Performance evaluation and comparative analysis of different machine learning algorithms in predicting postnatal care utilization: Evidence from the ethiopian demographic and health survey 2016. by Daniel Niguse Mamo, Agmasie Damtew Walle, Eden Ketema Woldekidan, Jibril Bashir Adem, Yosef Haile Gebremariam, Meron Asmamaw Alemayehu, Ermias Bekele Enyew, Shimels Derso Kebede

“…This research aims to evaluate and compare the effectiveness of machine learning algorithms in predicting postnatal care utilization in Ethiopia and to identify the key factors involved. …”
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Diagnostic accuracy of artificial intelligence algorithms to predict remove all macroscopic disease and survival rate after complete surgical cytoreduction in patients with ovarian cancer: a systematic review and meta-analysis by Somayyeh Noei Teymoordash, Hoda Zendehdel, Ali Reza Norouzi, Mahdis Kashian

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Machine Learning-Based Lithium Battery State of Health Prediction Research by Kun Li, Xinling Chen

“…To address the problem of predicting the state of health (SOH) of lithium-ion batteries, this study develops three models optimized using the particle swarm optimization (PSO) algorithm, including the long short-term memory (LSTM) network, convolutional neural network (CNN), and support vector regression (SVR), for accurate SOH estimation. …”
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Intelligent Photolithography Corrections Using Dimensionality Reductions by Parag Parashar, Chandni Akbar, Tejender S. Rawat, Sparsh Pratik, Rajat Butola, Shih H. Chen, Yung-Sung Chang, Sirapop Nuannimnoi, Albert S. Lin

“…In this work, we use dimensionality reduction (DR) algorithms to reduce the computation time of complex OPC/EPC problems while the prediction accuracy is maintained. …”
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Short-Term Wind Power Prediction Model Based on PSO-CNN-LSTM by Qingquan Lv, Jialin Zhang, Jianmei Zhang, Zhenzhen Zhang, Qiang Zhou, Pengfei Gao, Haozhe Zhang

“…The predictive performance of the proposed PSO-CNN-LSTM hybrid algorithm is evaluated against benchmark models using four statistical metrics: RMSE, MAE, MSE, and R². …”
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Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction by Jingjing Chen, Dan Zhang, Chengxiu Zhu, Lin Lin, Kejun Ye, Ying Hua, Mengjia Peng

“…Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”
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Apply Ridge Regression Model to Predict the Lateral Velocity Difference of Tight Reservoirs by HAN Longfei, ZHANG Yongfei, WANG Miaomiao, LI Yu

“…Finally, a ridge regression algorithm is used to establish a prediction model of the lateral wave time lag based on the logging data of five wells in WQ block. …”
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Development and validation of a prediction model for coronary heart disease risk in depressed patients aged 20 years and older using machine learning algorithms by Yicheng Wang, Yicheng Wang, Yicheng Wang, Chuan-Yang Wu, Hui-Xian Fu, Jian-Cheng Zhang, Jian-Cheng Zhang, Jian-Cheng Zhang

“…Several evaluation metrics were employed to assess and compare the performance of eight different machine learning models, aiming to identify the most effective algorithm for predicting coronary heart disease risk in individuals with depression. …”
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Multiscale Feature Modeling and Interpretability Analysis of the SHAP Method for Predicting the Lifespan of Landslide Dams by Zhengze Huang, Yuqi Bai, Hengyu Liu, Yun Lin

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Prediction of zero-dose children using supervised machine learning algorithm in Tanzania: evidence from the recent 2022 Tanzania Demographic and Health Survey by Beminate Lemma Seifu, Angwach Abrham Asnake, Alemayehu Kasu Gebrehana

“…Maternal unemployment had the most significant positive impact (+0.060) on predicting zero-dose children. Lack of maternal education was the second most significant positive factor (+0.048), indicating that mothers without formal education are more likely to have zero-dose children. …”
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A new algorithm to assess the risk of malignancy in premenopausal patients with pelvic mass by M. S. Katsyuba, R. Sh. Khasanov, T. I. Madzhidov, G. Z. Muratova, G. A. Usmanova, A. F. Akhmetzyanova, A. Rakhimbekova, V. V. Terentyeva, G. R. Fakhrutdinova

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The reform of the teaching mode of aesthetic education for university students based on digital media technology by Yan Jiayue

Subjects: “…aesthetic education teaching…”
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Dimensionality Reduction by Weighted Connections between Neighborhoods by Fuding Xie, Yutao Fan, Ming Zhou

“…Dimensionality reduction is the transformation of high-dimensional data into a meaningful representation of reduced dimensionality. …”
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Predicting Rolling Element Bearings’ Deterioration Vibration Trend based on Limited Historical Data for a Desired Confidence Level using Machine Learning Algorithms by Mohammad Reza Seif, Somaye Mohammadi, Parham Rahimi, Mehdi Behzad

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Advancing flood susceptibility prediction: A comparative assessment and scalability analysis of machine learning algorithms via artificial intelligence in high‐risk regions of Pakistan by Mirza Waleed, Muhammad Sajjad

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Prediction of 5-year postoperative survival and analysis of key prognostic factors in stage III colorectal cancer patients using novel machine learning algorithms by Wei Zhang, Yan Li, Jinghan Jia, Yuhang Yang, Yuyuan Hu, Yanhong Wang, Jinxi Wang

“…ObjectiveThis study explores the predictive value of clinical and socio-demographic characteristics for postoperative survival in stage III colorectal cancer (CRC) patients and develops a 5-year postoperative survival prediction model using machine learning algorithms.MethodsData from 13,855 stage III CRC patients who underwent surgery were extracted from the SEER database. …”
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External validation of the QCovid 2 and 3 risk prediction algorithms for risk of COVID-19 hospitalisation and mortality in adults: a national cohort study in Scotland by Aziz Sheikh, Julia Hippisley-Cox, Chris Robertson, Holly Tibble, Colin R Simpson, Colin McCowan, Igor Rudan, Adeniyi Francis Fagbamigbe, Steven Kerr, Tristan Millington, Karen Jeffrey

“…Objective The QCovid 2 and 3 algorithms are risk prediction tools developed during the second wave of the COVID-19 pandemic that can be used to predict the risk of COVID-19 hospitalisation and mortality, taking vaccination status into account. …”
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Predictive Maintenance Algorithms, Artificial Intelligence Digital Twin Technologies, and Internet of Robotic Things in Big Data-Driven Industry 4.0 Manufacturing Systems by Marek Nagy, Marcel Figura, Katarina Valaskova, George Lăzăroiu

“…In Industry 4.0, predictive maintenance (PdM) is key to optimising production processes. …”
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Enhanced data-driven shear strength predictive modeling framework for RCDBs using explainable boosting-based ensemble learning algorithms coupled with Bayesian optimization by Imad Shakir Abbood, Noorhazlinda Abd Rahman, B.H. Abu Bakar

“…Despite over 70 years of investigation into the behavior of reinforced concrete deep beams (RCDBs), it remains challenging to accurately predict their shear strength (SS) due to the underlying intricate mechanism. …”
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Prediction of carbon dioxide emissions from Atlantic Canadian potato fields using advanced hybridized machine learning algorithms – Nexus of field data and modelling by Muhammad Hassan, Khabat Khosravi, Aitazaz A. Farooque, Travis J. Esau, Alaba Boluwade, Rehan Sadiq

“…In this study, three novel machine learning algorithms of additive regression-random forest (AR-RF), Iterative Classifier Optimizer (ICO-AR-RF), and multi-scheme (MS-RF) were explored for carbon dioxide (CO2) flux rate prediction from three agricultural fields. …”
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