Real-time traffic enhancement scheduling for train communication networks based on TSN by Deqiang He, Zeqian Chen, Daliang Sun, Zhenzhen Jin, Yanjun Chen, Rui Ma, Chen Liang

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Boosting Barlow Twins Reduced Order Modeling for Machine Learning‐Based Surrogate Models in Multiphase Flow Problems by T. Kadeethum, V. L. S. Silva, P. Salinas, C. C. Pain, H. Yoon

“…To address the challenge of high contrast data in multiphase flow problems due to injection wells and faults, we employ a boosting algorithm within BBT‐ROM. This algorithm sequentially trains a set of weak models (i.e., inaccurate models), improving prediction accuracy through ensemble learning. …”
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Mathematical model of the formation of the basic statistical sample for evaluating the level of the digital competence of lecturers by Svetlana V. Avilkina, Marina A. Bakuleva, Nadezhda P. Kleynosova

“…Further researches are planned to be conducted in the sphere of automation of process of the statistical data analysis on digitalization of the population of the region, first of all in the sphere of professional education. On the basis of the mathematical model the algorithm of analytical processing of statistical data on monitoring of digital competences is developed.…”
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Lightweight CNC digital process twin framework: IIoT integration with open62541 OPC UA protocol by Arivazhagan Anbalagan, Waqir Yusuf Zanhar, Shone George, Marcos Kauffman, Tengfei Long

“…This data trained five ML models to predict sensor positions with high accuracies (Random-Forest: R²(0.9994), KNN: R²(0.9998). …”
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Optimal Task Offloading Strategy for Vehicular Networks in Mixed Coverage Scenarios by Xuewen He, Yuhao Cen, Yinsheng Liao, Xin Chen, Chao Yang

“…This study employs long short-term memory networks to predict the loading status of base stations. Then, based on the prediction results, we propose an optimized task offloading strategy using the proximal policy optimization algorithm. …”
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Design and Deployment of ML in CRM to Identify Leads by Alonso Yocupicio-Zazueta, Agustin Brau-Avila, Federico Cirett-Galán, Margarita Valenzuela-Galván

“…In Jupyter Notebooks, logistic regression was utilized to design and to train a model to accurately predict whether a lead will convert into a client or not. …”
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Dynamic Water Scheduling in the Northwest River Delta Basin Based on Minimum Discharge Flow Control in Cross-section by WU Di, FANG Chunyan, SHANG Yunfei

“…In response to the problem of insufficient future demand forecasting and poor scheduling balance caused by traditional watershed water scheduling methods relying on historical data and fixed rules, a dynamic water scheduling method for the Northwest River Delta Basin based on minimum discharge flow control of cross-sections was proposed. By using the grey prediction model, the production and domestic water consumption in the downstream areas of the Northwest River Delta Basin was accurately predicted. …”
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Twin Support Vector Regression Model Based on Heteroscedastic Gaussian Noise and Its Application by Shiguang Zhang, Ge Feng, Feng Yuan, Shuangle Guo

“…The main purpose of twin support vector regression (TSVR) is to find linear or nonlinear relationships in sample data, and then predict future data. TSVR is the decomposition of a large convex quadratic programming problem into two small convex quadratic programming problems. …”
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Applications, Challenges, and Future Perspectives of Artificial Intelligence in Psychopharmacology, Psychological Disorders and Physiological Psychology: A Comprehensive Review by Mohammad Hossein Salemi, Elham Foroozandeh, Molouk Khademi Ashkzari

“…Personalized medicine, powered by AI, predicts individual medication responses, minimizing side effects and optimizing outcomes. …”
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Analytical approach for modelling of thread crimp in jacquard woven two-dimensional fabrics by Brigita Kolcavová Sirková, Iva Mertová

“…The theoretical predicted values of thread crimp in jacquard fabrics were compared with experimentally obtained values. …”
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A Quantitative Evaluation Method Based on Back Analysis and the Double-Strength Reduction Optimization Method for Tunnel Stability by Jinglai Sun, Fan Wang, Xinling Wang, Xu Wu

