Prediction of Monthly Temperature Over China Based on a Machine Learning Method by Ping Mei, Zixin Yin, Haoyu Wang, Changzheng Liu, Yaoming Liao, Qiang Zhang, Liping Yin

“…Five machine learning algorithms are employed as regressors one by one: linear regression (LR), ridge regression (RR), random forest (RF), support vector machine (SVM), and gradient boosting decision trees (GBDTs). …”
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Predicting asphaltene precipitation during natural depletion of oil reservoirs by integrating SARA fractions with advanced intelligent models by Behnam Amiri-Ramsheh, Sara Sahebalzamani, Reza Zabihi, Abdolhossein Hemmati-Sarapardeh

“…Four smart models, namely, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), cascade forward neural network (CFNN), and generalized regression neural network (GRNN) were constructed. To train the CFNN, the Levenberg-Marquardt (LM) algorithm was implemented. …”
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Relationship Between Weight Status and Health-Related Quality of Life in School-age Children in China by Mandana Zanganeh, Peymané Adab, Bai Li, Miranda Pallan, Wei J. Liu, Lin Rong, Wei Liu, James Martin, Kar K. Cheng, Emma Frew

“…CHU-9D-CHN utility scores were generated using 2 scoring algorithms (UK and Chinese tariffs). Height and weight measures were taken at school by trained researchers using standardized methods, and BMI _z_ scores were calculated using the World Health Organization 2007 growth charts. …”
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Enhancing phase change thermal energy storage material properties prediction with digital technologies by Minghao Yu, Jing Liu, Cheng Chen, Mingyue Li

“…The method integrates MD simulations for atomic-level interactions using Lennard-Jones and embedded-atom method (EAM) potentials, FEM-based continuum mechanics for stress-strain analysis and thermal response evaluation, and ML techniques trained on multiscale descriptors (e.g., bond energy, stress tensor, coordination number) to model nonlinear property relations and accelerate design iteration. …”
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Integrating explainable artificial intelligence and light gradient boosting machine for glioma grading by Teuku Rizky Noviandy, Ghalieb Mutig Idroes, Irsan Hardi

“…Utilizing a dataset from The Cancer Genome Atlas, which comprises molecular and clinical characteristics of 839 glioma patients, the LightGBM model is meticulously trained, and its parameters finely tuned. Its performance is benchmarked against various other ML models through a comprehensive evaluation involving metrics such as accuracy, precision, recall, and F1-score. …”
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Data augmentation of time-series data in human movement biomechanics: A scoping review. by Christina Halmich, Lucas Höschler, Christoph Schranz, Christian Borgelt

“…These challenges make it difficult to train models that perform reliably across individuals, tasks, and settings. …”
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BiLSTM-Based Parallel CNN Models With Attention and Ensemble Mechanism for Twitter Sentiment Analysis by Anas W. Abulfaraj

“…Our methodology incorporates four classifiers to produce text class predictions. Among them, five algorithms are selected for evaluation: Ridge Classifier (RC), Linear Discriminant Analysis (LDA), Extra Trees (ET), and Light Gradient Boosting Machine (LightGBM). …”
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High-throughput end-to-end aphid honeydew excretion behavior recognition method based on rapid adaptive motion-feature fusion by Zhongqiang Song, Jiahao Shen, Qiaoyi Liu, Wanyue Zhang, Ziqian Ren, Kaiwen Yang, Xinle Li, Jialei Liu, Fengming Yan, Wenqiang Li, Yuqing Xing, Lili Wu

“…This framework integrates spatiotemporal motion features with deep learning architectures to enhance detection accuracy and operational efficacy.MethodsThis study established the first fine-grained dataset encompassing aphid Crawling Locomotion(CL), Leg Flicking(LF), and HE behaviors, offering standardized samples for algorithm training. A rapid adaptive motion feature fusion algorithm was developed to accurately extract high-granularity spatiotemporal motion features. …”
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Performance and emission analysis of CI engine fueled with Dunaliella salina biodiesel and TiO₂ nanoparticle additives: Experimental and ANN-based Predictive Approach by V Hariram, S Balamurugan, R Mohan, R Karthick, Nandagopal Kaliappan, K Barathiraja, J Godwin John, K Kamakshi Priya

