Time Series Modelling and Predictive Analytics for Sustainable Environmental Management—A Case Study in El Mar Menor (Spain) by Rosa Martínez, Ivan Felis, Mercedes Navarro, J. Carlos Sanz-González

“…In this study on data science and machine learning, time series analysis plays a key role in predicting evolving data patterns. …”
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Discount-Based Cloud Resource Management Using Cloud Broker by M Vinoth Kumar, Medhavi Malik, Suchita Arora, Vinam Tomar, Sunita Pachar, Abhishek Yadav

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Prediction of elastic modulus, yield strength, and tensile strength in biocompatible titanium alloys by Marković G., Ružić J., Sokić M., Milojkov D., Manojlović V.D.

“…Furthermore, there is a need to understand the relationship between parameters and properties, and machine learning is being applied to make the whole process cheaper and more efficient. …”
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Employing Spectral Features to Accelerate Sorghum Phenotyping Against Sap‐Feeding Aphids by Kumar Shrestha, Kantilata Thapa, Esha Kaler, Misaki Taniguchi, Scott E. Sattler, James C. Schnable, Joe Louis

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Posts Quality Prediction for StackOverflow Website by Jiawei Hu, Bo Yang

“…After training 5 different machine learning models, including decision trees, random forests, naive Bayes, support vector machines, logistic regression, and 2 deep learning models, Bi-LSTM and BERT, these models are compared through experiments by adjusting the values of different parameters. …”
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Characteristics determination for milling working equipment of excavator by I. S. Kuznetsov, S. V. Saveliev

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Data-Driven Prediction of Binder Rheological Performance in RAP/RAS-Containing Asphalt Mixtures by Eslam Deef-Allah, Magdy Abdelrahman

“…The framework predicted the rheological resistance of the binders to rutting and cracking using linear and nonlinear machine learning models. The nonlinear models outperformed the linear models for the three rheological parameters. …”
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Experimental verification of position sensing for a magnetic coil via Fano resonance by Shrinathan Esaki Muthu Pandara Kone, Kenichi Yatsugi, Mikiko Suzuki, Michio Yasunishi, Hiroshi Yoshimoto, Xiaopeng Li, Ziqi Yu, Taehwa Lee, Hideo Iizuka

“…Measured spectra of scattering parameters reasonably agree with analytical and numerical results. …”
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Artificial intelligence-driven modeling of biodiesel production from fats, oils, and grease (FOG) with process optimization via particle swarm optimization by Badril Azhar, Muhammad Ikhsan Taipabu, Cries Avian, Karthickeyan Viswanathan, Wei Wu, Raymond Lau

“…This study presents the design and optimization of a biodiesel production process, emphasizing the integration of machine learning (ML) models and process optimization techniques. …”
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Prediction of the Ultimate Impact Response of Concrete Strengthened with Polyurethane Grout as the Repair Material by Sadi I. Haruna, Yasser E. Ibrahim, Sani I. Abba

“…This study uses explainable deep learning techniques to evaluate the ultimate strength capacity (<i>U</i>s) of U-shaped normal concrete (NC) strengthened with polyurethane grouting (PUG) materials. Machine learning algorithms (ML) such as Long Short-Term Memory (LSTM), Random Forest (RF), and Wide Neural Network (WNN) models were developed to estimate <i>U</i>s by considering five input parameters: the initial crack strength (<i>C</i>s), thickness of the grouting materials (<i>T</i>), mid-span deflection (<i>λ</i>), and peak applied load (<i>P</i>). …”
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Distinguishing Dyslexia, Attention Deficit, and Learning Disorders: Insights from AI and Eye Movements by Alae Eddine El Hmimdi, Zoï Kapoula

“…Machine learning models, including logistic regression, random forest, support vector machines, and neural networks, are trained using a GroupKFold strategy to ensure patient data are present in either the training or test set. …”
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Determination of torque for milling working equipment of excavator when repairing a pipeline by I. S. Kuznetsov

“…It makes possible to get the dependence of the resistance forces arising on the cutters of the working equipment, on its design parameters and the physical and mechanical properties of the soil.Results. …”
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A XGBoost-Based Prediction Method for Meat Sheep Transport Stress Using Wearable Photoelectric Sensors and Infrared Thermometry by Ruiqin Ma, Runqing Chen, Buwen Liang, Xinxing Li

