Cascaded Machine Learning of Soil Moisture and Salinity Prediction in Estuarine Wetlands Based on In Situ Internet of Things Monitoring by Jie Song, Yujun Yi

“…To address these challenges and improve our ability to predict and manage wetland soil properties, this study employs an in situ Internet of Things (IoT)‐based monitoring network and a interpretable, cascaded machine learning model to predict these critical soil parameters. …”
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Machine learning-based integration develops a metabolism-derived consensus model for improving immunotherapy in pancreatic cancer by Yang Song, Cheng Yang, Jie Min, Lei Hua, Haichuan Su, Ting Zhao, Yongdong Guo, Ronglin Wang, Jingjie Shi, Peixiang Ma, Junqiang Li

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Radiomics-Based Machine Learning for Determining Amplification Status in Childhood Neuroblastoma: A Systematic Review and Meta-Analysis by Haoru Wang MD, Yi Ji MD, Xin Chen MD, Ling He MD, Xiangming Fang MD, Jinhua Cai MD

“…This systematic review and meta-analysis aimed to quantitatively evaluate the diagnostic accuracy of radiomics-based machine learning models for determining MYCN amplification in neuroblastoma and to critically assess the methodological quality of the included studies. …”
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Dempster Shafer-Empowered Machine Learning-Based Scheme for Reducing Fire Risks in IoT-Enabled Industrial Environments by Jayameena Desikan, Sushil Kumar Singh, A. Jayanthiladevi, Saurabh Singh, Byungun Yoon

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Contrast-enhanced CT-based deep learning model assists in preoperative risk classification of thymic epithelial tumors by Xuhui Zhao, Lingyu Zhang, Li Liang, Qi Zhang, Wencan Wang, Junlin Li, Hua Zhang, Chunhai Yu, Lingjie Wang

“…BackgroundThis study aimed to develop and evaluate a deep learning (DL) model utilizing contrast-enhanced computed tomography (CT) to assist radiologists in accurately stratifying the risk of thymic epithelial tumors (TETs) based on the World Health Organization (WHO) classification.MethodsInvolved retrospectively enrolling clinical data from 266 patients with histopathologically confirmed TETs from two centers: Center 1 (training set, n=205) and Center 2 (external testing set, n=61). …”
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Deep learning-based prediction of nodal metastasis in lung cancer using endobronchial ultrasoundCentral MessagePerspective by Tsukasa Ishiwata, MD, PhD, Terunaga Inage, MD, PhD, Masato Aragaki, MD, PhD, Alexander Gregor, MD, PhD, Zhenchian Chen, MD, Nicholas Bernards, PhD, Kamran Kafi, MD, Kazuhiro Yasufuku, MD, PhD

“…Image frames were randomly selected and split into training and validation datasets on a per-patient basis. A deep learning model with convolutional neural networks, SqueezeNet, was used for image classification via transfer learning based on pretraining from ImageNet. …”
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The relationship between activities of daily living and speech impediments based on evidence from statistical and machine learning analyses by Liu Jun, Hongguo Li, Yu Mao, Lan Hu, Dan Wu

“…While the role of ADL as a potential biomarker for SI remains unclear, this study aims to provide new evidence supporting ADL as an early predictor of SI through statistical analysis and machine learning validation.MethodsData were derived from the 2018 CHARLS national baseline survey, comprising 10,136 participants aged 45 and above. …”
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Machine learning-based radiomics for differentiating lung cancer subtypes in brain metastases using CE-T1WI by Xueming Xia, Wei Du, Qiheng Gou

“…ObjectivesThe purpose of this research was to create and validate radiomic models based on machine learning that can effectively discriminate between primary non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) in individuals with brain metastases (BMs) by utilizing high-dimensional radiomic characteristics derived from contrast-enhanced T1-weighted imaging (CE-T1WI).MethodsA cohort of 260 individuals were chosen as participants. …”
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Deep learning-based automated segmentation for the quantitative diagnosis of cerebral small vessel disease via multisequence MRI by Huiyu Zhao, Miaoyi Zhang, Weijun Tang, Luyuan Jin, Jie Tang, Langfeng Shi, Xiao Deng, Jianhui Fu, Weiwen Zou