“…This study aims to optimize the biodiesel production process and evaluate its feasibility in compression ignition (CI) engine applications. …”
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Implications of machine learning techniques for prediction of motor health disorders in Saudi Arabia by Ehab M. Almetwally, I. Elbatal, Mohammed Elgarhy, Amr R. Kamel

“…The algorithms were evaluated using a standardized dataset obtained from the Kaggle website. …”
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Machine learning-based prediction model for recurrence after radiofrequency catheter ablation in patients with atrial fibrillation by Lujing Nie, Tianwei Zhang, Wenhua Wang, Xuefu Han, Meng Liu, Shujie Zhang, Wenjiu Feng, Yujie Wang, Yanbo Chen

“…Patients were randomly assigned to either a training cohort (70%) or a testing cohort (30%). Four ML algorithms were employed to develop prediction models. …”
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Leveraging Machine Learning for Accurate and Fast Stellar Mass Estimation of Galaxies by Vahid Asadi, Akram Hasani Zonoozi, Hosein Haghi, Fatemeh Abedini, Atousa Kalantari, Marziye Jafariyazani, Nima Chartab

“…We train various ML algorithms using simple model SEDs in photometric space, generated with the BC03 code. …”
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Professional competence of the person in the Smart-society by Nina V. Komleva

“…In addition, requirements for the employees are changing, and the person must evaluate its relevance to society. This raises the question: how to evaluate the relevance? …”
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Predicting ICU mortality in heart failure patients based on blood tests and vital signs by Yeao Wang, Jianke Rong, Zhili Wei, Xiaoyu Bai, YunDan Deng

“…Enhance the accuracy of data analysis and improve the universality of the model, all data underwent rigorous preprocessing prior to training, combined with data standardization. We utilized a variety of machine learning algorithms for modeling purposes, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Trees, Random Forests, Gradient Boosting Machines (GBM), XGBoost, and Neural Networks. …”
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A Machine Learning-Based Guide for Repeated Laboratory Testing in Pediatric Emergency Departments by Adi Shuchami, Teddy Lazebnik, Shai Ashkenazi, Avner Herman Cohen, Yael Reichenberg, Vered Shkalim Zemer

“…The DT model was developed and evaluated on training and validation cohorts, and it demonstrated high accuracy in predicting the need for repeat CBC and ELE tests but not CRP. …”
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Artificial Intelligence Control Methodologies for Shape Memory Alloy Actuators: A Systematic Review and Performance Analysis by Stefano Rodinò, Giuseppe Rota, Matteo Chiodo, Antonio Corigliano, Carmine Maletta

“…Challenges persist in computational demands for online training and reinforcement learning’s exploration–exploitation trade-offs. …”
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Machine learning prediction of metabolic dysfunction-associated fatty liver disease risk in American adults using body composition: explainable analysis based on SHapley Additive e... by Yan Hong, Xinrong Chen, Ling Wang, Fan Zhang, ZiYing Zeng, Weining Xie

“…The Boruta algorithm was used for feature selection, and model performance was evaluated using cross-validation and a validation set. …”
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Construction and validation of HBV-ACLF bacterial infection diagnosis model based on machine learning by Neng Wang, Shuai Tao, Liang Chen

“…Among them, 786 patients were assigned to the training set, and 338 patients were assigned to the test set. …”
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Screening of endoplasmic reticulum stress characteristic genes and immune infiltration manifestations in chronic obstructive pulmonary disease by ZHANG Shuang, LUO Chenyang, HE Zhiyi

“…Three machine learning algorithms, LASSO, SVM-RFE, and RF, were used to screen the characteristic genes, and their diagnostic performance was verified and evaluated in the GSE10006. …”
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Development of an ensemble prediction model for acute graft-versus-host disease in allogeneic transplantation based on machine learning by Lin Song, Xingwei Wu, Mengjia Xu, Ling Xue, Xun Yu, Zongqi Cheng, Chenrong Huang, Liyan Miao

“…Then fifteen algorithms were used to establish models, and an ensemble model was established through soft voting based on the top five performance algorithms. …”
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