Sonographic machine-assisted recognition and tracking of B-lines in dogs: the SMARTDOG study by Aurélie Jourdan, Aurélie Jourdan, Caroline Dania, Caroline Dania, Maxime Cambournac, Maxime Cambournac

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Machine learning models for predicting multimorbidity trajectories in middle-aged and elderly adults by Li Yao, Qiaoxing Li, Zihan Zhou, Jiajia Yin, Tingrui Wang, Yan Liu, Qinqin Li, Lu Xiao, Dongliang Yang

“…Then, predictive models based on machine learning techniques were developed to forecast the progression of different trajectories and identify key risk factors. …”
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Metering Automation System 3.0 Base Version Based on Machine Learning by Sheng Li, Leping Zhang, Hang Dai, Lukun Zeng, Yuan Ai, Shuang Qi, Yuanzhai Cui

“…However, traditional machine learning methods and standalone deep learning architectures struggle to balance spatiotemporal feature extraction, computational efficiency, and deployment constraints for high-frequency multivariate metering data. …”
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Does machine learning outperform logistic regression in predicting individual tree mortality? by Aitor Vázquez-Veloso, Astor Toraño Caicoya, Felipe Bravo, Peter Biber, Enno Uhl, Hans Pretzsch

“…However, innovative classification algorithms can go deep into data to find patterns that can model or even explain their relationship. …”
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A statistical and machine learning approach for monthly precipitation forecasting in an Amazon city by Ewerton Cristhian Lima de Oliveira, Eduardo Costa de Carvalho, Edmir dos Santos Jesus, Rafael de Lima Rocha, Rafael de Lima Rocha, Helder Moreira Arruda, Ronnie Cley de Oliveira Alves, Ronnie Cley de Oliveira Alves, Renata Gonçalves Tedeschi

“…Additionally, we use meteorological data from a set of sensors installed at a meteorological station located in Belém to train multivariate statistical and machine learning (ML) models to predict precipitation. …”
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Recent advances in machine learning for defects detection and prediction in laser cladding process by X.C. Ji, R.S. Chen, C.X. Lu, J. Zhou, M.Q. Zhang, T. Zhang, H.L. Yu, Y.L. Yin, P.J. Shi, W. Zhang

“…By employing algorithms to analyze data, discern patterns and regularities, rendering predictions and decisions, machine learning has significantly influenced various aspects of laser cladding processes. …”
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Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis by Brahim Jabir, Bendaoud Nadif, Isabel De la Torre Diez, Helena Garay, Irene Delgado Noya

“…Our research dives into the performance comparison of two popular machine learning approaches: the support vector machine (SVM) and the more recent deep learning-based stacked autoencoder (SAE). …”
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GENDER-SPECIFIC PREDICTORS OF VAULT PERFORMANCE IN GYMNASTICS: A MACHINE LEARNING APPROACH by Dušan Đorđević, Janez Vodičar, Robi Kreft, Edvard Kolar, Miloš Paunović, Saša Veličković, Miha Marinšek

“… This study investigated gender-specific predictors of vault performance in gymnastics by applying machine learning techniques to analyse body composition and run-up dynamics. …”
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Multi-scale machine learning model predicts muscle and functional disease progression by Silvia S. Blemker, Lara Riem, Olivia DuCharme, Megan Pinette, Kathryn Eve Costanzo, Emma Weatherley, Jeff Statland, Stephen J. Tapscott, Leo H. Wang, Dennis W. W. Shaw, Xing Song, Doris Leung, Seth D. Friedman

“…This study introduces a multi-scale machine learning framework leveraging whole-body magnetic resonance imaging (MRI) and clinical data to predict regional, muscle, joint, and functional progression in FSHD. …”
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Using machine learning to identify Parkinson’s disease severity subtypes with multimodal data by Hwayoung Park, Changhong Youm, Sang-Myung Cheon, Bohyun Kim, Hyejin Choi, Juseon Hwang, Minsoo Kim

