Predicting hospital outpatient volume using XGBoost: a machine learning approach by Lingling Zhou, Qin Zhu, Qian Chen, Ping Wang, Hao Huang

“…Accurate prediction of outpatient demand can significantly enhance operational efficiency and optimize the allocation of medical resources. This study aims to develop a predictive model for daily hospital outpatient volume using the XGBoost algorithm. …”
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Connecting metal-organic framework synthesis to applications using multimodal machine learning by Sartaaj Takrim Khan, Seyed Mohamad Moosavi

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Predicting the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms by Haobo Kong, Yong Li, Ya Shen, Jingjing Pan, Min Liang, Zhi Geng, Yanbei Zhang

“…Abstract Background This study aimed to develop predictive models with robust generalization capabilities for assessing the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms. …”
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Day-Ahead Energy Price Forecasting with Machine Learning: Role of Endogenous Predictors by Chibuike Chiedozie Ibebuchi

“…Accurate Day-Ahead Energy Price (DAEP) forecasting is essential for optimizing energy market operations. This study introduces a machine learning framework to predict the DAEP with a 24 h lead time, leveraging historical data and forecasts available at the prediction time. …”
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Predicting Ship Waiting Times Using Machine Learning for Enhanced Port Operations by Min-Hwa Choi, Woongchang Yoon

“…By using a dataset of 121,401 voyage records, we evaluated nine regression models, including conventional, ensemble-based, and deep learning models. …”
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Stochastic Machine Scheduling to Minimize Waiting Time Related Objectives with Emergency Jobs by Lianmin Zhang, Lei Guan, Ke Zhou

“…All jobs have random processing times and should be completed on a single machine. The most common case of the model is the surgery scheduling problem, where some elective surgeries are to be arranged in an operation room when emergency cases are coming during the operating procedure of the elective surgeries. …”
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The use of machine learning methods in the development of nasal dosage forms with cerebroprotective action by B. S. Burlaka, I. F. Bielenichev

“…The use of machine learning models in pharmaceutical development will contribute to resource conservation and optimization of the composition of the formulation.…”
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Estimation of Mango Fruit Production Using Image Analysis and Machine Learning Algorithms by Liliana Arcila-Diaz, Heber I. Mejia-Cabrera, Juan Arcila-Diaz

“…This significant increase in dataset size notably enhances the robustness and generalization capacity of the model. The YOLO-trained model achieves an accuracy of 96.72%, a recall of 77.4%, and an F1 Score of 86%, compared to the results of Faster R-CNN, which are 98.57%, 63.80%, and 77.46%, respectively. …”
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Exploring nontoxic perovskite materials for perovskite solar cells using machine learning by W. G. A. Pabasara, H. A. H. M. Wijerathne, M. G. M. M. Karunarathne, D. M. C. Sandaru, Pradeep K. W. Abeygunawardhana, Galhenage A. Sewvandi

“…A highly accurate machine learning model was developed to predict Goldschmidt factor and the band gap, aiming to discover lead-free perovskites. …”
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Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics by Ariane Khaledi, Aaron Weimann, Monika Schniederjans, Ehsaneddin Asgari, Tzu‐Hao Kuo, Antonio Oliver, Gabriel Cabot, Axel Kola, Petra Gastmeier, Michael Hogardt, Daniel Jonas, Mohammad RK Mofrad, Andreas Bremges, Alice C McHardy, Susanne Häussler

“…In this study, we sequenced the genomes and transcriptomes of 414 drug‐resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and expression profiles, we generated predictive models and identified biomarkers of resistance to four commonly administered antimicrobial drugs. …”
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Risk stratification in neuroblastoma patients through machine learning in the multicenter PRIMAGE cohort by Jose Lozano-Montoya, Ana Jimenez-Pastor, Almudena Fuster-Matanzo, Glen J. Weiss, Leonor Cerda-Alberich, Diana Veiga-Canuto, Blanca Martínez-de-Las-Heras, Blanca Martínez-de-Las-Heras, Adela Cañete-Nieto, Sabine Taschner-Mandl, Barbara Hero, Thorsten Simon, Ruth Ladenstein, Luis Marti-Bonmati, Luis Marti-Bonmati, Angel Alberich-Bayarri

