Identification of Subtypes of Post-Stroke and Neurotypical Gait Behaviors Using Neural Network Analysis of Gait Cycle Kinematics by Andrian Kuch, Nicolas Schweighofer, James M. Finley, Alison McKenzie, Yuxin Wen, Natalia Sanchez

“…We first trained a Convolutional Neural Network and a Temporal Convolutional Network to extract features that distinguish impaired from neurotypical gait. …”
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Investigating Brain Responses to Transcutaneous Electroacupuncture Stimulation: A Deep Learning Approach by Tahereh Vasei, Harshil Gediya, Maryam Ravan, Anand Santhanakrishnan, David Mayor, Tony Steffert

“…Saliency maps were applied to identify the most critical EEG electrodes, potentially reducing the number needed without sacrificing accuracy. …”
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Optimized Motion Capture for Cricket Shot Classification Using Minimal Hardware and Machine Learning by J. Ishan Randika, Kanishka Rajamanthri, Avishka Kothalawala, Niroshan Gunawardana, Ashan Induranga, Pathum Weerakkody, Kaveendra Maduwantha, B. T. G. S. Kumara, Kaveenga Koswattage

“…These patterns were used to train a hybrid machine learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. …”
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Optimizing Traffic Speed Prediction Using a Multi-Objective Genetic Algorithm-Enhanced RNN for Intelligent Transportation Systems by C. Swetha Priya, F. Sagayaraj Francis

“…Many existing approaches integrate Convolutional Neural Networks (CNNs) and variants of Recurrent Neural Networks (RNNs) to analyze spatially correlated traffic data over time. …”
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A deep learning model for predicting systemic lupus erythematosus-associated epitopes by Jiale He, Zixia Liu, Xiaopo Tang

“…Notably, ablation studies revealed that the CNN component had the most substantial influence on performance, while the custom fusion mechanism yielded better integration of features than conventional strategies. …”
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Deep Learning-Based Detection of Tuberculosis Using a Gaussian Chest X-Ray Image Filter as a Software Lens by Luca Eisentraut, Christopher Mai, Johanna Hosch, Amelie Benecke, Pascal Penava, Ricardo Buettner

“…Tuberculosis remains one of the most prevalent and lethal infectious diseases, with millions of cases reported each year. …”
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PRDAGE: a prescription recommendation framework for traditional Chinese medicine based on data augmentation and multi-graph embedding by Zhihua Wen, Yunchun Dong, Lihong Peng, Longxin Zhang, Junfeng Yan

“…However, the semantic information inherent in both symptoms and herbs has received limited attention. Furthermore, most datasets in the field of TCM suffer from limited data volumes, which can adversely impact model training. …”
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Leveraging data analytics for detection and impact evaluation of fake news and deepfakes in social networks by Tony Mathew Abraham, Tao Wen, Ting Wu, Yu-wang Chen

“…Despite many advantages social media offers, one of the most significant challenges is the rapid rise of fake news and AI-generated deepfakes across these social networks. …”
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Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach. by Hamidreza Moradi, H Timothy Bunnell, Bradley S Price, Maryam Khodaverdi, Michael T Vest, James Z Porterfield, Alfred J Anzalone, Susan L Santangelo, Wesley Kimble, Jeremy Harper, William B Hillegass, Sally L Hodder, National COVID Cohort Collaborative (N3C) Consortium

“…<h4>Methods</h4>Gradient Boosted Decision Tree, Deep and Convolutional Neural Network classifiers were implemented and trained on the National COVID Cohort Collaborative (N3C) data repository to predict the patients' outcome of death or discharge. …”
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Technical study on the efficiency and models of weed control methods using unmanned ground vehicles: A review by Evans K. Wiafe, Kelvin Betitame, Billy G. Ram, Xin Sun

“…Finally, trials of most UGVs have limited documentation or lack extensive trials under various conditions, such as varying soil types, crop fields, topography, field geometry, and annual weather conditions. …”
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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