“…To feasibly and reliably obtain geotechnical parameters for the surrounding rock (which vary in different places), a real-coded genetic algorithm is used in setting the initial parameters of the neural network to improve the prediction accuracy of the parameters via back analysis by reasonably selecting the selection operator, crossover operator, and mutation operator. …”
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The System Research and Implementation for Autorecognition of the Ship Draft via the UAV by Wei Zhan, Shengbing Hong, Yong Sun, Chenguang Zhu

“…In order to solve the above problems, this paper introduces the computer image processing technology based on deep learning, and the specific process is divided into three steps: first, the video sampling is carried out by the UAV to obtain a large number of pictures of the ship draft reading face, and the images are preprocessed; then, the deep learning target detection algorithm of improved YOLOv3 is used to process the images to predict the position of the waterline and identify the draft characters; finally, the prediction results are analyzed and processed to obtain the final reading results. …”
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Towards a New Taxonomy of Pattern-Making in the Visuo-Spatial Domain in Early Childhood Based on Zygonic Theory and the Sounds of Intent Framework of Musical Development by Adam Ockelford, Sarah McCarthy, Sue Gifford, Helen Thouless, Sharon Kirk, Michael Thorpe

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Real-time classification of EEG signals using Machine Learning deployment by Swati CHOWDHURI, Satadip SAHA, Samadrita KARMAKAR, Ankur CHANDA

“…This study proposes a machine learning-based approach for predicting the level of students' comprehension with regard to a certain topic. …”
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Forecasting Ultrafine Dust Concentrations in Seoul: A Machine Learning Approach by Sophia Park, Myeong Jun Kim

“…Using daily data from 1 January 2018 to 30 June 2023, this study employed the Boruta algorithm, a variable selection technique based on the random forest model, to identify the most influential predictors for predicting PM2.5 concentrations. …”
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Monitoring the Concentrations of Na, Mg, Ca, Cu, Fe, and K in <i>Sargassum fusiforme</i> at Different Growth Stages by NIR Spectroscopy Coupled with Chemometrics by Sisi Wei, Jing Huang, Ying Niu, Haibin Tong, Laijin Su, Xu Zhang, Mingjiang Wu, Yue Yang

“…Superior CARS-PLS models were established for Na, Mg, Ca, Cu, Fe, and K with root mean square error of prediction (<i>RMSEP</i>) values of 0.8196 × 10³ mg kg⁻¹, 0.4370 × 10³ mg kg⁻¹, 1.544 × 10³ mg kg⁻¹, 0.9745 mg kg⁻¹, 49.88 mg kg⁻¹, and 7.762 × 10³ mg kg⁻¹, respectively, and coefficient of determination of prediction (<i>R_P</i>²) values of 0.9787, 0.9371, 0.9913, 0.9909, 0.9874, and 0.9265, respectively. …”
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Genetic Subtype‐Based International Prognostic Index Prognostic Model in Diffuse Large B‐Cell Lymphoma by Lan Mi, Jili Deng, Jiayue Qin, Chen Zhang, Lixia Liu, Shunli Yang, Libin Chen, Hua‐Jun Wu, Haojie Wang, Jun Zhu, Hong Chen, Feng Lou, Shanbo Cao, Yuqin Song, Weiping Liu

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Automated CATS system for distance learning by Yu. B. Popova

“…These mathematical methods made it possible to develop adaptability algorithms, their software implementation and testing in the educational process. …”
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AgriChainSync: A Scalable and Secure Blockchain-Enabled Framework for IoT-Driven Precision Agriculture by Hassam Ahmed Tahir, Walaa Alayed, Waqar Ul Hassan, Fahad Nabi, Truong X. Tran

“…The rapid advancement of precision farming, automated irrigation systems, and predictive analytics has revolutionized agriculture, but these innovations also introduce new challenges, particularly in data integrity and security. …”
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SHapley Additive exPlanations (SHAP) for Landslide Susceptibility Models: Shedding Light on Explainable AI by H. Al-Najjar, B. Kalantar, B. Pradhan, G. Beydoun, N. Ueda

“…Various evaluation metrics, including overall accuracy and precision-recall, are employed to assess the predictive capabilities of each model. The findings reveal the strengths and limitations of both models, providing valuable insights for stakeholders and decision-makers involved in land use planning and disaster preparedness. …”
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