“…In response, this investigation developed a machine learning model using clinical, molecular, and magnetic resonance (MR) radiomics features at diagnosis to predict patient’s overall survival (OS) and improve their risk stratification.MethodsPRIMAGE database, including 513 patients (discovery cohort), was used for model training, validation, and testing. …”
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Predicting Coronary Heart Disease Using Data Mining and Machine Learning Solutions by VIJAI M. MOORTHY, BHUPAL N. DHARAMSOTH, VIJAYALAKSHMI MUTHUKARUPPAN, ARUL ELANGO, KALAIARASI GANESAN

“…The authors developed a novel ensemble learning model, combining Linear Regression, Random Forest, and Gradient Boosting algorithms, optimized using Bayesian hyperparameter tuning. …”
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A Comparison of Approaches for Handling Concept Drifts in Data Processed With Machine Learning by Emanuel Valerio Pereira, Wendley Souza da Silva

“…In addition to shedding light on the behavior of machine learning models under concept drift, the findings empower practitioners and researchers to make informed decisions to optimize model robustness.…”
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Machine Translation Performance for Low-Resource Languages: A Systematic Literature Review by Taofik O. Tafa, Siti Zaiton Mohd Hashim, Mohd Shahizan Othman, Hitham Alhussian, Maged Nasser, Said Jadid Abdulkadir, Sharin Hazlin Huspi, Sarafa O. Adeyemo, Yunusa Adamu Bena

“…Machine translation (MT) for low-resource languages continues to face significant challenges because of limited digital resources and parallel corpora, despite remarkable developments in neural machine translation (NMT). …”
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Gold nanobiosensors and Machine Learning: Pioneering breakthroughs in precision breast cancer detection by Soheil Sadr, Ashkan Hajjafari, Abbas Rahdar, Sadanand Pandey, Parian Poorjafari Jafroodi, Narges Lotfalizadeh, Mahdi Soroushianfar, Shahla Salimpour Kavasebi, Zelal Kharaba, Sonia Fathi-karkan, Hassan Borji

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Research on Atlantic surface pCO2 reconstruction based on machine learning by Jiaming Liu, Jie Wang, Xun Wang, Yixuan Zhou, Runbin Hu, Haiyang Zhang

“…These are followed by TP, latitude, longitude, SHWW, U10, and E. (2) After comprehensive data testing, the six machine learning models select the optimal hyperparameters for reconstruction. …”
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Interpretable Machine Learning for Explaining and Predicting Collapse Hazards in the Changbai Mountain Region by Xiangyang He, Qiuling Lang, Jiquan Zhang, Yichen Zhang, Qingze Jin, Jinyuan Xu

“…Model performance is evaluated on a test set by several statistical metrics, which shows that the optimized random forest model performs best and outperforms SVM, XGBoost, and LightGBM. …”
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A machine learning tool for identifying metastatic colorectal cancer in primary care by Eliya Abedi, Marcela Ewing, Elinor Nemlander, Jan Hasselström, Annika Sjövall, Axel C. Carlsson, Andreas Rosenblad

“…Risks of having MCRC were calculated using odds ratios of marginal effects (ORME).Results The optimal model included 76 variables with non-zero influence, had an area under the curve of 76.5%, a sensitivity of 77.8%, and a specificity of 69.2%. …”
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Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related Concussion by Anthony J. Maxin, Bridget M. Whelan, Michael R. Levitt, Lynn B. McGrath, Kimberly G. Harmon

“…All combinations of the seven PLR parameters were tested in machine learning binary classification models to determine the optimal combination for differentiating between non-concussed and concussed athletes. …”
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Machine Learning and Deep Learning for Crop Disease Diagnosis: Performance Analysis and Review by Habiba Njeri Ngugi, Andronicus A. Akinyelu, Absalom E. Ezugwu

“…This paper presents a review of machine learning (ML) and deep learning (DL) techniques for crop disease diagnosis, focusing on Support Vector Machines (SVMs), Random Forest (RF), k-Nearest Neighbors (KNNs), and deep models like VGG16, ResNet50, and DenseNet121. …”
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