Application of Machine Learning Models for the Early Detection of Metritis in Dairy Cows Based on Physiological, Behavioural and Milk Quality Indicators by Karina Džermeikaitė, Justina Krištolaitytė, Ramūnas Antanaitis

“…In this study, we investigated the potential of machine learning (ML) techniques applied to physiological, behavioural, and milk quality parameters for the early detection of metritis in dairy cows during the postpartum period. …”
Get full text

Timeseries Fault Classification in Power Transmission Lines by Non-Intrusive Feature Extraction and Selection Using Supervised Machine Learning by Rab Nawaz, Hani A. Albalawi, Syed Basit Ali Bukhari, Khawaja Khalid Mehmood, Muhammad Sajid

“…This paper presents a supervised machine learning approach using eight popular classifiers for fault classification in power transmission lines. …”
Get full text

Durability prediction of sustainable marine concrete under freeze-thaw cycles using multi-objective machine learning models by Aïssa Rezzoug, Ali H. AlAteah, Sadiq Alinsaif, Sahar A. Mostafa

“…This study introduces machine learning (ML)-based algorithms for forecasting FTC-induced durability of concrete while simultaneously predicting sustainability metrics such as CO₂ emissions and production cost. …”
Get full text

Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining by Yewuhalashet Fissha, Prashanth Ragam, Hajime Ikeda, N. Kushal Kumar, Tsuyoshi Adachi, P.S. Paul, Youhei Kawamura

“…Therefore, this study employs five machine learning models for predicting the PPV and frequency resulting from quarry blasting. …”
Get full text

Smart and accurate: A new tool to identify stressed soybean seeds based on multispectral images and machine learning models by Ana Carolina Picinini Petronilio, Clíssia Barboza Mastrangelo, Thiago Barbosa Batista, Gustavo Roberto Fonseca de Oliveira, Isabela Lopes dos Santos, Edvaldo Aparecido Amaral da Silva

“…We designed machine learning models using multispectral imaging data based on three algorithms: neural network, support vector machine, and random forest. …”
Get full text

A machine learning model accurately identifies glycogen storage disease Ia patients based on plasma acylcarnitine profiles by Joost Groen, Bas M. de Haan, Ruben J. Overduin, Andrea B. Haijer-Schreuder, Terry GJ Derks, M. Rebecca Heiner-Fokkema

“…The model demonstrated high sensitivity and specificity, with selected parameters that were not only robust but also highly interpretable. …”
Get full text

Improved mapping of highland bamboo forests using Sentinel-2 time series and machine learning in Google Earth Engine by Dagnew Yebeyen, Binyam Tesfaw Hailu, Worku Zewdie, Temesgen Abera, Gudeta W. Sileshi, Melaku Getachew, Sileshi Nemomissa

“…Here, we show that image compositing and multiple input parameters using machine learning techniques can overcome challenges facing land cover classification, which can hinder accurate mapping of Afromontane forests. …”
Get full text

Energy intensity of water supply by pumping stations during reconstruction of irrigated sites using low-pressure sprinkling machines by Nikolay F. Ryzhko, Fyrid K. Abdrazakov, Sergey N. Ryzhko, Evgeny S. Smirnov, Evgeny A. Shishenin, Sergey A. Horin

“…Calculations using an improved method for assessing the energy intensity of irrigation will allow you to select the optimal parameters of pumps, sprinkler machines and pipes of a closed irrigation network. …”
Get full text

Helium retention feature in the boron deposited layer on tungsten substrate by laser-induced breakdown spectroscopy and machine learning approach by Muhammad Amir Shabbir, Ran Hai, Zhonglin He, Zehua Liu, Fahad Rehman, Xue Bai, Jianping Mu, Ding Wu, Cong Li, Hongbin Ding

“…Machine learning (ML) algorithms are used to classify the co-deposits and substrates. …”
Get full text

Development and multi-cohort validation of a machine learning-based simplified frailty assessment tool for clinical risk prediction by Jiahui Lai, Cailian Cheng, Tiantian Liang, Leile Tang, Xinhua Guo, Xun Liu

“…This study aimed to develop and validate a clinically feasible, machine learning-based frailty assessment tool that balances predictive accuracy with implementation simplicity in real-world clinical settings. …”
Get full text

Mapping Nationwide Subfield Division Dynamics in Saudi Arabia Using Temporal Patterns of Sentinel-2 NDVI and Machine Learning by Ting Li, Oliver Miguel Lopez Valencia, Matthew F. McCabe

