CLCluster: A redundancy-reduction contrastive learning-based clustering method of cancer subtype based on multi-omics data by Hong Wang, Yi Zhang, Wen Li, Zhen Wei, Zhenlong Wang, Mengyuan Yang

“…Here, we propose a redundancy-reduction contrastive learning-based method (CLCluster) based on copy number variation, methylation, gene expression, miRNA expression, and AS for cancer subtype clustering of 33 cancer types. …”
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Impact of flipped classroom method in physical education on the intrinsic motivation, self-efficacy, and learning satisfaction: A meta-analysis. by Qingxu Wu, Zhenxiao Han, Zikang Hao, Jiping Chen, Yang Pan

“…<h4>Background</h4>Flipped classroom (FC) is a novel teaching method. To assess its effectiveness in teaching physical education in schools, a systematic review and meta-analysis were conducted with the aim of exploring the Impact of FC method in PE on the intrinsic motivation, self-efficacy, and learning satisfaction.…”
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Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine by Zhijian Wang, Likang Zheng, Junyuan Wang, Wenhua Du

“…In this paper, a novel bearing intelligent fault diagnosis method based on a novel krill herd algorithm (NKH) and kernel extreme learning machine (KELM) is proposed. …”
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Urban Tree Canopy Mapping and Analysis Using Iterative Annotation Method and Deep Learning: A Case Study in Beijing by Yulong Ding, Ximin Cui, Zhengchao Chen, Zeqing Wang, Debao Yuan, Xiang Meng, Xuan Yang, Yue Xu, Xiangyu Tian

“…Urban trees have significant ecological and social functions, and generating urban tree canopy (UTC) maps is an effective method for understanding their distribution. However, existing studies primarily rely on medium-resolution to low-resolution imagery for large-scale extraction or high-resolution imagery for small-scale extraction, making it challenging to balance spatial coverage and accuracy. …”
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Machine Learning Method Application to Detect Predisposing Factors to Open-Pit Landslides: The Sijiaying Iron Mine Case Study by Jiang Li, Zhuoying Tan, Naigen Tan, Aboubakar Siddique, Jianshu Liu, Fenglin Wang, Wantao Li

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Deep Learning‐Based Prediction of Global Ionospheric TEC During Storm Periods: Mixed CNN‐BiLSTM Method by Xiaochen Ren, Biqiang Zhao, Zhipeng Ren, Yan Wang, Bo Xiong

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Machine Learning Method (Decision Tree) to Predict the Physicochemical Properties of Premium Lebanese Kishk Based on Its Hedonic Properties by Ossama Dimassi, Youmna Iskandarani, Houssam Shaib, Lina Jaber, Shady Hamadeh

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Autonomous Robot Goal Seeking and Collision Avoidance in the Physical World: An Automated Learning and Evaluation Framework Based on the PPO Method by Wen-Chung Cheng, Zhen Ni, Xiangnan Zhong, Minghan Wei

“…However, application of RL methods to real-world tasks such as mobile robot navigation, and evaluating their performance under various training–testing settings has not been sufficiently researched. …”
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A partially heterogeneous weighted fusion learning method for potential heterogeneous treatment effect in multi-site survival study by Chen Huang, Kecheng Wei, Yongfu Yu, Guoyou Qin

“…Addressing both the potential heterogeneity of site-specific treatment effects and the issues of model misspecification in the context of multi-site survival data is a critical area that warrants further research. Methods We propose a novel partially heterogeneous weighted fusion learning method. …”
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An Ensemble Learning Method for the Kernel-Based Nonlinear Multivariate Grey Model and its Application in Forecasting Greenhouse Gas Emissions by Lan Wang, Nan Li, Ming Xie

“…In order to give policy makers more power to set the specific target of GHG emission reduction, we propose an ensemble learning method with the least squares boosting (LSBoost) algorithm for the kernel-based nonlinear multivariate grey model (KGM) (1, N), and it is abbreviated as BKGM (1, N). …”
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A Construction and Representation Learning Method for a Traffic Accident Knowledge Graph Based on the Enhanced TransD Model by Xiaojia Liu, Haopeng Wu, Dexin Yu, Yunjie Chen, Hao Wu