“…We aimed to develop an automated segmentation method based on deep learning (DL) to quantify the typical neuroimaging markers of CSVD on multisequence magnetic resonance imaging (MRI).Materials and methodsMRI scans from internal (July 2018 to July 2022) and external (November 2012 to January 2015) datasets were analyzed. …”
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Machine learning prediction of HER2-low expression in breast cancers based on hematoxylin–eosin-stained slides by Jun Du, Jun Shi, Dongdong Sun, Yifei Wang, Guanfeng Liu, Jingru Chen, Wei Wang, Wenchao Zhou, Yushan Zheng, Haibo Wu

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Predicting mucosal healing in Crohn’s disease: development of a deep-learning model based on intestinal ultrasound images by Li Ma, Yuepeng Chen, Xiangling Fu, Jing Qin, Yanwen Luo, Yuanjing Gao, Wenbo Li, Mengsu Xiao, Zheng Cao, Jialin Shi, Qingli Zhu, Chenyi Guo, Ji Wu

“…This study aimed to develop a deep learning model based on baseline intestinal ultrasound (IUS) images and clinical information to predict mucosal healing. …”
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Study on Electric Power Fittings Identification Method for Snake Inspection Robot Based on Non-Contact Inductive Coils by Zhiyong Yang, Jianguo Liu, Shengze Yang, Changjin Zhang

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Robust Tracking Method for Small and Weak Multiple Targets Under Dynamic Interference Based on Q-IMM-MHT by Ziqian Yang, Hongbin Nie, Yuxuan Liu, Chunjiang Bian

“…To address these issues, this paper presents an improved multi-target tracking algorithm, termed Q-IMM-MHT. This method integrates Multiple Hypothesis Tracking (MHT) with Interactive Multiple Model (IMM) and introduces a Q-learning-based adaptive model switching strategy to dynamically adjust model selection in response to variations in the target’s motion patterns. …”
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The Impact of Camtasia-Based Learning Videos on Vocabulary Mastery in Eighth-Grade MTsN 1 Padang Pariaman by Yulva Maya Padilah, Amin Harahap, Haerani Kadar, Raudhatunnur

“…These results indicate a substantial positive effect of Camtasia-based learning videos on students' vocabulary mastery. …”
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Quantitative evaluation of grooves in fuel ice layers of ICF based on deep learning and x-ray phase retrieval by Kaijun Shi, Kai Wang, Xin Wang, Ji Yan, Baolu Yang, Cheng Yang, Mingtao Li, Mingxun Wang, Jie Xu, Fei Dai, Xing Zhang, Zhanshan Wang, Baozhong Mu

“…However, effective quantitative evaluation for grooves based on in-situ XPC imaging remains limited. This study presents a quantitative evaluation method for grooves based on deep learning and phase retrieval. …”
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Machine Learning-Based Prediction of Resilience in Green Agricultural Supply Chains: Influencing Factors Analysis and Model Construction by Daqing Wu, Tianhao Li, Hangqi Cai, Shousong Cai

“…Secondly, by integrating configurational analysis with machine learning, it innovatively constructs a resilience level prediction model based on fsQCA-XGBoost. …”
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Improving the functionality of wireless sensor networks through the use of reinforcement learning and metaheuristic based energy efficient system by Shiwei Zhang, Xinghan Liu

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Development of Environment-Based Digital Flipbook Teaching Materials to Enhance Learning Independence among Elementary School Students by Elsa Chaeratunnisa, Enggar Utari, Iing Dwi Lestari

“…This study aims to develop environment-based digital flipbook teaching materials to enhance elementary school students' learning independence, particularly on the topic of food chains. …”
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Development of Motion and Force Learning E-Modules Based on PjBL-STEM-Jigsaw to Improve Student Creativity by Ayu Ariyani, Irvan Permana, Anna Permanasari

“…<p>This research aims to develop a PjBL-STEM-based learning e-module to improve student creativity in motion and force material. …”
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Deep learning-based screening for locomotive syndrome using single-camera walking video: Development and validation study. by Junichi Kushioka, Satoru Tada, Noriko Takemura, Taku Fujimoto, Hajime Nagahara, Masahiko Onoe, Keiko Yamada, Rodrigo Navarro-Ramirez, Takenori Oda, Hideki Mochizuki, Ken Nakata, Seiji Okada, Yu Moriguchi

“…To address this, we introduced a Deep Learning (DL)-based computer vision model that employs OpenPose for pose estimation and MS-G3D for spatial-temporal graph analysis. …”
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