Spatio-Temporal Graph Neural Networks for Streamflow Prediction in the Upper Colorado Basin by Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

“…Streamflow prediction is vital for effective water resource management, enabling a better understanding of hydrological variability and its response to environmental factors. …”
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DRDA-Net: Deep Residual Dual-Attention Network with Multi-Scale Approach for Enhancing Liver and Tumor Segmentation from CT Images by Wail M. Idress, Yuqian Zhao, Khalid A. Abouda, Shaodi Yang

“…The accurate segmentation of liver and tumors from clinical CT images plays a crucial role in selecting therapeutic strategies for liver disease and treatment monitoring but remains challenging due to liver shape variability, proximity to other organs, low contrast between tumors and healthy tissues, and unclear lesion boundaries. …”
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Generation of Seismocardiography Heartbeats Using a Wasserstein Generative Adversarial Network With Feature Control by James Skoric, Yannick D'Mello, David V. Plant

“…<italic>Results</italic>: The model effectively replicated SCG signal morphology, while maintaining a level of variance which matches the variability of cardiac activity. Comparisons with real SCG waveforms yielded Pearson&#x0027;s r-squared correlation of 0.62 for average heartbeats. …”
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Artificial Vision Systems for Mobility Impairment Detection: Integrating Synthetic Data, Ethical Considerations, and Real-World Applications by Santiago Felipe Luna-Romero, Mauren Abreu de Souza, Luis Serpa Andrade

“…Our analysis reveals that convolutional neural network (CNN) approaches, such as YOLO and Faster R-CNN, frequently outperform traditional computer vision methods in accuracy and real-time efficiency, though their success depends on the availability of large, high-quality datasets that capture real-world variability. …”
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Enhancing Attendance Management Through Face Recognition Technology: A Case Study at Rugarama School of Nursing and Midwifery. by Taremwa, Benjamin

“…However, limitations such as lighting variability and dataset size indicate further refinements are needed to optimize the system for broader implementation.…”
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Transformers for Neuroimage Segmentation: Scoping Review by Maya Iratni, Amira Abdullah, Mariam Aldhaheri, Omar Elharrouss, Alaa Abd-alrazaq, Zahiriddin Rustamov, Nazar Zaki, Rafat Damseh

“…Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation. …”
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Multi-Scale Hierarchical Feature Fusion for Infrared Small-Target Detection by Yue Wang, Xinhong Wang, Shi Qiu, Xianghui Chen, Zhaoyan Liu, Chuncheng Zhou, Weiyuan Yao, Hongjia Cheng, Yu Zhang, Feihong Wang, Zhan Shu

“…Traditional methods rely on assumption-based modeling and manual design, struggling to handle the variability of real-world scenarios. Although convolutional neural networks (CNNs) increase robustness to diverse scenes with a data-driven paradigm, many CNN-based methods are insufficient in capturing fine-grained details necessary for small targets and are less effective during multi-scale feature fusion. …”
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Infilling of missing rainfall radar data with a memory-assisted deep learning approach by J. Meuer, L. M. Bouwer, L. M. Bouwer, F. Kaspar, R. Lehmann, W. Karl, T. Ludwig, C. Kadow

“…Although recent machine learning advancements have shown promise in addressing missing meteorological or satellite observations, they typically focus on spatial aspects, overlooking the complex spatiotemporal variability characteristic of precipitation, especially during extreme events. …”
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Deep learning algorithm on H&E whole slide images to characterize TP53 alterations frequency and spatial distribution in breast cancer by Chiara Frascarelli, Konstantinos Venetis, Antonio Marra, Eltjona Mane, Mariia Ivanova, Giulia Cursano, Francesca Maria Porta, Alberto Concardi, Arnaud Gerard Michel Ceol, Annarosa Farina, Carmen Criscitiello, Giuseppe Curigliano, Elena Guerini-Rocco, Nicola Fusco

“…DL-based approaches offer significant promise for enhancing biomarker testing and precision oncology by reducing intra- and inter-observer variability, but further validation is required to optimize their integration into real-world clinical workflows. …”
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LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection by Jyoti Parashar, Rituraj Jain, Mahesh K. Singh, Ashwani Kumar, Premananda Sahu, Kamal Upreti

“…CT scans are widely used for lung cancer screening; however, their manual interpretation is time-consuming and prone to variability. This study introduces LungDxNet, a deep learning-based framework that integrates transfer learning to enhance diagnostic accuracy and efficiency. …”
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Privacy–preserving dementia classification from EEG via hybrid–fusion EEGNetv4 and federated learning by Muhammad Umair, Muhammad Shahbaz Khan, Muhammad Hanif, Wad Ghaban, Ibtehal Nafea, Sultan Noman Qasem, Sultan Noman Qasem, Faisal Saeed