“…With rapid urbanization and surging traffic volumes, traffic accident data have become high-dimensional, multi-source, heterogeneous, and spatiotemporally dynamic, posing challenges for traditional statistical methods and machine learning models to simultaneously account for data heterogeneity and nonlinear interactions. …”
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An Improved Machine Learning-Based Method for Unsupervised Characterisation for Coral Reef Monitoring in Earth Observation Time-Series Data by Zayad AlZayer, Philippa Mason, Robert Platt, Cédric M. John

“…This study presents an innovative approach to automated coral reef monitoring using satellite imagery, addressing challenges in image quality assessment and correction. The method employs Principal Component Analysis (PCA) coupled with clustering for efficient image selection and quality evaluation, followed by a machine learning-based cloud removal technique using an XGBoost model trained to detect land and cloudy pixels over water. …”
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A novel trajectory learning method for robotic arms based on Gaussian Mixture Model and k-value selection algorithm. by Jingnan Yan, Yue Wu, Kexin Ji, Cheng Cheng, Yili Zheng

“…To address this challenge, this paper proposes a novel trajectory learning method for robotic arms that combines Gaussian Mixture Model with a k-value selection algorithm. …”
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Implementing the Project-Based Learning Method in a Social Work Classroom: Mental Health Awareness – A Case Study by Mirela M. Doga

“…This article presents a classroom experience with the application of the Project-Based Learning (PBL) method in the Social Work specialization during the second year of study. …”
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Large-area urban TomoSAR method with limited a priori knowledge and a complex deep learning model by Haoxuan Duan, Yuzhou Liu, Hong Zhang, Peifeng Ma, Zhongqi Shi, Zihuan Guo, Yixian Tang, Fan Wu, Chao Wang

“…However, existing TomoSAR height inversion methods do not fully leverage a priori knowledge, compromising the accuracy of deformation estimation; deep learning-based methods involve the integration of multiple steps, complicating the process. …”
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Graph-Based Few-Shot Learning for Synthetic Aperture Radar Automatic Target Recognition with Alternating Direction Method of Multipliers by Jing Jin, Zitai Xu, Nairong Zheng, Feng Wang

Subjects: “…alternating direction method of multipliers (ADMM)…”
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Magneto Inductive (MI) Channel Variables Prediction Through Machine Learning Linear Regression Method, for Underwater and Underground WSNs by Aman Muhammad, Fuzhong Li, Syed Agha Hassnain Mohsan, Zahid Ullah Khan, Waqar Khan, Sajid Ullah Khan, Ziqi Han, Faheem Khan

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Robust Anti-Jamming Method for Large-Array Radar Systems Using Deep Learning Based Null-Space Beamforming by Jaehyuk Lim, Hogeun Yoo, Euihyuk Lee, Sunjin Oh, Jaehoon Lee

“…The proposed method combines the computational simplicity of null-space beamforming with the adaptability of deep learning to effectively suppress interference and maintain a high signal-to-interference-plus-noise ratio (SINR). …”
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Impact of self-directed learning strategy, an innovative method in nursing undergraduates: Study protocol for a randomized controlled trial. by Sabina Chaudhary, Adarsh Lata Singh

“…Game based learning has a potential of motivating students' engagement and creating fascinating self-directed learning environment for favorable outcome. …”
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Machine learning method based on radiomics help differentiate posterior pituitary tumors from pituitary neuroendocrine tumors and craniopharyngioma by Yukun Liu, Yanpeng Zhou, Chunyao Zhou, Zhenmin Wang, Ziwen Fan, Kai Tang, Siyuan Chen

“…This study aimed to differentiate PPTs from PitNET and craniopharyngioma using a machine learning method based on radiomics. The cohort used for training and testing contained 33 PPTs and 99 non-posterior pituitary tumors (NPPTs). …”
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