Estimation of the air conditioning energy consumption of a classroom using machine learning in a tropical climate by Liliana Ortega-Diaz, Julian Jaramillo-Ibarra, German Osma-Pinto

“…Machine learning is the most widely used approach for prediction due to its speed, accuracy, and non-linear modeling. …”
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Machine Learning-Based Seismic Response Prediction for Nuclear Power Plant Structures Considering Aging Deterioration by Hyunsu Kim, Soyeon Lee, Junsu Jang, Sihyeon An

“…Given that aging deterioration significantly influences the structural behavior of reinforced concrete (RC) nuclear power plant (NPP) structures, it is crucial to incorporate changes in the material properties of NPPs for accurate prediction of seismic responses. In this study, machine learning (ML) models for predicting the seismic response of RC NPP structures were developed by considering aging deterioration. …”
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Visual/Near-Infrared Spectroscopy Combined with Linear Discriminant Analysis and Machine Learning for Classification of Apple Damage by ZHANG Yu, ZHANG Chongyang, DUAN Xinxin, MA Shaoge, ZHAO Fu, WANG Juxia

“…The Vis-NIR spectral data of apples with different degrees of damage were collected, and the effect of different spectral preprocessing methods on the support vector machine (SVM) classification model was analyzed. LDA was used to reduce the dimensionality of the preprocessed spectral data, and five machine learning models including SVM, random forest (RF), K-nearest neighbor (KNN), decision tree (DT) and extreme gradient boosting (XGBoost) were constructed and compared for the classification of apple damage. …”
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Enhancing Supply Chain Agility and Sustainability through Machine Learning: Optimization Techniques for Logistics and Inventory Management by Vikram Pasupuleti, Bharadwaj Thuraka, Chandra Shikhi Kodete, Saiteja Malisetty

“…<b>Methods</b>: This study leverages advanced machine learning (ML) techniques to enhance logistics and inventory man-agement. …”
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Using machine learning and single nucleotide polymorphisms for improving rheumatoid arthritis risk Prediction in postmenopausal women. by Yingke Xu, Qing Wu

“…These findings emphasize the advantage of combining in-depth genomic data with advanced machine learning for RA risk prediction. The most robust performance of the XGBoost model, which integrated both conventional risk factors and individual SNPs, demonstrates its potential as a tool in personalized medicine for complex diseases like RA. …”
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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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Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy by Mahadee Al Mobin

“…This study introduces a rigorous multivariate time series analysis, integrating meteorological factors with state-of-the-art machine learning (ML) models, to predict DENV case trends across different temporal scales. …”
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Machine Learning Techniques for Predicting Typhoon‐Induced Storm Surge Using a Hybrid Wind Field by Changyu Su, Bishnupriya Sahoo, Miaohua Mao, Meng Xia

“…Since there have been limited typhoon‐induced storm surges in the Bohai Sea, an innovative prediction system is warranted to address frequent and intense typhoon‐induced impacts. Four Machine Learning (ML) models (Long Short‐Term Memory (LSTM), Convolutional Neural Networks (CNN), CNN‐LSTM, and ConvLSTM) were built to predict storm surges and significantly improve prediction when combined with a three‐dimensional Finite Volume Community Ocean Model (FVCOM), that is, FVCOM‐ML. …”
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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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BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs by Youngbin Kim, Kunlun Wang, Roberta I. Lock, Trevor R. Nash, Sharon Fleischer, Bryan Z. Wang, Barry M. Fine, Gordana Vunjak-Novakovic

“…We developed BeatProfiler, a suite of cardiac analysis tools designed to quantify contractile function, calcium handling, and force generation for multiple in vitro cardiac models and apply downstream machine learning methods for deep phenotyping and classification. …”
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Designing an explainable bio-inspired model for suspended sediment load estimation: eXtreme Gradient Boosting coupled with Marine Predators Algorithm by Roozbeh Moazenzadeh, Okan Mert Katipoğlu, Ahmadreza Shateri, Hamid Nasiri, Mohammed Abdallah

“…While Xtreme Gradient Boosting (XGB), a powerful ensemble machine learning (ML) model, has been employed in previous studies, the novelty of this research lies in the introduction of a hybrid approach that synergistically combines XGB with the bio-inspired Marine Predators Algorithm (XGB-MPA) to estimate SL in the Yeşilirmak River (Turkey). …”
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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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Increasing comprehensiveness and reducing workload in a systematic review of complex interventions using automated machine learning by Olalekan A Uthman, Rachel Court, Jodie Enderby, Lena Al-Khudairy, Chidozie Nduka, Hema Mistry, GJ Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

“…Background As part of our ongoing systematic review of complex interventions for the primary prevention of cardiovascular diseases, we have developed and evaluated automated machine-learning classifiers for title and abstract screening. …”
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Hybrid machine learning-enabled multivariate bridge-specific seismic vulnerability and resilience assessment of UHPC bridges by Tadesse G. Wakjira, M. Shahria Alam

“…Thus, this study proposes a hybrid machine learning (ML)-enabled multivariate bridge-specific seismic vulnerability and resilience assessment framework for UHPC bridges. …”
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A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data by M. Priyadharshini, B. Deevena Raju, A. Faritha Banu, P. Jagdish Kumar, V. Murugesh, Oleg Rybin

“…QProteoML was experimentally tested by comparing accuracy, F1 score and AUC ROC between classical machine learning models such as Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbors (KNN). …”
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A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings by Hafiz Muhammad Shakeel, Shamaila Iram, Richard Hill, Hafiz Muhammad Athar Farid, Akbar Sheikh-Akbari, Farrukh Saleem

“…Additionally, a customised machine learning interface was developed to visualise the multifaceted data analyses and model evaluations, promoting informed decision-making.…”
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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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A novel mechanism-guided residual network for accurate modelling of scroll expander under noisy and sparse data conditions by Xiaoshuang Lv, Xin Ma, Wei Peng, Ke Li, Chengdong Li

“…It has the ability to incorporate mechanistic constraints within the data-driven approach, setting it apart from conventional machine learning and deep learning methods that often disregard underlying physical laws. …”
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Simulation of Ground Visibility Based on Atmospheric Boundary Layer Data Using K-Nearest Neighbors and Ensemble Model Algorithms by Ruolan Liu, Shujie Yuan, Duanyang Liu, Lin Han, Fan Zu, Hong Wu, Hongbin Wang

“…This study introduces a machine learning approach for simulating visibility, utilizing the K-Nearest Neighbors algorithm and an ensemble model, which incorporate data from atmospheric boundary layer detection and conventional ground meteorological observations as simulation inputs. …”
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Spatiotemporal dynamics and key drivers of carbon emissions in regional construction sectors: Insights from a Random Forest Model by Zhonghan Yu, Qudsia Kanwal, Menghan Wang, Anissa Nurdiawati, Sami G. Al-Ghamdi

“…This research utilizes the Random Forest Model, a sophisticated machine learning method, to examine the determinants of carbon emissions in China's construction sector at the regional scale. …”
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