Machine Learning for Long COVID Inference Based on the Acute Phase: A Case Study in Healthcare Professionals by Caio B. S. Maior, Sandrely P. Silva, Isis D. Lins, Ana Lisa Gomes, Marcio C. Moura

“…In addition to five ML (i.e., models such asRandom Forest, K-Nearest Neighbors, Logistic Regression, Support Vector Machine, and Multilayer Perceptron), we applied dimensionality reduction techniques such as Principal Components Analysis, Linear Discriminant Analysis, and Feature Selection. …”
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Evaluating the impact of industrial wastes on the compressive strength of concrete using closed-form machine learning algorithms by Carlos Roberto López Paredes, Cesar García, Kennedy C. Onyelowe, Kennedy C. Onyelowe, Maria Gabriela Zuniga Rodriguez, Tammineni Gnananandarao, Alexis Ivan Andrade Valle, Nancy Velasco, Greys Carolina Herrera Morales

“…In this research investigation, the impact of wastes from the industry on the compressive strength of concrete incorporating fly ash (FA) and silica fume (SF) as additional components alongside traditional concrete mixes has been studied through the application of machine learning (ML). A green concrete database comprising 330 concrete mix data points has been collected and modelled to estimate the unconfined compressive strength behaviour. …”
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Real‐Time Self‐Optimization of Quantum Dot Laser Emissions During Machine Learning‐Assisted Epitaxy by Chao Shen, Wenkang Zhan, Shujie Pan, Hongyue Hao, Ning Zhuo, Kaiyao Xin, Hui Cong, Chi Xu, Bo Xu, Tien Khee Ng, Siming Chen, Chunlai Xue, Zhanguo Wang, Chao Zhao

“…In this work, in situ reflection high‐energy electron diffraction (RHEED) is integrated with machine learning (ML) to correlate the surface reconstruction with the photoluminescence (PL) of InAs/GaAs quantum dots (QDs), which serve as the active region of lasers. …”
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Synergy of machine learning and the Einstein Choquet integral with LOPCOW and fuzzy measures for sustainable solid waste management by Yasir Yasin, Muhammad Riaz, Kholood Alsager

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Data-Driven Fault Detection and Diagnosis in Cooling Units Using Sensor-Based Machine Learning Classification by Amilcar Quispe-Astorga, Roger Jesus Coaquira-Castillo, L. Walter Utrilla Mego, Julio Cesar Herrera-Levano, Yesenia Concha-Ramos, Erwin J. Sacoto-Cabrera, Edison Moreno-Cardenas

“…This research is based on data-driven models and machine learning, where a specific strategy is proposed for five types of system failures. …”
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Prediction of injuries in elite soccer players with the analysis of asymmetries in the CMJ through the use of Machine Learning tools by Ángel Aceña Rodríguez, Alvaro Vita, Raul Quintana, Carlos Reyes, Aitor Abal, Pol Corpas, Luis Vita, David Agusti, Enrique Portaz

“…Methodology: Through the use of force platforms (ForceDecks, Valdperformance) and 4 machine learning models, data from 29 Asian Football Confederation (AFC) Champions League elite level professional soccer players were analyzed during a regular season (with a total of 1265 jumps analyzed, during the days Match Day Training MD+1, MD+2 and MD-1). …”
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Prediction of Metabolic Parameters of Diabetic Patients Depending on Body Weight Variation Using Machine Learning Techniques by Oana Vîrgolici, Daniela Lixandru, Andrada Mihai, Diana Simona Ștefan, Cristian Guja, Horia Vîrgolici, Bogdana Virgolici

“…<b>Methods</b>: The dataset includes medical records from patients in Bucharest hospitals, collected between 2012 and 2016. Several machine learning models, namely linear regression, polynomial regression, Gradient Boosting, and Extreme Gradient Boosting, were employed to predict changes in medical parameters as a function of body weight variation. …”
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Advanced In Vitro Models for Preclinical Drug Safety: Recent Progress and Prospects by Dileep G. Nair, Ralf Weiskirchen

