Early identification of dropouts during the special forces selection program by Ruud J. R. den Hartigh, Rik Huijzer, Frank J. Blaauw, Age de Wit, Peter de Jonge

“…Using linear regression as well as state-of-the-art machine learning techniques, we aimed to build a model that could meaningfully predict dropout while remaining interpretable. …”
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Data-driven model discovery and model selection for noisy biological systems. by Xiaojun Wu, MeiLu McDermott, Adam L MacLean

“…Ordinary differential equation models are widespread; until recently their construction has required extensive prior knowledge of the system. Machine learning methods offer alternative means of model construction: differential equation models can be learnt from data via model discovery using sparse identification of nonlinear dynamics (SINDy). …”
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Predicting transient response using data-driven models for ball-impact simulations by Ross Pivovar, Fei Chen, Raghunath Katragadda, Vidyasagar Ananthan

“…This study investigates the application of machine learning (ML) models for predicting transient responses in ball-impact elastodynamics simulations. …”
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Application of adaptive ensemble neural network method for short-term load forecasting electrical engineering complex of regional electric grid by N. A. Serebryakov

“…The article is devoted to the problem of improving the accuracy of short-term load forecasting of electrical engineering complex of regional electric grid with the use deep machine learning tools. The effectiveness of the application of the adaptive learning algorithm for deep neural networks for short-term load forecasting of this electrical complex has been investigated. …”
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The use of drones and Artificial Intelligence for dugong sighting detection in a limited resource scenario by Ario Digdo Akbar, Astari Elisabeth, Riskyta Arinda Bella, Cahyono Topan

“…Therefore, more flights are needed to enhance the sample size for machine learning.…”
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Theoretical and computational investigations on estimation of viscosity of ionic liquids for green adsorbent: Effect of temperature and composition by Zhaoxiong Han

“…The viscosity of ionic liquids versus temperature and composition are estimated in this study via machine learning. The models used are Gaussian Process Regression (GPR), Multilayer Perceptron (MLP), and Support Vector Regression (SVR). …”
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Exploring Bare Ownership Supply of Housing in Urban Environments by Maria Rosaria Guarini, Alejandro Segura-de-la-Cal, Francesco Sica, Yilsy Núñez-Guerrero

“…This paper examines the status of bare ownership in the city of Rome by web scraping the house offers published on web portals and segmenting those offered as bare ownership. Machine learning analysis based on neural networks and binary logit regression allows for the observation of the particular behavior of the housing supply in bare ownership; it shows the different intrinsic and extrinsic characteristics that determine this Real Estate segment. …”
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Improvising Personalized Travel Recommendation System with Recency Effects by Paromita Nitu, Joseph Coelho, Praveen Madiraju

“…In this project, we have analyzed users’ twitter data, as well as their friends and followers in a timely fashion to understand recent travel interest. A machine learning classifier identifies tweets relevant to travel. …”
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Online Self-Supervised Learning for Accurate Pick Assembly Operation Optimization by Sergio Valdés, Marco Ojer, Xiao Lin

“…It then builds and evaluates multiple regression models through an auto machine learning implementation. The system selects the best-performing model to correct the misalignment and dynamically chooses between corrective strategies and the learned model, optimizing the cycle times and improving the performance during the cycle, without halting the production process. …”
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Bioinformatic analysis of ferroptosis related biomarkers and potential therapeutic targets in vitiligo by Linli Liu, Lingli Deng, Li Guan, Yuan Hu, Qianying Li, Chunshui Yu

“…Functional enrichment analysis revealed that these DE-FRGs are significantly involved in oxidative stress, immune regulation, and vitiligo-associated signaling pathways. Utilizing machine learning approaches, including LASSO and SVM-RFE, we identified four key marker genes (ALOX5, SNCA, SLC1A4, and IL33) with strong diagnostic potential. …”
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Digital technology and artificial intelligence issues in scientific works by A. N. Timokhovich, E. G. Samokhodkina, E. V. Samokhodkin, A. A. Elzon

“…The main semantic units, reflecting different aspects of the research field are digitalization; artificial intelligence (additional semantic units: knowledge representation, theorem proving, computer vision, robotics, machine learning, multi-agent systems, artificial intelligence tools); neural networks (additional semantic units: learning with a teacher, learning without a teacher, input data); strong or general artificial intelligence, weak or applied artificial intelligence; Marusya voice assistant, Alisa voice assistant, Siri voice assistant, Bixby voice assistant, Google Assistant; speech recognition, fingerprint recognition, human face identification. …”
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Air Transportation Direct Share Analysis and Forecast by Xufang Zheng, Chia-Mei Liu, Peng Wei

“…To find factors which have significant impacts on O&D direct share and to build an accurate model for O&D direct share forecasting, both parametric and nonparametric machine learning models are investigated in this research. …”
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A scientometric review of the relationship between learning agility and work engagement in modern management context by Farira Nareswari, Rini Juni Astuti

“…The results showed that learning agility, the ability to quickly adapt to new experiences, work commitment, focus on completing tasks and achieving goals are closely related. Machine learning, artificial neural networks, and predictive analytics can improve learning agility and work engagement. …”
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A Review Article: Mitigation Strategies for Seismic Damage in Structures by Raihana M. Ejam, Hatem Hadi Obeid

“…Additionally, the review discusses the potential of machine learning techniques in predicting seismicity rates and improving risk management in seismic-prone areas. …”
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Real-Time Travel Time Prediction Based on Evolving Fuzzy Participatory Learning Model by Yongyi Li, Ming Zhang, Yixing Ding, Zhenghua Zhou, Lingyu Xu

“…We employed and improved a machine learning method called the evolving fuzzy participatory learning (ePL) model to predict the freeway travel time online in this paper. …”
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An Improved YOLOv7-Tiny-Based Algorithm for Wafer Surface Defect Detection by Mengyun Li, Xueying Wang, Hongtao Zhang, Xiaofeng Hu

“…To address the shortcomings of manual inspection and the limitations of existing machine learning methods, this paper proposes a wafer defect detection algorithm based on an improved YOLOv7-tiny. …”
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Investigation of ANN Architecture for Predicting Load-Carrying Capacity of Castellated Steel Beams by Thuy-Anh Nguyen, Hai-Bang Ly, Van Quan Tran

“…The ANN model seems to be the best algorithm of machine learning for predicting the CSB load-carrying capacity.…”
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Accurate identification of primary site in tumors of unknown origin (TUO) using DNA methylation by Drew Duckett, Erica R. Vormittag-Nocito, Pouya Jamshidi, Madina Sukhanova, Stephanie Parker, Daniel J. Brat, Lawrence J. Jennings, Lucas Santana-Santos

“…Here, we develop a random forest machine learning TUO methylation classifier using a large number of primary and metastatic tumor samples. …”
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Risk in management decisions of the modern organization and methods of management of this risk by Veronika Holomozenko, Yuliia Stasiuk

“…Key risk management techniques such as quantitative and qualitative risk assessment tools, scenario analysis, and the use of advanced technologies such as artificial intelligence and machine learning are discussed. The study also discusses the role of risk management committees and the importance of establishing a risk-aware culture in an organization. …”
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Design and modeling of a nanocomposite system for demineralization of sweet whey by Mina Rezapour, Mohsen Esmaiili, Mehdi Mahmoudian, Alireza Behrooz Sarand

“…The dynamic flux behavior of whey output and salt rejection from whey was modeled using convolutional neural network (CNN) machine learning tools. Linear and non-linear correlations demonstrated that the CNN model correlates well with experimental data on dynamic flux (R2–1.00). …”
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