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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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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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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Seasonal quantile forecasting of solar photovoltaic power using Q-CNN-GRU by Louiza Ait Mouloud, Aissa Kheldoun, Samira Oussidhoum, Hisham Alharbi, Saud Alotaibi, Thabet Alzahrani, Takele Ferede Agajie

“…Through sensitivity analysis, the influence of these additional inputs on forecast horizons and seasonal variability is systematically explored. The study reveals that integrating NWP data significantly improves the model’s predictive skill, particularly for longer forecast horizons and during transitional seasons like spring and fall, when solar variability is higher.…”
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Enhancing Indian summer monsoon prediction: Deep learning approach for skillful long-lead forecasts of rainfall by Kalpesh R. Patil, Takeshi Doi, J.V. Ratnam, Swadhin K. Behera

“…Analysis of saliency-based heatmaps indicated the high skill to be due to the model capturing the leading modes of climate variability, such as the Indian Ocean Dipole and El Niño-Southern Oscillation, realistically. …”
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Progressive Cluster-Guided Knowledge Distillation for Remote Sensing Image Scene Classification by Zhaopeng Deng, Zheng Zhou, Haoran Zhao, Xiaolin Chen, Danfeng Hong, Xin Sun

“…However, existing KD methods neglect the high interclass similarity and significant intraclass variability, as well as the imbalance of data features in RSIs. …”
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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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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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Gitelman Syndrome: A Rare Cause of Seizure Disorder and a Systematic Review by Muhammad Asim Shahzad, Maryam Mukhtar, Asrar Ahmed, Waqas Ullah, Rehan Saeed, Mohsin Hamid

“…It is caused by tubular defects at the level of distal convoluted tubules, mimicking a thiazide-like tumor. …”
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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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Spatial recognition and semi-quantification of epigenetic events in pancreatic cancer subtypes with multiplexed molecular imaging and machine learning by Krzysztof Szymoński, Natalia Janiszewska, Kamila Sofińska, Katarzyna Skirlińska-Nosek, Dawid Lupa, Michał Czaja, Marta Urbańska, Katarzyna Jurkowska, Kamila Konik, Marta Olszewska, Dariusz Adamek, Kamil Awsiuk, Ewelina Lipiec

“…We analyzed and semi-quantified the resulting molecular data, revealing significant variability in their epigenomes. DNA and histone modifications, specifically methylation and acetylation, were investigated. …”
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Toward long-range ENSO prediction with an explainable deep learning model by Qi Chen, Yinghao Cui, Guobin Hong, Karumuri Ashok, Yuchun Pu, Xiaogu Zheng, Xuanze Zhang, Wei Zhong, Peng Zhan, Zhonglei Wang

“…Abstract El Niño-Southern Oscillation (ENSO) is a prominent mode of interannual climate variability with far-reaching global impacts. Its evolution is governed by intricate air-sea interactions, posing significant challenges for long-term prediction. …”
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Mitigating bias in prostate cancer diagnosis using synthetic data for improved AI driven Gleason grading by Derek J. Van Booven, Cheng-Bang Chen, Oleksandr N. Kryvenko, Sanoj Punnen, Victor Sandoval, Sheetal Malpani, Ahmed Noman, Farhan Ismael, Yujie Wang, Rehana Qureshi, Joshua M. Hare, Himanshu Arora

“…Machine learning (ML) models offer potential for automated grading but are limited by dataset biases, staining variability, and data scarcity, reducing their generalizability. …”
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Depression detection methods based on multimodal fusion of voice and text by Zhenrong Xu, Yuan Gao, Fang Wang, Longqian Zhang, Li Zhang, Junke Wang, Jie Shu

“…Traditional diagnostics often rely on subjective judgments, leading to variability and inefficiency. This study proposes a fusion model for automated depression detection, leveraging bimodal data from voice and text. …”
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A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model by Soree Hwang, Nayeon Kwon, Dongwon Lee, Jongman Kim, Sumin Yang, Inchan Youn, Hyuk-June Moon, Joon-Kyung Sung, Sungmin Han

“…Physical fatigue significantly impacts safety and performance across industrial, athletic, and medical domains, yet its detection remains challenging due to individual variability and limited generalizability of existing methods. …”
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Automated classification of midpalatal suture maturation stages from CBCTs using an end-to-end deep learning framework by Omid Halimi Milani, Lauren Mills, Amanda Nikho, Marouane Tliba, Veerasathpurush Allareddy, Rashid Ansari, Ahmet Enis Cetin, Mohammed H. Elnagar

“…The feature extraction integrates Convolutional Neural Networks (CNN) architectures, such as EfficientNet and ResNet18, alongside our novel Multi-Filter Convolutional Residual Attention Network (MFCRAN) enhanced with Discrete Cosine Transform (DCT) layers. …”
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Assessment of a Hyperspectral Remote Sensing Model Performance for Particulate Phosphorus in Optically Shallow Lake Water by Banglong Pan, Wuyiming Liu, Zhuo Diao, Qianfeng Gao, Lanlan Huang, Shaoru Feng, Juan Du, Qi Wang, Jiayi Li, Jiamei Cheng

“…It also serves as one of the most significant sources of phosphorus for primary productivity, serving as a possible source of soluble reactive phosphorus, and contributing a sizable amount of the total phosphorus (TP), so monitoring the spatial and temporal variability of PP is crucial for understanding eutrophication in water bodies. …”
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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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