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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A Multi-Scale Adaptive Fusion Network: End-to-End Interpretable Small-Sample Classifier for Motor Imagery EEG by Qiulei Han, Yan Sun, Ze Song, Hongbiao Ye, Tingwei Chen, Jian Zhao

“…However, the non-stationarity and individual variability of EEG signals present significant challenges to improving decoding accuracy. …”
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Improving Hand Pose Recognition Using Localization and Zoom Normalizations over MediaPipe Landmarks by Miguel Ángel Remiro, Manuel Gil-Martín, Rubén San-Segundo

“…This can be mitigated by employing MediaPipe to facilitate the efficient extraction of representative landmarks from static images combined with the use of Convolutional Neural Networks. Extracting these landmarks from the hands mitigates the impact of lighting variability or the presence of complex backgrounds. …”
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A dual-branch deep learning model based on fNIRS for assessing 3D visual fatigue by Yan Wu, Yan Wu, Yan Wu, TianQi Mu, SongNan Qu, XiuJun Li, XiuJun Li, XiuJun Li, Qi Li, Qi Li, Qi Li

“…Given the time-series nature of fNIRS data and the variability of fatigue responses across different brain regions, a dual-branch convolutional network was constructed to separately extract temporal and spatial features. …”
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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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A Deep Learning Inversion Method for 3D Temperature Structures in the South China Sea with Physical Constraints by Dongcan Xu, Yahao Liu, Yuan Kong

“…This study develops a Convolutional Long Short-Term Memory (ConvLSTM) neural network, integrating multi-source satellite remote sensing data, to reconstruct the Ocean Subsurface Temperature Structure (OSTS). …”
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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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CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland by B. Kraft, M. Schirmer, W. H. Aeberhard, M. Zappa, S. I. Seneviratne, L. Gudmundsson

“…We test two sequential deep-learning architectures: a long short-term memory (LSTM) model, which is a recurrent neural network able to learn complex temporal features from sequences, and a convolution-based model, which learns temporal dependencies via 1D convolutions in the time domain. …”
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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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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

“…EGAN constructs a spatiotemporal graph representation that integrates physical proximity, ecological similarity, and temporal dynamics, and applies graph convolutional encoders to learn expressive spatial features. …”
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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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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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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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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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GAT-ADNet: Leveraging Graph Attention Network for Optimal Power Flow in Active Distribution Network With High Renewables by Dinesh Kumar Mahto, Mahipal Bukya, Rajesh Kumar, Akhilesh Mathur, Vikash Kumar Saini

“…The GAT model exhibited less variability in its median error 0.22, 0.21, and 0.038, respectively, in each case. …”
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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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