“…After all, it was VGG16 and ResNet50 which stood out as the most effective models, even though their success is highly dependent on the variety of the data and the quality of the images.…”
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Cytopathological quantification of NORs using artificial intelligence to oral cancer screening by Tatiana Wannmacher LEPPER, Luara Nascimento do AMARAL, Ana Laura Ferrares ESPINOSA, Igor Cavalcante GUEDES, Maikel Maciel RÖNNAU, Natália Batista DAROIT, Alex Nogueira HAAS, Fernanda VISIOLI, Manuel Menezes de OLIVEIRA NETO, Pantelis Varvaki RADOS

“…Abstract Oral squamous cell carcinoma (OSCC) remains the most prevalent neoplasm of the head and neck. In recent decades, the incidence and prevalence of OSCC have not significantly changed, highlighting the critical need to develop and implement new risk assessment measures. …”
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Preoperative prediction of pulmonary ground-glass nodule infiltration status by CT-based radiomics combined with neural networks by Kun Mei, Zikang Feng, Hui Liu, Min Wang, Chao Ce, Shi Yin, Xiaoying Zhang, Bin Wang

“…Feature selection was performed using the Lasso algorithm to identify the most predictive variables, which were subsequently incorporated into the radiomics-based neural network model. …”
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Vision Transformer-Based Unhealthy Tree Crown Detection in Mixed Northeastern US Forests and Evaluation of Annotation Uncertainty by Durga Joshi, Chandi Witharana

“…Additionally, we investigated the impact of different spectral band combinations on model performance to identify the most effective configuration without incurring additional data acquisition costs. …”
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TransformerPayne: Enhancing Spectral Emulation Accuracy and Data Efficiency by Capturing Long-range Correlations by Tomasz Różański, Yuan-Sen Ting, Maja Jabłońska

“…The newly introduced TransformerPayne emulator outperformed all other tested models, achieving a mean absolute error (MAE) of approximately 0.15% when trained on the full grid. The most significant improvements were observed in grids containing between 1000 and 10,000 spectra, with TransformerPayne showing 2–5 times better performance than the scaled-up version of The Payne. …”
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An Attention‐Based Explainable Deep Learning Approach to Spatially Distributed Hydrologic Modeling of a Snow Dominated Mountainous Karst Watershed by Qianqiu Longyang, Seohye Choi, Hyrum Tennant, Devon Hill, Nathaniel Ashmead, Bethany T. Neilson, Dennis L. Newell, James P. McNamara, Tianfang Xu

“…Analysis of attention weights in the trained model unveiled distinct areas contributing the most to discharge under snowmelt and recession conditions. …”
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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

“…Estimating a child&#x2019;s age from ocular biometric images is challenging due to subtle physiological changes and the limited availability of longitudinal datasets. Although most biometric age estimation studies have focused on facial features and adult subjects, pediatric-specific analysis, particularly of the iris and periocular regions, remains relatively unexplored. …”
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PBC-Transformer: Interpreting Poultry Behavior Classification Using Image Caption Generation Techniques by Jun Li, Bing Yang, Jiaxin Liu, Felix Kwame Amevor, Yating Guo, Yuheng Zhou, Qinwen Deng, Xiaoling Zhao

“…Accurate classification of poultry behavior is critical for assessing welfare and health, yet most existing methods predict behavior categories without providing explanations for the image content. …”
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MultiRepPI: a cross-modal feature fusion-based multiple characterization framework for plant peptide-protein interaction prediction by Yu Zhiguo, Li Zixuan, Li Peng

“…However, existing computational methods still face several challenges in PepPI prediction. First, most methods fail to adequately integrate multimodal information such as sequence, structure, and disorder properties, leading to inadequate characterization of complex interaction patterns. …”
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Early Prediction of Sepsis in the Intensive Care Unit Using the GRU-D-MGP-TCN Model by Seunghee Lee, Geonchul Shin, Jeongseok Hwang, Yunjeong Hwang, Hyunwoo Jang, Ju Han Park, Sunmi Han, Kyeongmin Ryu, Jong-Yeup Kim

“…Sepsis is a life-threatening condition with significant risk to individuals, most prevalent in intensive care units (ICUs). Early diagnosis and prompt treatment are crucial to reducing sepsis-related mortality. …”
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