Predicting postoperative nausea and vomiting using machine learning: a model development and validation study by Maxim Glebov, Teddy Lazebnik, Maksim Katsin, Boris Orkin, Haim Berkenstadt, Svetlana Bunimovich-Mendrazitsky

“…An ensemble model of machine-learning algorithms trained on the data of 35,003 patients was developed. …”
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A redundant weight removal method inspired by specific immunotherapy for boosting transfer learning security by Qing Huang, Hongli Deng, Junxiang Wang, Tao Yang, Bochuan Zheng

“…Abstract Redundant weights bring security risks to transfer learning, how to remove redundant weights effectively is an important research topic in transfer learning. Currently, redundant weights are removed mainly through pruning algorithms designed by correlating student data with teacher model nodes. …”
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Preliminary Study on Real-Time Phonocardiogram Signal Acquisition and Analysis Using Machine Learning and IoMT for Digital Stethoscope by M. Kalimuthu, C. Hemanth

“…Cardiovascular diseases currently pose the greatest threat to human health and future predicament is uncertain. …”
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Patient perspectives on the use of digital medical devices and health data for AI-driven personalised medicine in Parkinson’s Disease by Ivana Paccoud, Ivana Paccoud, Mayca Marín Valero, Laura Carrasco Marín, Noémi Bontridder, Alzhraa Ibrahim, Alzhraa Ibrahim, Jüergen Winkler, Jüergen Winkler, Messaline Fomo, Messaline Fomo, Stefano Sapienza, Stefano Sapienza, Fouad Khoury, Jean-Christophe Corvol, Holger Fröhlich, Holger Fröhlich, Jochen Klucken, Jochen Klucken, Jochen Klucken

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Activated TBK1 promotes ACSL1-mediated microglia lipid droplet accumulation and neuroinflammation in Parkinson’s disease by Chunlei Han, Huizhi Wang, Moxuan Zhang, Liuliu Wu, Anni Wang, Pengda Yang, Yuan Gao, Chaonan Zhang, Jianing Gao, Tao Xue, Tingting Du, Yucheng Ji, Lin Shi, Chunkui Zhang, Renpeng Li, Yilei Xiao, Jianguo Zhang, Fangang Meng

“…Dysregulation of lipid droplet homeostasis is a significant factor affecting microglial inflammatory responses, but the mechanisms underlying lipid droplet imbalance in PD are currently unclear. Here, we report a subtype of microglia characterized by high expression of long-chain acyl-CoA synthetase 1 (ACSL1) through single-nucleus RNA sequencing analysis and machine learning algorithms, linking lipid metabolism to PD neuroinflammation. …”
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Environmental thresholds triggering changes in above and belowground biomass carbon in China by Xin Zhang, Shihang Zhang, Jun Zhou, Jianrong Fan

“…Using machine learning algorithms, we modeled AGBC and BGBC distributions for the years 2010 and 2100 (SSP5–8.5 scenario) and identified regions with the most significant expected changes. …”
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Improving the explainability of CNN-LSTM-based flood prediction with integrating SHAP technique by Hao Huang, Zhaoli Wang, Yaoxing Liao, Weizhi Gao, Chengguang Lai, Xushu Wu, Zhaoyang Zeng

“…Convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) are popular deep learning architectures currently used for rapid flood simulations. However, deep learning algorithms are difficult to explain, like a “black box” that lacks insight. …”
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Rapid Automated Mapping of Clouds on Titan With Instance Segmentation by Zachary Yahn, Douglas M. Trent, Ethan Duncan, Benoît Seignovert, John Santerre, Conor A. Nixon

“…We compare the efficiencies of human‐driven versus algorithmic approaches, showing that transfer learning provides speed‐ups that may open new horizons for data investigation for Titan. …”
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A Predictive Model for Secondary Posttonsillectomy Hemorrhage in Pediatric Patients: An 8‐Year Retrospective Study by Yuting Ge, Wenchuan Chang, Lixiao Xie, Yan Gao, Yue Xu, Huie Zhu

“…Early identification and prediction of PTH are of great significance. Currently, there are very few tools available for clinicians to accurately assess the risk of PTH. …”
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Research on Reservoir Identification of Gas Hydrates with Well Logging Data Based on Machine Learning in Marine Areas: A Case Study from IODP Expedition 311 by Xudong Hu, Wangfeng Leng, Kun Xiao, Guo Song, Yiming Wei, Changchun Zou

“…The internal relationship between logging data and hydrate reservoir is analyzed through six ML algorithms. The results show that the constructed ML model performs well in gas hydrate reservoir identification. …”
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Hybrid Active and Passive Cable Contour Shielding of Magnetic Fields of Double-Circuit Overhead Power Lines by Kuznetsov B.I., Kutsenko A.S., Nikitina T.B.