“…Electroencephalography (EEG) based diagnosis presents a non-invasive, cost effective alternative for early detection, yet existing methods are challenged by data scarcity, inter-subject variability, and privacy concerns. This study proposes lightweight and privacy-preserving EEG classification framework combining deep learning and Federated Learning (FL). …”
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Ensemble reconstruction of missing satellite data using a denoising diffusion model: application to chlorophyll <i>a</i> concentration in the Black Sea by A. Barth, J. Brajard, A. Alvera-Azcárate, B. Mohamed, C. Troupin, J.-M. Beckers

“…Such methods can naturally provide an ensemble of reconstructions where each member is spatially coherent with the scales of variability and with the available data. Rather than providing a single reconstruction, an ensemble of possible reconstructions can be computed, and the ensemble spread reflects the underlying uncertainty. …”
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A Multi-Domain Feature Fusion CNN for Myocardial Infarction Detection and Localization by Yunfan Chen, Jinxing Ye, Yuting Li, Zhe Luo, Jieqiang Luo, Xiangkui Wan

“…However, relying solely on single-domain features of the electrocardiogram (ECG) proves challenging for accurate MI detection and localization due to the inability of these features to fully capture the complexity and variability in cardiac electrical activity. To address this, we propose a multi-domain feature fusion convolutional neural network (MFF–CNN) that integrates the time domain, frequency domain, and time-frequency domain features of ECG for automatic MI detection and localization. …”
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A Two-Stage Deep Fusion Integration Framework Based on Feature Fusion and Residual Correction for Gold Price Forecasting by Cihai Qiu, Yitian Zhang, Xunrui Qian, Chuhang Wu, Jiacheng Lou, Yang Chen, Yansong Xi, Weijie Zhang, Zhenxi Gong

“…Nonetheless, traditional single prediction models usually suffer from limited predictive performance and fail to capture complex variability of market behavior. Aiming to solve these limitations, an innovative two-stage hybrid deep integration framework that combines feature extraction and residual correction techniques is proposed with a view to predicting the gold price more accurately. …”
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Automated lung cancer detection using novel genetic TPOT feature optimization with deep learning techniques by Mohamed Hammad, Mohammed ElAffendi, Muhammad Asim, Ahmed A. Abd El-Latif, Radwa Hashiesh

“…However, previous deep learning models for lung cancer detection have faced challenges such as limited data, inadequate feature extraction, interpretability issues, and susceptibility to data variability. This paper presents a novel deep learning methodology that addresses these limitations. …”
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Emotion-Aware Ensemble Learning (EAEL): Revolutionizing Mental Health Diagnosis of Corporate Professionals via Intelligent Integration of Multi-Modal Data Sources and Ensemble Tech... by Gaurav Yadav, Mohammad Ubaidullah Bokhari, Saleh I. Alzahrani, Shadab Alam, Mohammed Shuaib

“…Future iterations could enhance the framework by incorporating physiological signals, such as heart rate variability and EEG data, further improving diagnostic accuracy. …”
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Machine Learning Techniques for Predicting Typhoon‐Induced Storm Surge Using a Hybrid Wind Field by Changyu Su, Bishnupriya Sahoo, Miaohua Mao, Meng Xia

“…The prediction performances were analyzed for both spatial (e.g., single and multiple sites) and temporal (e.g., single and multiple steps) scale variability. ML is trained to overcome the residual error of the FVCOM, effectively reducing the inherent uncertainty of traditional methods. …”
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PM2.5 Forecasting at U.S. Embassies and Consulates Worldwide Using NASA Model Powered by Machine Learning by Junhyeon Seo, Alqamah Sayeed, Seohui Park, John Kerekes, Stephanie M. Christel, Mary T. Tran, Pawan Gupta

“…Local models showed improved performance with RMSE of 3.21 μg/m3 and slope of 0.98, outperforming the global model in Air Quality Index predictions by 6.57% in accuracy and greater stability during variability. The forecasts are publicly accessible via an application programming interface, providing global air quality predictions for 269 U.S. embassy and consulate sites to support public health and operational planning.…”
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Time series changes and influencing factors of fractional vegetation coverage under weed competition in wheat field ecosystems through remote sensing by Guofeng Yang, Yong He, Zhenjiang Zhou, Lingzhen Ye, Hui Fang, Xuping Feng

“…European germplasms exhibited the highest maximum FVC, Oceanic germplasms showed high variability, and Asian and American germplasms had intermediate maximum FVC. …”
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A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction by Sangkeum Lee, Mohammad H. Almomani, Saleh Ali Alomari, Kashif Saleem, Aseel Smerat, Vaclav Snasel, Amir H. Gandomi, Laith Abualigah

“…However, the inherent variability and non-linearity of wind power data pose significant challenges. …”
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