State of Charge Estimation in Li-Ion Batteries Using a Parallel LSTM-Based Approach: The Impact of Modeling Based on Operating States by Osman Ozer, Hayri Arabaci

“…Nevertheless, the current-voltage behavior of Li-ion cells varies significantly under different operating conditions, such as charging, discharging, and idle states. This variability negatively impacts the performance of conventional LSTM models. …”
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Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis by Alexander Maniangat Luke, Nader Nabil Fouad Rezallah

“…Significant variability in study results highlights the need for additional research to comprehend the components affecting AI effectiveness. …”
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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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Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed by Rukai Wang, Ximin Yuan, Fuchang Tian, Minghui Liu, Xiujie Wang, Xiaobin Li, Minrui Wu

“…Flash floods are highly nonlinear and exhibit rapid spatiotemporal variability. Existing methods struggle to capture these features, leading to suboptimal long‐term and peak flood prediction accuracy. …”
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Cognitive Electronic Unit for AI-Guided Real-Time Echocardiographic Imaging by Emanuele De Luca, Emanuele Amato, Vincenzo Valente, Marianna La Rocca, Tommaso Maggipinto, Roberto Bellotti, Francesco Dell’Olio

“…Preliminary results indicate that the combined use of CNN-based classification and inertial sensor-based feedback can reduce inter-operator variability and may also enhance diagnostic precision. …”
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Enabling Automated Device Size Selection for Transcatheter Aortic Valve Implantation by Patricio Astudillo, Peter Mortier, Johan Bosmans, Ole De Backer, Peter de Jaegere, Matthieu De Beule, Joni Dambre

“…We propose a method combining two deep convolutional neural networks followed by a postprocessing step. …”
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AI-Based Forecasting in Renewable-Rich Microgrids: Challenges and Comparative Insights by Martins Osifeko, Josiah Lange Munda

“…Amid the accelerating global transition to renewable energy, accurate forecasting has become the cornerstone for unlocking the full potential of solar and wind power in modern power grids, especially in regions with high resource variability. This study begins with a review of forecasting challenges in microgrids located in developing areas where issues related to data sparsity, model limitations, environmental variability, and operational limitations are prevalent. …”
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Liver Semantic Segmentation Method Based on Multi-Channel Feature Extraction and Cross Fusion by Chenghao Zhang, Lingfei Wang, Chunyu Zhang, Yu Zhang, Peng Wang, Jin Li

“…Firstly, a multi-scale input strategy is employed to account for the variability in liver features at different scales. A multi-scale convolutional attention (MSCA) mechanism is integrated into the encoder to aggregate multi-scale information and improve feature representation. …”
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NeoPred: dual-phase CT AI forecasts pathologic response to neoadjuvant chemo-immunotherapy in NSCLC by Rui Wang, Guan Wang, Ying Huang, Yu Jiang, Zhigang Li, Chao Yang, Yuan Zhang, Hengrui Liang, Jianxing He, Zhichao Liu, Hongxu Liu, Jia Zhang, Hong Yu, Guangjian Zhang, Hongshen Deng, Zeping Yan, Wenhai Fu, Jianqi Zheng, Runchen Wang, Houlu Xiao, Zhenlin Chen, Xiaomin Ge, Pingwen Yu, Junke Fu, Bohao Liu, Chudong Wang, Yuechun Lin, Linchong Huang, Fei Cui

“…Three 3-dimensional convolutional neural networks (pre-treatment CT, pre-surgical CT, dual-phase CT) were developed; the best-performing dual-phase model (NeoPred) optionally integrated clinical variables. …”
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Analysis of Nonviscous Oscillators Based on the Damping Model Perturbation by Mario Lázaro, César F. Casanova, Ignacio Ferrer, Pedro Martín

“…After choosing one of them as independent variable, the key idea of the current paper is to obtain a differential equation whose solution can be considered, under certain conditions, a good approximation. …”
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OPTIMAL CONTROL OF INVESTMENTS AROUND COURNOT POINT by Y. Aganin

“…The equations of dynamics of variables for equilibrium, developing and crisis markets in a linear approximation are obtained. …”
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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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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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ScarNet: Development and Validation of a Novel Deep CNN Model for Acne Scar Classification With a New Dataset by Masum Shah Junayed, Md Baharul Islam, Afsana Ahsan Jeny, Arezoo Sadeghzadeh, Topu Biswas, A. F. M. Shahen Shah

“…Dermatologists mainly recognize the type of acne scars manually based on visual inspections, which are time- and energy-consuming and subject to intra- and inter-reader variability. In this paper, a novel automated acne scar classification system is proposed based on a deep Convolutional Neural Network (CNN) model. …”
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TCN-MAML: A TCN-Based Model with Model-Agnostic Meta-Learning for Cross-Subject Human Activity Recognition by Chih-Yang Lin, Chia-Yu Lin, Yu-Tso Liu, Yi-Wei Chen, Hui-Fuang Ng, Timothy K. Shih

“…However, real-world deployment faces two major challenges: (1) significant cross-subject signal variability due to physical and behavioral differences among individuals, and (2) limited labeled data, which restricts model generalization. …”
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Adaptive Outdoor Cleaning Robot with Real-Time Terrain Perception and Fuzzy Control by Raul Fernando Garcia Azcarate, Akhil Jayadeep, Aung Kyaw Zin, James Wei Shung Lee, M. A. Viraj J. Muthugala, Mohan Rajesh Elara

“…Outdoor cleaning robots must operate reliably across diverse and unstructured surfaces, yet many existing systems lack the adaptability to handle terrain variability. This paper proposes a terrain-aware cleaning framework that dynamically adjusts robot behavior based on real-time surface classification and slope estimation. …”
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