Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones by Aske G. Larsen, Line Ø. Sadolin, Trine R. Thomsen, Anderson S. Oliveira

“…Stride time predictions were highly accurate (<5% error), while stance and swing times exhibited moderate variability and double support time showed the highest errors (>20%). …”
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Application of Gated Recurrent Unit in Electroencephalogram (EEG)-Based Mental State Classification by Gst. Ayu Vida Mastrika Giri, Ngurah Agus Sanjaya ER, I Ketut Gede Suhartana

“…Due to high signal variability and sensitivity to noise, correct classification is still tricky, even with advances in the analysis of EEG signals. …”
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Hand Gesture Recognition in Indian Sign Language Using Deep Learning by Harsh Kumar Vashisth, Tuhin Tarafder, Rehan Aziz, Mamta Arora, Alpana

“…This is achieved by using and implementing Convolutional Neural Networks on our self-made dataset. …”
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Unsupervised Deep Clustering on Spatiotemporal Objects Extracted from 4D Point Clouds for Automatic Identification of Topographic Processes in Natural Environments by J. Wang, K. Anders

“…By leveraging the representation learning capability of autoencoders, especially using convolutional neural networks (CNNs) as feature extractors, our approach implements the dimensionality reduction of the original inputs to uniform low-dimensional vectors in latent space. …”
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HEE-SegGAN: A holistically-nested edge enhanced GAN for pulmonary nodule segmentation. by Yong Wang, Seri Mastura Mustaza, Mohammad Syuhaimi Ab-Rahman, Siti Salasiah Mokri

“…However, this task remains challenging due to the complex morphological variability of pulmonary nodules in CT images and the limited availability of well-annotated datasets. …”
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Leveraging artificial intelligence for diagnosis of children autism through facial expressions by Mahmood A. Mahmood, Leila Jamel, Nazik Alturki, Medhat A. Tawfeek

“…Future research needs to include multiple data types as well as extend dataset variability while optimizing hybrid architecture systems to elevate diagnostic forecasting. …”
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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

“…However, in cases where the input data exhibit limited variation over time and consist of low-dimensional features, deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) may tend toward overfitting. …”
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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

“…We employed linear permutations in the latent and conditional spaces to control signal features, and a convolutional network to classify lung volume states from real and synthetic data separately. …”
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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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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

“…Here, we address this challenge by developing a cognitive electronic unit that integrates convolutional neural network (CNN) models and an inertial sensor for assisted echocardiography. …”
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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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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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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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