RAT-CC: A Recurrent Autoencoder for Time-Series Compression and Classification by Giacomo Chiarot, Sebastiano Vascon, Claudio Silvestri, Idoia Ochoa

“…This combined loss ensures that the learned embeddings remain meaningful for classification tasks while preserving the necessary information for reconstruction. …”
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Introducing a Deep Neural Network Model with Practical Implementation for Polyp Detection in Colonoscopy Videos by Hajar Keshavarz, Zohreh Ansari, Hossein Abootalebian, Babak Sabet, Mohammadreza Momenzadeh

“…Background: Deep learning has gained much attention in computer-assisted minimally invasive surgery in recent years. …”
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Few-Shot Object Detection via Sample Processing by Honghui Xu, Xinqing Wang, Faming Shao, Baoguo Duan, Peng Zhang

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Impact of different renal artery clamping strategies on postoperative renal function in patients with pre-existing renal insufficiency in robotic partial nephrectomy by LI Linfei, WANG Cong, WEI Ling

“…Objective‍　‍To compare the effects of main artery clamping (MAC) and selective artery clamping (SAC) strategies on postoperative renal function in patients with chronic renal insufficiency undergoing robot-assisted partial nephrectomy. …”
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Preliminary results of the EPIDIA4Kids study on brain function in children: multidimensional ADHD-related symptomatology screening using multimodality biometry by Yanice Guigou, Alexandre Hennequin, Théo Marchand, Théo Marchand, Mouna Chebli, Lucie Isoline Pisella, Pascal Staccini, Pascal Staccini, Vanessa Douet Vannucci, Vanessa Douet Vannucci

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Alterations of gray matter volume and functional connectivity in patients with cognitive impairment induced by occupational aluminum exposure: a case-control study by Huaxing Meng, Bo Liu, Xiaoting Lu, Yan Tan, Shanshan Wang, Baolong Pan, Hui Zhang, Qiao Niu

“…Plasma aluminum levels were measured using inductively coupled plasma-mass spectrometry. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) and an auditory-verbal learning test (AVLT). …”
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Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivity by Xiaochen Tang, Yanyan Wei, Jiaoyan Pang, Lihua Xu, Huiru Cui, Xu Liu, Yegang Hu, Mingliang Ju, Yingying Tang, Bin Long, Wei Liu, Min Su, Tianhong Zhang, Jijun Wang

“…Functional connectivity (FC) features that were correlated with symptom severity were subjected to the single-cell interpretation through multikernel learning (SIMLR) algorithm in order to identify latent homogeneous subgroups. …”
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Improving computer vision for plant pathology through advanced training techniques by Jamie R. Sykes, Katherine J. Denby, Daniel W. Franks

“…Discussion This research underscores the potential of semi‐supervised learning and advanced loss functions in enhancing the applicability of deep learning models in agricultural disease management. …”
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ASAP: Automated Style-Aware Similarity Measurement for Selection of Annotated Pre-Training Datasets in 2D Biomedical Imaging by Miguel Molina-Moreno, Marcel P. Schilling, Markus Reischl, Ralf Mikut

“…In this paper, we propose an automated style-aware framework for predicting the similarity value of a new biomedical dataset with respect to the state-of-the-art annotated datasets, selecting the most suitable annotated dataset for transfer learning or domain adaptation. Our pipeline, consisting of an autoencoder trained with self-supervised learning through a comprehensive loss function that considers the image reconstruction, style features, and dataset membership, does not need any kind of labels in training and test stages. …”
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Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate by Tiantian Luan, Mingxiao Sun, Guoqing Xia, Daidai Chen

“…This paper proposes a new variable structure radial basis function (VS-RBF) with a fast learning rate, in order to solve the problem of structural optimization design and parameter learning algorithm for the radial basis function neural network. …”
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Enhancing Secure Energy Efficiency of SWIPT IoT Network Considering IRS and Artificial-Noise: A Deep Learning Approach by Kimchheang Chhea, Jung-Ryun Lee

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Machine learning-based prognostic model of lactylation-related genes for predicting prognosis and immune infiltration in patients with lung adenocarcinoma by Mingjun Gao, Mengmeng Wang, Siding Zhou, Jiaqi Hou, Wenbo He, Yusheng Shu, Xiaolin Wang

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Mapping changes of grassland to arable land using automatic machine learning of stacked ensembles and H2O library by Jiří Šandera, Přemysl Štych

“…The aim of this study was to evaluate the potential of H2O library and within implemented Automachine learning function (AutoML) and its stacked ensembles for mapping changes from grasslands to arable lands. …”
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Adaptive Generation Method for Small Volume Easy Fabrication Freeform Unobscured Three-Mirror Systems Based on Machine Learning by Yiwei Sun, Yangjie Wei, Ji Zhao

“…First, an error function based on volume, fabrication, and imaging quality functions is constructed, and a dataset is generated using this error function. …”
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Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI by Dayeong An, El-Sayed Ibrahim

“…Cardiac MRI was performed 8 and 10 weeks post-treatment to assess global and regional cardiac function. ML algorithms were applied to differentiate sham-treated and irradiated rats based on early changes in myocardial function. …”
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Evaluating Cardiac Impairment From Abnormal Respiratory Patterns: Insights From a Wireless Radar and Deep Learning Study by Chun-Chih Chiu, Wen-Te Liu, Jiunn-Horng Kang, Chun-Chao Chen, Yu-Hsuan Ho, Yu-Wen Huang, Zong-Lin Tsai, Rachel Chien, Ying-Ying Chen, Yen-Ling Chen, Nai-Wen Chang, Hung-Wen Lu, Kang-Yun Lee, Arnab Majumdar, Shu-Han Liao, Ju-Chi Liu, Cheng-Yu Tsai

“…Conclusions: This study demonstrates that a wireless radar framework combined with deep learning can effectively monitor respiratory patterns that are associated with cardiac function. …”
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PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context by Guojie Zhong, Yige Zhao, Demi Zhuang, Wendy K. Chung, Yufeng Shen

“…Pathogenic missense variants in the same gene may act through different modes of action (i.e., gain/loss-of-function) by affecting different aspects of protein function. …”
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A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI. by Elizabeth M Sweeney, Joshua T Vogelstein, Jennifer L Cuzzocreo, Peter A Calabresi, Daniel S Reich, Ciprian M Crainiceanu, Russell T Shinohara

“…We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. …”
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Hindcasting Maximum Water Depths in Coastal Watersheds: The Importance of Incorporating Off‐Channel Data and Their Uncertainties in Machine Learning Models by Maryam Pakdehi, Ebrahim Ahmadisharaf

“…The model was developed under three scenarios, which differed in terms of the flood observational data (stream gauges and HWMs) used for their training and validation. A loss function was proposed to incorporate the uncertainty of observations. …”
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Aerodynamic optimization of a coaxial rotor system using a deep learning-based multi-fidelity surrogate model by Shu-Hui Qin, Ai-Ming Yang

“…The DBN model provides reliable predictions and supplementary low-fidelity samples capturing global function trend. A Multilevel Hierarchical Kriging (MHK) model is built to conduct a deep learning-based multi-fidelity optimization, combining existing low- and medium-fidelity samples with additional high-fidelity data to achieve the optimum. …”
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