An Effective Deep Neural Network Architecture for EEG-Based Recognition of Emotions by Khadidja Henni, Neila Mezghani, Amar Mitiche, Lina Abou-Abbas, Amel Benazza-Ben Yahia

“…Current research in EEG-based emotion recognition faces significant challenges due to the high-dimensionality and variability of EEG signals, which complicate accurate classification. …”
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TDFNet: twice decoding V-Mamba-CNN Fusion features for building extraction by Wenlong Wang, Peng Yu, Mengmeng Li, Xiaojing Zhong, Yuanrong He, Hua Su, Yunxuan Zhou

“…Therefore, methods integrating convolutional neural networks (CNNs) and visual transformers (ViTs) are popular nowadays. …”
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Effect of natural and synthetic noise data augmentation on physical action classification by brain–computer interface and deep learning by Yuri Gordienko, Nikita Gordienko, Vladyslav Taran, Anis Rojbi, Sergii Telenyk, Sergii Telenyk, Sergii Stirenko

“…The detrended fluctuation analysis (DFA) was applied to investigate the fluctuation properties and calculate the correspondent Hurst exponents H for the quantitative characterization of the fluctuation variability. H values for the low time window scales (< 2 s) are higher in comparison with ones for the bigger time window scales. …”
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A New Transformer Network for Short-Term Global Sea Surface Temperature Forecasting: Importance of Eddies by Tao Zhang, Pengfei Lin, Hailong Liu, Pengfei Wang, Ya Wang, Weipeng Zheng, Zipeng Yu, Jinrong Jiang, Yiwen Li, Hailun He

“…This study introduces a specialized Transformer model (U-Transformer) to forecast global short-term SST variability and compares its performance with Convolutional Long Short-Term Memory (ConvLSTM) and Residual Neural Network (ResNet) models. …”
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Spatiotemporal Forecasting of Solar and Wind Energy Production: A Robust Deep Learning Model with Attention Framework by Md. Shadman Abid, Razzaqul Ahshan, Mohammed Al-Abri, Rashid Al Abri

“…The variability in the spatiotemporal distribution of power generation is a significant challenge for accurately predicting renewable energy production patterns. …”
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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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Advanced Multi-Level Ensemble Learning Approaches for Comprehensive Sperm Morphology Assessment by Abdulsamet Aktas, Taha Cap, Gorkem Serbes, Hamza Osman Ilhan, Hakkı Uzun

“…<b>Objectives:</b> This study aims to develop a robust and fully automated sperm morphology classification framework capable of accurately identifying a wide range of morphological abnormalities, thereby minimizing observer variability and improving diagnostic support in reproductive healthcare. …”
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Machine learning-based model for behavioural analysis in rodents applied to the forced swim test by Andrea Della Valle, Sara De Carlo, Gregorio Sonsini, Sebastiano Pilati, Andrea Perali, Massimo Ubaldi, Roberto Ciccocioppo

“…Despite its widespread use, the FST behaviours are still manually scored, resulting in a labor-intensive and time-consuming process that is prone to human bias and variability. Despite eliminating some biases, existing automated systems are costly and typically only able to distinguish between immobility and active behaviours. …”
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Cross-dataset evaluation of deep learning models for crack classification in structural surfaces by Rashid Taha, Mokji Musa Mohd, Rasheed Mohammed

“…ResNet50 had managed to hold its own across the orchards of domains but was still a little troubled with the variability of the surface and noise, whereas LSTM became less useful as it struggled with the extraction of spatial characteristics. …”
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OcularAge: A Comparative Study of Iris and Periocular Images for Pediatric Age Estimation by Naveenkumar G. Venkataswamy, Poorna Ravi, Stephanie Schuckers, Masudul H. Imtiaz

“…A multi-task deep learning framework was employed to jointly perform age prediction and age-group classification, enabling a systematic exploration of how different convolutional neural network (CNN) architectures, particularly those adapted for non-square ocular inputs, capture the complex variability inherent in pediatric eye images. …”
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Automated Detection and Biomarker Identification Associated with the Structural and Functional Progression of Glaucoma on Longitudinal Color Fundus Images by Iyad Majid, Zubin Mishra, Ziyuan Chris Wang, Vikas Chopra, Dale Heuer, Zhihong Jewel Hu