“…The solution to the minimax vector optimization problem is calculated based on multi-particle swarm optimization algorithms from Pareto-optimal solutions taking into account binary preference relations. …”
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Low-Scalability Distributed Systems for Artificial Intelligence: A Comparative Study of Distributed Deep Learning Frameworks for Image Classification by Manuel Rivera-Escobedo, Manuel de Jesús López-Martínez, Luis Octavio Solis-Sánchez, Héctor Alonso Guerrero-Osuna, Sodel Vázquez-Reyes, Daniel Acosta-Escareño, Carlos A. Olvera-Olvera

“…Currently, cloud services offer products for running distributed data training, such as NVIDIA Deep Learning Solutions, Amazon SageMaker, Microsoft Azure, and Google Cloud AI Platform. …”
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RGB-T Object Detection With Failure Scenarios by Qingwang Wang, Yuxuan Sun, Yongke Chi, Tao Shen

“…Currently, RGB-thermal (RGB-T) object detection algorithms have demonstrated excellent performance, but issues such as modality failure caused by fog, strong light, sensor damage, and other conditions can significantly impact the detector's performance. …”
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Chlormethine Gel for Treatment of Patients with Mycosis Fungoides: Best Practices and Guidance to Clinicians by Larisa Geskin, Christiane Querfeld, Emmilia Hodak, Neda Nikbakht, Evangelia Papadavid, Marco Ardigò, Ulrike Wehkamp, Martine Bagot

“…While multiple guidelines provide treatment recommendations, there are currently no clear treatment algorithms for MF. Chlormethine gel is recommended by major treatment guidelines as a first-line option for stage IA–IIA disease, and, on the basis of these guidelines, used in combination with other therapies in patients with advanced-stage MF in clinical practice. …”
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Recent Advances in AlN-Based Acoustic Wave Resonators by Hao Lu, Xiaorun Hao, Ling Yang, Bin Hou, Meng Zhang, Mei Wu, Jie Dong, Xiaohua Ma

“…Additionally, optimization algorithms for designing AlN-based BAW filters are outlined to reduce design time and improve efficiency. …”
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DISPEL: A Python Framework for Developing Measures From Digital Health Technologies by A. Scotland, G. Cosne, A. Juraver, A. Karatsidis, J. Penalver-Andres, E. Bartholome, C. M. Kanzler, C. Mazza, D. Roggen, C. Hinchliffe, S. Del Din, S. Belachew

“…While the framework has already been refined based on clinical trials’ data, ad-hoc validation of the provided algorithms in their specific context of use is recommended to the users.…”
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A Comprehensive Review of the Main Heavyweight and Lightweight SDLC for Big Data Analytics Systems (BDAS) by David Alejandro Montoya-Murillo, Sergio Galvan-Cruz, Manuel Mora, Estela Lizbeth Munoz Andrade

“…Big Data Analytics Systems (BDAS) are software systems developed with descriptive, predictive, or prescriptive decision-making purposes, and currently are implemented in diverse domains – i.e. …”
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Acoustic-based machine learning approaches for depression detection in Chinese university students by Yange Wei, Yange Wei, Shisen Qin, Fengyi Liu, Rongxun Liu, Yunze Zhou, Yuanle Chen, Xingliang Xiong, Wei Zheng, Guangjun Ji, Yong Meng, Fei Wang, Fei Wang, Ruiling Zhang

“…BackgroundDepression is major global public health problems among university students. Currently, the evaluation and monitoring of depression predominantly depend on subjective and self-reported methods. …”
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Cardiovascular Diseases and Non-Alcoholic Fatty Liver Disease: Relationship and Pathogenetic Aspects of Pharmacotherapy by A. V. Nelidova, M. A. Livzan, N. A. Nikolaev, T. S. Krolevets

“…The association of non-alcoholic fatty liver disease (NAFLD) and cardiovascular risk is currently one of the actively studied areas. The incidence of non-alcoholic fatty  liver disease continues to grow worldwide. …”
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Fostemsavir resistance in clinical context: a narrative review by Jonathan M. Schapiro, Rolf Kaiser, Mark Krystal, Chris M. Parry, Allan R. Tenorio, Eugene Stewart, Bruce Gilliam, Margaret Gartland, Andrew Clark, Jose R. Castillo-Mancilla

“…Due to these factors and limited phenotypic clinical data, thus far, no relevant phenotypic cutoff or genotypic algorithms have been derived that reliably predict response to fostemsavir-based therapy in individuals who are HTE; therefore, pre-treatment temsavir resistance testing may be of limited benefit. …”
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