: A Systematic Review for Detection, Segmentation, and Classification by Kabiru Abdullahi, Kannan Ramakrishnan, Aziah Binti Ali

“…However, challenges persist, including dataset scarcity, annotation variability, and population generalizability. Hybrid architectures, such as convolutional neural networks (CNNs) and transformers, show promise in improving nodule localization. …”
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Application of Atmospheric Gases and Particulate Matter to the Assessment of Urban Heat Island by Christian Mark Salvador, Pablo Fernandez, Kelsey Carter, Joanna Tannous, David Weston, Christopher DeRolph, Melanie A. Mayes

“…Objectives This study aims to utilize the variability of atmospheric components such as particulate matter (PM), inorganic gases, and volatile organic compounds (VOCs) as direct tracers of the degree of urbanization for ground-based measurements to fully comprehend UHI in convoluted regions with indistinct delineation of urban and nonurban environments. …”
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DermViT: Diagnosis-Guided Vision Transformer for Robust and Efficient Skin Lesion Classification by Xuejun Zhang, Yehui Liu, Ganxin Ouyang, Wenkang Chen, Aobo Xu, Takeshi Hara, Xiangrong Zhou, Dongbo Wu

“…Currently, skin lesion classification faces challenges such as lesion–background semantic entanglement, high intra-class variability, artifactual interference, and more, while existing classification models lack modeling of physicians’ diagnostic paradigms. …”
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Thyroid nodule segmentation in ultrasound images using transformer models with masked autoencoder pre-training by Yi Xiang, Rajendra Acharya, Quan Le, Jen Hong Tan, Chiaw-Ling Chng

“…The difficulty arises from factors such as the absence of prior knowledge about the thyroid region, low contrast between anatomical structures, and speckle noise, all of which obscure boundary detection and introduce variability in nodule appearance across different images.MethodsTo address these challenges, we propose a transformer-based model for thyroid nodule segmentation. …”
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Klasifikasi Ekspresi Wajah Menggunakan Covolutional Neural Network by Ahmad Taufiq Akbar, Shoffan Saifullah, Hari Prapcoyo

“…Abstract Facial expression recognition is a significant challenge in image processing and human-computer interaction due to its inherent complexity and variability. This study proposes a simple Convolutional Neural Network (CNN) architecture to enhance the efficiency of emotion classification on small datasets. …”
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Predicting Epileptic Seizures Using EfficientNet-B0 and SVMs: A Deep Learning Methodology for EEG Analysis by Yousif A. Saadoon, Mohamad Khalil, Dalia Battikh

“…The EfficientNet-B0 backbone ensures high accuracy with computational efficiency, while the SVM ensemble enhances prediction reliability by mitigating noise and variability in EEG data.…”
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Artificial intelligence in acupuncture: bridging traditional knowledge and precision integrative medicine by Guo-Liang Hou, Bao-Qiang Dong, Ben-Xing Yu, Jian-Yu Dai, Xing-Xing Lin, Ze-Zhong Cheng

“…Despite their potential, current implementations are constrained by limited and heterogeneous datasets, annotation variability, and gaps in clinical validation. We analyze key methodological innovations and challenges, and recommend future directions including the construction of federated multimodal data platforms, development of explainable AI frameworks, and promotion of open science practices. …”
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Development and evaluation of deep learning models for cardiotocography interpretation by Nicole Chiou, Nichole Young-Lin, Christopher Kelly, Julie Cattiau, Tiya Tiyasirichokchai, Abdoulaye Diack, Sanmi Koyejo, Katherine Heller, Mercy Asiedu

“…Abstract The variability in the visual interpretation of cardiotocograms (CTGs) poses substantial challenges in obstetric care. …”
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