“…Results We identified three PD severity subtypes, each exhibiting different patterns of clinical severity, with the severity increasing as it progressed from clusters 1 to 3. …”
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An integrated machine learning and fractional calculus approach to predicting diabetes risk in women by David Amilo, Khadijeh Sadri, Evren Hincal, Muhammad Farman, Kottakkaran Sooppy Nisar, Mohamed Hafez

“…We employ seven machine learning algorithms: Decision Tree, Logistic Regression, Support Vector Machine (SVM), Random Forest, Bagged Trees, Naive Bayes, and XGBoost, to identify key risk factors, with XGBoost demonstrating higher performance. …”
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Enhancing Antidiabetic Drug Selection Using Transformers: Machine-Learning Model Development by Hisashi Kurasawa, Kayo Waki, Tomohisa Seki, Eri Nakahara, Akinori Fujino, Nagisa Shiomi, Hiroshi Nakashima, Kazuhiko Ohe

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Integration of multimodal imaging data with machine learning for improved diagnosis and prognosis in neuroimaging by Saurabh Bhattacharya, Sashikanta Prusty, Sanjay P. Pande, Monali Gulhane, Santosh H. Lavate, Nitin Rakesh, Saravanan Veerasamy

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A Synergy Between Machine Learning and Formal Concept Analysis for Crowd Detection by Anas M. Al-Oraiqat, Oleksandr Drieiev, Sattam Almatarneh, Mohammadnoor Injadat, Karim A. Al-Oraiqat, Hanna Drieieva, Yassin M. Y. Hasan

“…Recent systems take advantage of the synergy between machine learning, data mining, and image processing to extract/analyze features from crowded zones and recognize patterns and anomalies from the crowd behavior. …”
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Hybrid Machine Learning Model for Efficient Botnet Attack Detection in IoT Environment by Mudasir Ali, Mobeen Shahroz, Muhammad Faheem Mushtaq, Sultan Alfarhood, Mejdl Safran, Imran Ashraf

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Transplantation of Patients with Hepatocellular Carcinoma Through Increased Utilization of Machine Perfusion Technology by Lauren E. Matevish, MD, Jason Guo, BS, Andrew D. Shubin, MD, PhD, Malcolm MacConmara, MD, Christine S. Hwang, MD, Nathanael Raschzok, MD, Nicole E. Rich, MD, MSCS, Arjmand R. Mufti, MD, Amit G. Singal, MD, MS, Parsia A. Vagefi, MD, Madhukar S. Patel, MD, MBA, ScM

“…To understand current use for patients with HCC, we examined liver transplant patterns with MP and the characteristics of patients with HCC receiving an MP liver. …”
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Exploring the potential of machine learning in gastric cancer: prognostic biomarkers, subtyping, and stratification by Haniyeh Rafiepoor, Mohammad M. Banoei, Alireza Ghorbankhanloo, Ahad Muhammadnejad, Amirhossein Razavirad, Saeed Soleymanjahi, Saeid Amanpour

“…Correlation analysis revealed different patterns of prognostic markers in the non-survivor and survivor cohorts and different GC subtypes. …”
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Application of machine learning in early childhood development research: a scoping review by Akbar K Waljee, Amina Abubakar, Patrick N Mwangala, Faith Neema Benson, Daisy Chelangat, Willie Brink, Cheryl A Moyer

“…Artificial intelligence techniques, particularly machine learning (ML), offer an innovative approach by analysing complex datasets to detect subtle developmental patterns.Objective To map the existing literature on the use of ML in ECD research, including its geographical distribution, to identify research gaps and inform future directions. …”
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Modulation pattern recognition method of wireless communication automatic system based on IABLN algorithm in intelligent system. by Ting Xie, Xing Han

“…The aim of this study is to address the limitations of convolutional networks in recognizing modulation patterns. These networks are unable to utilize temporal information effectively for feature extraction and modulation pattern recognition, resulting in inefficient modulation pattern recognition. …”
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The Impact of Various Filling Patterns and Building Orientations on the Mechanical Characteristics and Building Time of PLA Using FDM by M. Hamoud, Sachin Salunkhe, Lenka Cepova, H. M. A. Hussien

“…The results show that the filling pattern, orientation, and density at which the part is built significantly affect the strength, hardness, and building time. …”
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