“…Object-based analysis is widely used for extracting information from satellite data using machine learning, offering reduced sensitivity to fine-scale variability, noise, and computational cost compared to pixel-based methods. …”
Get full text

A retrospective study using machine learning to develop predictive model to identify rotavirus-associated acute gastroenteritis in children by Sourav Paul, Minhazur Rahman, Anutee Dolley, Kasturi Saikia, Chongtham Shyamsunder Singh, Arifullah Mohammed, Ghazala Muteeb, Rosy Sarmah, Nima D. Namsa

“…Objectives This study aims to develop a machine-learning predictive model integrated with multiple sources of clinical parameters specific to rotavirus infection without relying on laboratory tests. …”
Get full text

Machine Learning Framework for Early Detection of Chronic Kidney Disease Stages Using Optimized Estimated Glomerular Filtration Rate by Samit Kumar Ghosh, Namareq Widatalla, Ahsan H. Khandoker

“…Additionally, it will incorporate extra clinical parameters, including biomarkers and genetic data, to enhance the precision of CKD risk prediction. …”
Get full text

Using explainable machine learning and eye-tracking for diagnosing autism spectrum and developmental language disorders in social attention tasks by Adoración Antolí, Adoración Antolí, Francisco Javier Rodriguez-Lozano, José Juan Cañas, Julia Vacas, Julia Vacas, Fátima Cuadrado, Fátima Cuadrado, Araceli Sánchez-Raya, Araceli Sánchez-Raya, Carolina Pérez-Dueñas, Carolina Pérez-Dueñas, Juan Carlos Gámez-Granados

“…They also underscore the clinical relevance of such approaches for identifying the variables and parameters that differentiate between disorders.…”
Get full text

Prevalence and clinical characteristics of patients with rheumatoid arthritis with interstitial lung disease using unstructured healthcare data and machine learning by Raul Castellanos-Moreira, Diego Benavent, Alejandra López Robles, Ernesto Trallero-Araguás, Lucía Silva-Fernández, Juliana Restrepo, Jose A Román Ivorra, Maria Lopez Lasanta, Laura Cebrián, Leticia Lojo, Belén López-Muñíz, Julia Fernández-Melon, Belén Núñez, Raúl Veiga Cabello, Pilar Ahijado, Isabel De la Morena Barrio, Nerea Costas Torrijo, Belén Safont, Enrique Ornilla, Arantxa Campo, Jose L Andreu, Elvira Díez, Elena Bollo, David Vilanova, Sara Luján Valdés

Get full text

Predicting Postoperative Blood Transfusion in Elderly Patients Undergoing Total Hip and Knee Arthroplasty Using Machine Learning Models by Liang D, Pang Y, Huang J, Che X, Zhou R, Ding X, Wang C, Zhao L, Han Y, Rong X, Li P

“…Five models—Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB)—were developed and validated. …”
Get full text

NitroNet – a machine learning model for the prediction of tropospheric NO₂ profiles from TROPOMI observations by L. Kuhn, L. Kuhn, S. Beirle, S. Osipov, S. Osipov, A. Pozzer, T. Wagner, T. Wagner

Get full text

Optimization of CNC End Milling Process Parameters of Low-Carbon Mold Steel Using Response Surface Methodology and Grey Relational Analysis by R. Suresh Kumar, S. Senthil Kumar, K. Murugan, B. Guruprasad, Sreekanth Manavalla, S. Madhu, M. Hariprabhu, S. Balamuralitharan, S. Venkatesa Prabhu

“…This is achieved by a proper understanding of every minute influence of parameters on the entire process. Understanding the influencing parameters in determining the machining process efficacy is inevitable. …”
Get full text

INFLUENCE OF THE STEEL CHEMICAL COMPOSITION, STATE OF THE EQUIPMENT OF MNLZ AND TECHNOLOGICAL PARAMETERS OF CASTING ON APPEARANCE OF SURFACE AND INTERNAL DEFECTS OF CONTINUOUSLY CA... by M. E. Kontanistov, S. V. Terletsky, V. A. Shatilo

Get full text

Theoretical research of some parameters of contact area of wheel cutting surface and workpiece at flat face grinding with preliminary inclination of spindle axis by I. N. Pyzhov, V. G. Klimenko

“…It guarantees that on the machined surface there will be no areas unaffected by the wheel. …”
Get full text