A Deep Learning Inversion Method for 3D Temperature Structures in the South China Sea with Physical Constraints by Dongcan Xu, Yahao Liu, Yuan Kong

“…The proposed approach advances ocean remote sensing by synergizing data-driven learning with thermodynamic principles, offering a robust framework for understanding the South China Sea’s thermal variability.…”
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A Deep Learning Approach for Mental Fatigue State Assessment by Jiaxing Fan, Lin Dong, Gang Sun, Zhize Zhou

“…This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. …”
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Automatic Mushroom Species Classification Model for Foodborne Disease Prevention Based on Vision Transformer by Boyuan Wang

“…These results surpass previous approaches in reducing intraclass variability and generating well-separated feature embeddings. …”
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Ensemble Approach for Image Recompression-Based Forgery Detection by Se-Jun Ham, Van-Ha Hoang, Chun-Su Park

“…With the advances in deep learning (DL), convolutional neural network (CNN) and Transformer models have emerged as prominent tools in this field. …”
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Deep learning-based object detection for environmental monitoring using big data by Wenbo Lin, Tingting Li, Xiao Li

“…However, high-dimensionality, spatiotemporal variability, and heterogeneous data sources continue to pose major challenges.MethodsIn this work, we introduce the Environmental Graph-Aware Neural Network (EGAN), a novel framework designed to model and analyze large-scale, multi-modal environmental datasets. …”
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Can explainable AI classify shrike (Laniidae) eggs by uncovering species-wide pigmentation patterns? by Paweł Pstrokoński, Łukasz Roszkowiak, Anna Korzyńska, Wojciech Wójcik, Martin Päckert, Joanna Rosenberger, Dominika Mierzwa-Szymkowiak, Magdalena Sepkowska, Jan Lontkowski, Marek Słupek, Krzysztof Damaziak

“…The genus Lanius, known for its distinctive pigmentation patterns, shows considerable variability within species, making it an intriguing but poorly understood group. …”
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AcuSim: A Synthetic Dataset for Cervicocranial Acupuncture Points Localisation by Qilei Sun, Jiatao Ma, Paul Craig, Linjun Dai, Eng Gee Lim

“…It includes a creation of 63,936 RGB-D images and 504 synthetic anatomical models with 174 volumetric acupoints annotated, to capture the variability and diversity of human anatomies. The study validates a convolutional neural network (CNN) on the proposed dataset with an accuracy of 99.73% and shows that 92.86% of predictions in validation set align within a 5mm threshold of margin error when compared to expert-annotated data. …”
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A novel explainable deep learning framework for reconstructing South Asian palaeomonsoons by K. M. R. Hunt, K. M. R. Hunt, S. P. Harrison

“…<p>We present novel explainable deep learning techniques for reconstructing South Asian palaeomonsoon rainfall over the last 500 years, leveraging long instrumental precipitation records and palaeoenvironmental datasets from South and East Asia to build two types of models: dense neural networks (“regional models”) and convolutional neural networks (CNNs). The regional models are trained individually on seven regional rainfall datasets, and while they capture decadal-scale variability and significant droughts, they underestimate inter-annual variability. …”
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A Fusion Strategy for High-Accuracy Multilayer Soil Moisture Downscaling and Mapping by Xu Zhang, Xin Liu, Xiang Zhang, Aminjon Gulakhmadov, Jiefeng Wu, Xihui Gu, Won-Ho Nam, Panda Rabindra Kumar, Veber Afonso Figueiredo Costa, Mahlatse Kganyago, Berhanu Keno Terfa, Wenying Du, Chao Wang, Peng Wang, Jing Yuan, Nengcheng Chen

“…First, the ESTARFM spatiotemporal fusion algorithm was applied to combine high-resolution MODIS data with long-term GLASS data, generating daily SM driving variables (NDVI and LST) at a 1-km resolution. Subsequently, the LightGBM downscaling algorithm was used to reduce the spatial resolution of GLEAM SSM and RZSM data from 0.25&#x00B0; to daily 1-km resolution. …”
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Lightweight Apple Leaf Disease Detection Algorithm Based on Improved YOLOv8 by LUO Youlu, PAN Yonghao, XIA Shunxing, TAO Youzhi

