Physical Health Portrait and Intervention Strategy of College Students Based on Multivariate Cluster Analysis and Machine Learning by Rong Guo, Rou Dong, Ni Lu, Lin Yu, Chaoxian Chen, Yonglin Che, Jiajin Zhang, Jianke Yang

“…Machine learning models—including Random Forest, decision trees, Gradient Boosting Trees, and logistic regression—were utilized to validate the clustering outcomes and to identify key health indicators associated with different student groups. Based on the clustering and model analysis, targeted intervention programs are proposed, such as strength training for groups with low muscular explosiveness, endurance training for those with stamina deficiencies, and flexibility exercises for groups exhibiting limited mobility. …”
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Training and synchronizing oscillator networks with Equilibrium Propagation by Théophile Rageau, Julie Grollier

“…Thus far, these systems have primarily been demonstrated in small-scale implementations, such as Ising machines for solving combinatorial problems and associative memories for image recognition, typically trained without state-of-the-art gradient-based algorithms. …”
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Book Recommendation Using Collaborative Filtering Algorithm by Esmael Ahmed, Adane Letta

“…In model-based approach, K-nearest neighbour (KNN) and singular value decomposition (SVD) algorithms are also assessed experimentally. The SVD model is trained on our dataset optimized with a scored RMSE 0.1623 compared to RMSE 0.1991 before the optimization. …”
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Optimizing Stroke Classification with Pre-Trained Deep Learning Models by Serra Aksoy, Pinar Demircioglu, Ismail Bogrekci

“…The model was trained and validated on a labeled dataset to identify critical indicators and patterns associated with stroke risk. …”
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Overcoming Hardware Imperfections in Optical Neural Networks Through a Machine Learning-Driven Self-Correction Mechanism by Minjoo Kim, Beomju Kim, Yelim Kim, Lia Saptini Handriani, Suhee Jang, Dae Yeop Jeong, Sung Ik Yang, Won Il Park

“…Rather than pursuing an elusive, imperfection-free ONN or attempting to calibrate these defects individually, we addressed these challenges by introducing a self-correction mechanism that utilizes a machine learning algorithm. This approach effectively restored the recognition accuracy and minimized loss of our ONN to levels comparable to the digitally pre-trained model. …”
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Prediction of Train Arrival Delay Using Hybrid ELM-PSO Approach by Xu Bao, Yanqiu Li, Jianmin Li, Rui Shi, Xin Ding

“…First, nine characteristics (e.g., buffer time, the train number, and station code) associated with train arrival delays are chosen and analyzed using extra trees classifier. …”
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Analysis of effectiveness of the program of joined air traffic controlles and pilotes training by A. I. Stepnova, S. M. Stepanov, V. V. Borsoeva, V. A. Borsoev

“…As you know, the work of air traffic controllers is associated with the work of pilots, but training in educational institutions takes place separately, resulting in gaps in knowledge of the specifics of the adjacent specialty, and, eventually, leads to errors. …”
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Association of weight-adjusted waist index and body mass index with chronic low back pain in American adults: a retrospective cohort study and predictive model development based on... by Weiye Zhang, Weiye Zhang, Yan Li, Pengwei Shao, Yuxuan Du, Ke Zhao, Jiawen Zhan, Lee A. Tan

“…Receiver operating characteristic (ROC) curves were plotted to determine which indicator demonstrated stronger association with CLBP. Subsequently, permutation feature importance was applied for machine learning feature selection, random undersampling was utilized to address data imbalance, and the dataset was randomly divided into training and testing sets at a 7:3 ratio. …”
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The implementation of the situational control concept of information security in automated training systems by A. M. Chernih, S. V. Fedoseev

“…The main approaches to ensuring security of information in the automated training systems are considered, need of application of situational management of security of information for the automated training systems is proved, the mathematical model and a problem definition of situational control is offered, the technique of situational control of security of information is developed.The purpose of the study. …”
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Imminent dystopia? Media coverage of algorithmic surveillance at Berlin-Südkreuz by Anna Verena Eireiner

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Digital Staining Algorithm for Multi-Domain Transformation of Unstained Images by Dong-Bum Kim, Jong-Ha Lee

“…The proposed model was trained by incorporating a mask and latent loss within the framework of the StarGAN algorithm. …”
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Optimized Detection Algorithm for Vertical Irregularities in Vertical Curve Segments by Rong Xie, Chunjun Chen

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Belief in building a full-fledged distance learning course in athletic training by Andrii Yefremenko, Illia Shutieiev

“…An algorithm for organising practical training in athletic training has been formed. …”
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Hybrid machine learning algorithms accurately predict marine ecological communities by Luciana Erika Yaginuma, Luciana Erika Yaginuma, Fabiane Gallucci, Danilo Cândido Vieira, Paula Foltran Gheller, Simone Brito de Jesus, Thais Navajas Corbisier, Gustavo Fonseca

“…The unsupervised phase detected that the nematodes were geographically structured in six associations, each with representative genera. In the supervised stage, these associations were modeled as a function of the environmental features by five supervised algorithms (Support Vector Machine, Random Forest, k-Nearest Neighbors, Naive Bayes, and Stochastic Gradient Boosting), using 80% of the samples for training, leaving the remaining for testing. …”
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Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms by Mintae Kim, Muharrem A. Senturk, Liang Li

“…The dataset includes LL, PL, <i>W</i>, PI, <i>G_s</i>, and <i>e</i>₀ as the inputs, with <i>C_c</i> as the output parameter. The algorithms are trained and evaluated using metrics such as the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). …”
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GENETIC ALGORITHM-PID CONTROLLER FOR MODEL ORDER REDUCTION PANTOGRAPHCATENARY SYSTEM by Nasir A. Al-Awad, Izz K. Abboud, Muaayed F. Al-Rawi

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Identification of hypertension gene expression biomarkers based on the DeepGCFS algorithm. by Zongjin Li, Liqin Tian, Libing Bai, Zeyu Jia, Xiaoming Wu, Changxin Song

“…This algorithm utilizes a graph network to represent the interaction information between genes, builds a GNN model, designs a loss function based on link prediction and self-supervised learning ideas for training, and allows each gene node to obtain a feature vector representing global information. …”
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Machine Learning Algorithm for Estimating Surface PM2.5 in Thailand by Pawan Gupta, Shanshan Zhan, Vikalp Mishra, Aekkapol Aekakkararungroj, Amanda Markert, Sarawut Paibong, Farrukh Chishtie

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Interaction of the Shock Train Leading Edge and Filamentary Plasma in a Supersonic Duct by Loren C. Hahn, Philip A. Lax, Scott C. Morris, Sergey B. Leonov

“…Quasi-direct current (Q-DC) filamentary electrical discharges are used to control the shock train in a back-pressured Mach 2 duct flow. The coupled interaction between the plasma filaments and the shock train leading edge (STLE) is studied for a variety of boundary conditions. …”
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An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOT by Bahman Sanjabi, Mahmood Ahmadi

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