“…The diagnosis of primary open-angle glaucoma (POAG) progression based on structural imaging such as color fundus photos (CFPs) is challenging due to the limited number of early biomarkers, as commonly determined by clinicians, and the inherent variability in optic nerve heads (ONHs) between individuals. …”
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CoastVisionNet: transformer with integrated spatial-channel attention for coastal land cover classification by Li Yang, Liu Yijun, Wenhao Deng

“…Coastal zones, being highly dynamic and spatially heterogeneous, require sophisticated semantic modeling strategies that account for both spectral variability and spatial morphology. While traditional convolutional neural networks and fixed-resolution transformer models have made notable strides, they often struggle to generalize across varying topographies and spectral distributions. …”
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A CrossMod-Transformer deep learning framework for multi-modal pain detection through EDA and ECG fusion by Jaleh Farmani, Ghazal Bargshady, Stefanos Gkikas, Manolis Tsiknakis, Raul Fernandez Rojas

“…Physiological indicators offer valuable insights into pain-related states and are generally less influenced by individual variability compared to behavioural modalities, such as facial expressions. …”
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Less Is More: Brain Functional Connectivity Empowered Generalizable Intention Classification With Task-Relevant Channel Selection by Haowei Lou, Zesheng Ye, Lina Yao, Yu Zhang

“…Moreover, the model trained for one set of subjects cannot easily be adapted to other sets due to inter-subject variability, which creates even higher over-fitting risks. …”
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Private Data Incrementalization: Data-Centric Model Development for Clinical Liver Segmentation by Stephanie Batista, Miguel Couceiro, Ricardo Filipe, Paulo Rachinhas, Jorge Isidoro, Inês Domingues

“…<i>Private Data Incrementalization</i> thus offers a scalable strategy for building resilient segmentation models, ultimately benefiting clinical workflows, patient care, and healthcare resource management by addressing the variability inherent in clinical imaging data.…”
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RL-Cervix.Net: A Hybrid Lightweight Model Integrating Reinforcement Learning for Cervical Cell Classification by Shakhnoza Muksimova, Sabina Umirzakova, Jushkin Baltayev, Young-Im Cho

“…The model demonstrated superior accuracy and interpretability compared to existing methods, addressing variability and complexities inherent in cytological images. …”
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Building consistency in explanations: Harmonizing CNN attributions for satellite-based land cover classification by Timo T. Stomberg, Lennart A. Reißner, Martin G. Schultz, Ribana Roscher

“…Furthermore, the unique characteristics of remote sensing imagery pose additional challenges for attribution interpretation: it primarily comprises continuous “stuff” classes rather than objects, exhibits fine-grained spatial variability, contains mixed pixels, is often multispectral, and exhibits spatially heterogeneity. …”
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Diagnosis of clear cell renal cell carcinoma via a deep learning model with whole-slide images by Weixing Jiang, Siyu Qi, Cancan Chen, Wenying Wang, Xi Chen

“…Background: Traditional pathological diagnosis methods have limitations in terms of interobserver variability and the time consumption of evaluations. …”
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Unbiased identification of cell identity in dense mixed neural cultures by Sarah De Beuckeleer, Tim Van De Looverbosch, Johanna Van Den Daele, Peter Ponsaerts, Winnok H De Vos

“…Induced pluripotent stem cell (iPSC) technology is revolutionizing cell biology. However, the variability between individual iPSC lines and the lack of efficient technology to comprehensively characterize iPSC-derived cell types hinder its adoption in routine preclinical screening settings. …”
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A Computer-Aided Approach to Canine Hip Dysplasia Assessment: Measuring Femoral Head–Acetabulum Distance with Deep Learning by Pedro Franco-Gonçalo, Pedro Leite, Sofia Alves-Pimenta, Bruno Colaço, Lio Gonçalves, Vítor Filipe, Fintan McEvoy, Manuel Ferreira, Mário Ginja

“…Canine hip dysplasia (CHD) screening relies on radiographic assessment, but traditional scoring methods often lack consistency due to inter-rater variability. This study presents an AI-driven system for automated measurement of the femoral head center to dorsal acetabular edge (FHC/DAE) distance, a key metric in CHD evaluation. …”
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