Analysis of moving process of grain material components in suction chamber of grain-cleaning machine by A. L. Glushkov

“…Conducting of theoretic investigations with accounting of resistance force is necessary for determination of optimal parameters of working organs.…”
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Image-Based Laser-Beam Diagnostics Using Statistical Analysis and Machine Learning Regression by Tayyab Imran, Muddasir Naeem

“…This study is a comprehensive experimental and computational investigation into high-resolution laser beam diagnostics, combining classical statistical techniques, numerical image processing, and machine learning-based predictive modeling. A dataset of 50 sequential beam profile images was collected from a femtosecond fiber laser operating at a central wavelength of 780 nm with a pulse duration of approximately 125 fs. …”
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Enhanced Prediction and Uncertainty Modeling of Pavement Roughness Using Machine Learning and Conformal Prediction by Sadegh Ghavami, Hamed Naseri, Farzad Safi Jahanshahi

“…The performance of the methods was compared, and the light gradient boosting machine was identified as the best-performing method for IRI prediction. …”
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Remote sensing and TerraClimate datasets for wheat yield prediction using machine learning by Alireza Araghi, Andre Daccache

“…Using biophysical crop models to forecast yields is laborious and necessitates various, often unavailable, pedo-climatic, crop-specific, and management parameters. This study leverages satellite imagery and a gridded climate dataset (TerraClima) with machine learning (ML) to predict wheat yields in Mashhad County (Northeast Iran). …”
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Shale volume estimation using machine learning methods from the southwestern fields of Iran by Parirokh Ebrahimi, Ali Ranjbar, Yousef Kazemzadeh, Ali Akbari

“…However, estimating shale volume presents significant challenges, particularly in complex formations. In recent years, machine learning (ML) algorithms have gained prominence for shale volume estimation due to their capability to manage large datasets and complex relationships. …”
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Early detection of feline chronic kidney disease via 3-hydroxykynurenine and machine learning by Ellen Vanden Broecke, Laurens Van Mulders, Ellen De Paepe, Dominique Paepe, Sylvie Daminet, Lynn Vanhaecke

“…The serum-to-urine ratio of 3-hydroxykynurenine was identified as a single biomarker candidate, yielding a high AUC (0.844) and accuracy (0.804), while linear support vector machine-based modelling employing metabolites and clinical parameters enhanced AUC (0.929) and accuracy (0.862) six months before traditional diagnosis. …”
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Smart Strip-Till One-Pass Machine: Winter Wheat Sowing Accuracy Assessment by Dariusz Jaskulski, Iwona Jaskulska, Emilian Różniak, Maja Radziemska, Barbara Klik, Martin Brtnický

“…Modern agricultural machines are subject to requirements that result from developments in plant cultivation technology and environmental care. …”
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The Downscaled GOME-2 SIF Based on Machine Learning Enhances the Correlation with Ecosystem Productivity by Chenyu Hu, Pinhua Xie, Zhaokun Hu, Ang Li, Haoxuan Feng

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Predictive modeling of oil rate for wells under gas lift using machine learning by Famin Ma, Farag M. A. Altalbawy, Pinank Patel, R. Manjunatha, Rishiv Kalia, Shoira Formanova, P. Raja Naveen, Kamal Kant Joshi, Aashna Sinha, Abdolali Yarahmadi Kandahari, Taqi Mohammed Khattab Al-Rubaye, Mohammad Mahtab Alam

“…Abstract Optimizing oil production in wells employing gas lift systems is a critical challenge due to the complex interplay of operational and reservoir parameters. This study aimed to develop robust predictive models for estimating oil production rates using a comprehensive dataset from oil fields in south-eastern Iraq, leveraging advanced machine learning techniques. …”
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Prediction of compressive strength of fly ash-based geopolymers concrete based on machine learning by Hesong Hu, Mingye Jiang, Mengxiong Tang, Huqing Liang, Hao Cui, Chunlin Liu, Chunjie Ji, Yaozeng Wang, Simin Jian, Chaohai Wei, Siqi Song

“…Therefore, this study employs the use of machine learning models including Random Forest (RF), Artificial Neural Network (ANN), Gradient Boosting Decision Tree (GBDT), and Support Vector Machine (SVM) to predict the compressive strength of alkali-activated fly ash geopolymer concrete. …”
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Machine learning-based detection of medical service anomalies: Kazakhstan’s health insurance data by Maksut Kulzhanov, Alexander Wagner, Abylkair Skakov, Iliyas Mukhamejan, Saya Zhorabek, Ainur B. Qumar

“…With the exponential growth of medical data and limited analytical resources, healthcare systems are increasingly adopting Artificial Intelligence (AI) and Machine Learning (ML) technologies to enhance their decision-making processes. …”
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Activity recognition in motor-manual cross-cutting operations by machine learning on multimodal data by Stelian Alexandru Borz, Tomi Kaakkurivaara, Gabriel Osei Forkuo, Nopparat Kaakkurivaara

“…In forest operations, established time-study methods, such as the use of a stopwatch and video recording, have dominated for several years. Advancements in machine learning and innovative data loggers present opportunities to reconsider and enhance these methods. …”
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Applying Machine Learning for Sustainable Farm Management: Integrating Crop Recommendations and Disease Identification by P. Ankit Krishna, Neelamadhab Padhy, Archana Patnaik

“…These sensors supply vital information on temperature, humidity, soil nutrients, and other environmental parameters that are critical for crop selection. To suggest appropriate crops and detect pertinent plant diseases, cutting-edge machine learning and deep learning algorithms were used. …”
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Exploring machine learning-based archetypes for urban life cycle modeling (UBiM) by Giseli Mary Colleto, Vanessa Gomes

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Automated Measurement of Air Bubbles Dispersion in Ice Cream Using Machine Learning Methods by Igor A. Korolev

“…The automatic markup program employed the Python programming language, the Keras machine learning library, and the TensorFlow framework. …”
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Assessment of road-cut slope stability using empirical, numerical, and machine learning methodologies by Virat Singh Chauhan, Md. Rehan Sadique, Mohd. Masroor Alam, Mohd. Ahmadullah Farooqi

“…The critical parameters involved in slope stability analyses have been collected through extensive field visits. …”
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Machine Learning Inspired Design of Complex-Shaped GaN Subwavelength Grating Reflectors by Onoriode N. Ogidi-Ekoko, Wen Liang, Haotian Xue, Nelson Tansu

“…Our results show the rectangular grating exhibits slightly higher fabrication tolerances for grating parameters than the polynomial-shaped grating in general. …”
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Machine learning framework for investigating nano- and micro-scale particle diffusion in colonic mucus by Marco Tjakra, Kristína Lidayová, Christophe Avenel, Christel A.S. Bergström, Shakhawath Hossain

“…From each particle trajectory, 20 features —including microrheology-based parameters— were extracted. Based on these features, seven supervised machine learning models were applied to classify or identify similarities among mucus hydrogels. …”
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Optimisation of Aluminium Alloy Variable Diameter Tubes Hydroforming Process Based on Machine Learning by Yong Xu, Xuewei Zhang, Wenlong Xie, Shihong Zhang, Yaqiang Tian, Liansheng Chen

“…The minimum wall thickness, maximum wall thickness, and maximum expansion height of the formed tubes are included in the main evaluation factors of the forming results. A variety of machine learning algorithms are used to predict, and the prediction results are compared with the finite element model in terms of error. …”
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An Air Traffic Controller Action Extraction-Prediction Model Using Machine Learning Approach by Duc-Thinh Pham, Sameer Alam, Vu Duong

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