“…The results showed that the improved model could maintain a high detection accuracy in complex and variable scenes, with mAP50 and mAP50:95 increasing by 1.7% and 1.2%, respectively, compared to the original model. …”
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EFNet: estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learning by Waqas Ali, Wesam Alsabban, Muhammad Shahbaz, Ali Al-Laith, Bassam Almogadwy

“…Accurate heart failure prediction using cardiac ultrasound is challenging due to operator dependency and inconsistent video quality, resulting in significant interobserver variability. To address this, we developed a method integrating convolutional neural networks (CNN) and transformer models for direct EF estimation from ultrasound video scans. …”
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FROM PIXELS TO DIAGNOSIS: A DEEP LEARNING FRAMEWORK FOR HISTOPATHOLOGICAL IMAGE ANALYSIS IN CANINE TESTICULAR PATHOLOGY

“…Histopathological definition of testicular pathologies may be prone to inter-observer variability, thus leading to an erroneous diagnosis or to the exclusion of differential concurrent alteration. …”
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Artificial Intelligence in Patch Testing: Comprehensive Review of Current Applications and Future Prospects in Dermatology by Hilary S Tang, Joseph Ebriani, Matthew J Yan, Shannon Wongvibulsin, Mehdi Farshchian

“… Abstract BackgroundThe integration of artificial intelligence (AI) into patch testing for allergic contact dermatitis (ACD) holds the potential to standardize diagnoses, reduce interobserver variability, and improve overall diagnostic accuracy. …”
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AI-based orchard monitoring at night: Enhancing sustainable fruit production through real-time apple detection by Kutyrev Alexey, Khort Dmitry, Smirnov Igor, Zubina Valeria

“…The study highlights the complexities arising from the variability of lighting in natural daytime conditions of industrial gardens, which makes object recognition difficult due to the influence of various natural factors. …”
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Machine Learning for Human Activity Recognition: State-of-the-Art Techniques and Emerging Trends by Md Amran Hossen, Pg Emeroylariffion Abas

“…Despite significant progress, HAR still faces critical challenges, including handling environmental variability, ensuring model interpretability, and achieving high recognition accuracy in complex, real-world scenarios. …”
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Forecasting renewable energy for microgrids using machine learning by Piyumi Sudasinghe, Damayanthi Herath, Isiwara Karunarathne, Hansani Weeratunge, Lahiru Jayasuriya

“…However, the inherent variability of distributed wind and solar generation within microgrids presents operational stability challenges concerning voltage regulation and frequency stability. …”
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Performance Enhancement of EEG Signatures for Person Authentication Using CNN BiLSTM Method by Ashish Ranjan Mishra, Rakesh Kumar, Rajkumar Saini

“…The broad implementation of EEG-based authentication systems has been hindered by problems such as noise, variability, and inter-subject variances despite the potential distinctiveness of EEG signals. …”
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MFENet: A Multi-Feature Extraction Network for Enhanced Emotion Detection Using EEG and STFT by N. Ramesh Babu, Viswanathan Vadivel

“…However, the inherent nonstationarity of EEG signals and individual variability across subjects complicate the development of models that generalize reliably across diverse affective states. …”
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Artificial Intelligence for data modeling in triboelectric nanogenerators by Chenjia Li, Ali Matin Nazar

“…The review also addresses key challenges such as data variability, environmental robustness, and algorithmic scalability, and future directions in hybrid energy systems, adaptive algorithms, and cross-disciplinary collaboration for sustainable, intelligent sensing technologies.…”
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MRSNet: Multi-Resolution Scale Feature Fusion-Based Universal Density Counting Network by Yi Zhang, Wei Song, Mingyue Shao, Xiangchun Liu

“…The current methods, whether CNNs with fixed convolutional kernel sizes or Transformers with fixed attention sizes, struggle to handle such variability effectively. …”
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