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Study of Recent Image Restoration Techniques: A Comprehensive Survey by Nikita Singhal, Anup Kadam, Pravesh Kumar, Hritik Singh, Aaryan Thakur, Pranay

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Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics by Leonidas Theodorakopoulos, Aristeidis Karras, George A. Krimpas

“…In this study, we analyze the performance of the machine learning operators in Apache Spark MLlib for K-Means, Random Forest Regression, and Word2Vec. …”
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How artificial intelligence reduces human bias in diagnostics? by Artur Luczak

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DNS over HTTPS Tunneling Detection System Based on Selected Features via Ant Colony Optimization by Hardi Sabah Talabani, Zrar Khalid Abdul, Hardi Mohammed Mohammed Saleh

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Environmental regulations and eco-innovation as catalysts for green agricultural practices: insights from Pakistan–China agricultural cooperation by Ali Raza, Lu Hongliang, Zhu Yue, Tingyu Yang, Nian Wei

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Semi-Supervised Learning of Statistical Models for Natural Language Understanding by Deyu Zhou, Yulan He

“…The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). …”
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Unsupervised learning analysis on the proteomes of Zika virus by Edgar E. Lara-Ramírez, Gildardo Rivera, Amanda Alejandra Oliva-Hernández, Virgilio Bocanegra-Garcia, Jesús Adrián López, Xianwu Guo

“…Molecular epidemiology, supported by clustering phylogenetic gold standard studies using sequence data, has provided valuable information for tracking and controlling the spread of ZIKV. Unsupervised learning (UL), a form of machine learning algorithm, can be applied on the datasets without the need of known information for training. …”
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Assessment and Modeling of Green Roof System Hydrological Effectiveness in Runoff Control: A Case Study in Dublin by Mehdi Gholamnia, Payam Sajadi, Salman Khan, Srikanta Sannigrahi, Saman Ghaffarian, Himan Shahabi, Francesco Pilla

“…The comprehensive dataset enabled detailed modeling of runoff hydrograph parameters using rainfall hyetographs, which were subsequently analyzed through sophisticated machine learning algorithms. …”
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Economic growth of countries in the context of military operations by Olexandr Shapurov, Oleksii Hrechanyi, Volodymyr Stoiev, Anatolii Karpelianskyi, Alina Sosnovska

“…Key factors include international aid (29.8%), investments (24.6%), and conflict reduction (19.7%). Theoretical Implications. The study adapts growth models to wartime conditions, highlighting the advantages of endogenous models and machine learning for analyzing complex economies. …”
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Optimizing flood resilience in China’s mountainous areas: Design flood estimation using advanced machine learning techniques by Xuemei Wang, Ronghua Liu, Chaoxing Sun, Xiaoyan Zhai, Liuqian Ding, Xiao Liu, Xiaolei Zhang

“…Study region: China Study focus: We developed machine learning (ML) models for design flood estimation in mountainous catchments (≤ 500 km²) across China. …”
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Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity by Caden P. Chamberlain, Garrett W. Meigs, Derek J. Churchill, Jonathan T. Kane, Astrid Sanna, James S. Begley, Susan J. Prichard, Maureen C. Kennedy, Craig Bienz, Ryan D. Haugo, Annie C. Smith, Van R. Kane, C. Alina Cansler

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Comparison of machine learning and validation methods for high-dimensional accelerometer data to detect foot lesions in dairy cattle. by Muhammad Usman Riaz, Luke O'Grady, Conor G McAloon, Finnian Logan, Isobel Claire Gormley

“…Analyzing accelerometer data is challenging due to its wide, high-dimensional structure as it has many features and typically much fewer animals or samples, reducing the utility of many machine learning (ML) models and increasing the risk of overfitting. …”
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