“…The accuracy of the assessment of the transport stress state of meat sheep after the optimization of three parameters was 100%, 90.91%, and 93.33%, and the classification accuracy of the overall model reached 94.92%. …”
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Investigation of Mechanical and Corrosion Behavior of ECAP Processed AA7075 Through ML, ANNW, RSM, and SA Methodologies by Majed Alinizzi, W. H. El‐Garaihy, A. I. Alateyah, Samar El‐Sanabary, Fahad Nasser Alsunaydih, Mansour Alturki, H. Abd El‐Hafez, Mohamed S. El‐Asfoury, Eman M. Zayed, Hanan Kouta

“…ABSTRACT This study employs a multi‐perspective modeling approach combining Response Surface Methodology (RSM), Machine Learning (ML), Artificial Neural Networks (ANNW), and Simulated Annealing (SA) to optimize Equal Channel Angular Pressing (ECAP) parameters for improving the mechanical and corrosion properties of AA7075 alloy. …”
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Preoperative detection of extraprostatic tumor extension in patients with primary prostate cancer utilizing [68Ga]Ga-PSMA-11 PET/MRI by Clemens P. Spielvogel, Jing Ning, Kilian Kluge, David Haberl, Gabriel Wasinger, Josef Yu, Holger Einspieler, Laszlo Papp, Bernhard Grubmüller, Shahrokh F. Shariat, Pascal A. T. Baltzer, Paola Clauser, Markus Hartenbach, Lukas Kenner, Marcus Hacker, Alexander R. Haug, Sazan Rasul

“…The presence of EPE was measured from post-surgical histopathology and predicted using ML and pre-operative parameters, including PET/MRI-derived features, blood-based markers, histology-derived parameters, and demographic parameters. …”
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Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN by Vladislav Kaverinskiy, Illya Chaikovsky, Anton Mnevets, Tatiana Ryzhenko, Mykhailo Bocharov, Kyrylo Malakhov

“…This study explores the potential of unsupervised machine learning algorithms to identify latent cardiac risk profiles by analyzing ECG-derived parameters from two general groups: clinically healthy individuals (Norm dataset, <i>n</i> = 14,863) and patients hospitalized with heart failure (patients’ dataset, <i>n</i> = 8220). …”
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Predictive potential of cardiovascular risk factors and their associations with arterial stiffness in people of European and Korean ethnic groups by T. A. Brodskaya, V. A. Nevzorova, K. I. Shakhgeldyan, B. I. Geltser, D. A. Vrazhnov, Yu. V. Kistenev

“…Developed using modern machine learning technologies, the assessment aortic PWV models taking into account the ethnic factor can be a useful tool for processing and analyzing data in predictive studies.…”
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Predicting grade II-IV bone marrow suppression in patients with cervical cancer based on radiomics and dosiomics by Yanchun Tang, Yanchun Tang, Yaru Pang, Jingyi Tang, Jingyi Tang, Xinchen Sun, Xinchen Sun, Peipei Wang, Jinkai Li

“…Predictive models were constructed by intergrating clinical predictors with DVH parameters, combining DVH parameters and R-score with clinical predictors, and amalgamating clinical predictors with both D-score and R-score. …”
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Retrieval of crop traits using PROSAIL-based hybrid radiative transfer model and EnMAP hyperspectral data by Prachi Singh, Prashant K. Srivastava, Prakash Kumar Jha, Jochem Verrelst, Pashupati Nath Singh, Rajendra Prasad

“…The proposed methodology involves the integration and detailed analysis of Radiative Transfer Modelling (RTM) with an integrated approach of machine learning (ML) and Active Learning (AL) algorithms for the retrieval of the Leaf Chlorophyll Content (LCC), Carotenoids (Car) and Leaf Area index (LAI) of wheat cropland from the continuous three years of the dataset. …”
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Digital Model and Assembling of a Lathe by Besedin M., Popowska M., Ivanov V., Trojanowska J.

“…The geometric modeling techniques and the design documentation were implemented to justify the rational choice of design parameters of the machine tool design and its spatial model. …”
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