Machine Learning for Lung Cancer Subtype Classification: Combining Clinical, Histopathological, and Biophysical Features by Aiga Andrijanova, Lasma Bugovecka, Sergejs Isajevs, Donats Erts, Uldis Malinovskis, Andis Liepins

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Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University Hospital by Mohammad Alshraideh, Najwan Alshraideh, Abedalrahman Alshraideh, Yara Alkayed, Yasmin Al Trabsheh, Bahaaldeen Alshraideh

“…This study uses the Jordan University Hospital (JUH) Heart Dataset to develop and evaluate machine-learning models for predicting heart disease. The primary objective of this study is to enhance prediction accuracy by utilizing a comprehensive approach that includes data preprocessing, feature selection, and model development. …”
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Applied machine learning for predicting the properties and carbon and phosphorus fate of pristine and engineered hydrochar by Shiyu Xie, Tao Zhang, Siming You, Santanu Mukherjee, Mingjun Pu, Qing Chen, Yaosheng Wang, Esmat F. Ali, Hamada Abdelrahman, Jörg Rinklebe, Sang Soo Lee, Sabry M. Shaheen

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The Optimal Machine Learning Model for the Precise Prediction of HighPerformance Concrete Strength Property by Yufeng Qian

“…The present study employs a machine learning-based support vector regression (SVR) method to implement compressive strength prediction. …”
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Identifying Ocean Submesoscale Activity From Vertical Density Profiles Using Machine Learning by Leyu Yao, John R. Taylor, Dani C. Jones, Scott D. Bachman

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The good, the better and the challenging: Insights into predicting high-growth firms using machine learning by Sermet Pekin, Aykut Şengül

“…This study aims to classify high-growth firms using several machine learning algorithms, including K-Nearest Neighbors, Logistic Regression with L1 (Lasso) and L2 (Ridge) Regularization, XGBoost, Gradient Descent, Naive Bayes and Random Forest. …”
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Predicting the delisted companies of Tehran Stock Exchange using machine learning based algorithms by Aminollah Zarghami, Meysam Doaei, Abtin Boostani

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Machine learning techniques for estimating high–temperature mechanical behavior of high strength steels by C. Yazici, F.J. Domínguez-Gutiérrez

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Dynamic Interferometry for Freeform Surface Measurement Based on Machine Learning-Configured Deformable Mirror by Xu Chang, Yao Hu, Jintao Wang, Xiang Liu, Qun Hao

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Integrating Handcrafted Features with Machine Learning for Hate Speech Detection in Albanian Social Media by Fetahi Endrit, Hamiti Mentor, Susuri Arsim, Zenuni Xhemal, Ajdari Jaumin

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Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review by Taseef Hasan Farook, Nafij Bin Jamayet, Johari Yap Abdullah, Mohammad Khursheed Alam

“…GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. …”
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COVID-19 World Vaccine Adverse Reactions Based on Machine Learning Clustering Algorithm by Lozan M. Abdulrahman, Adnan Mohsin Abdulazeez, Dathar A. Hasan

Subjects: “…COVID-19 Vaccine Reactions, Machine Learning, Clustering Algorithm, VARSE, Weka…”
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Robust analysis of photovoltaic plants: A framework based on prediction uncertainties by machine learning by Seyyed Shahabaddin Hosseini Dehshiri, Bahar Firoozabadi

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Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods by Gumgum Darmawan, Gatot Riwi Setyanto, Defi Yusti Faidah, Budhi Handoko

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Password strength verification based on machine learning algorithms and LSTM recurrent neural networks by V. V. Belikov, I. A. Prokuronov

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A Machine Learning Approach to Adapt Local Land Use Planning to Climate Change by Julia Forster, Stefan Bindreiter, Birthe Uhlhorn, Verena Radinger-Peer, Alexandra Jiricka-Pürrer

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Selection of Machine Learning Models for Oil Price Forecasting: Based on the Dual Attributes of Oil by Lei Yan, Yuting Zhu, Haiyan Wang

“…Since the commodity and financial attributes of crude oil will have a long-term or short-term impact on crude oil prices, we propose a de-dimension machine learning model approach to forecast the international crude oil prices. …”
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A Machine Learning Algorithm to Predict Medical Device Recall by the Food and Drug Administration by Victor Barbosa Slivinskis, Isabela Agi Maluli, Joshua Seth Broder

“…Our objective was to evaluate the sensitivity, specificity, and accuracy of a machine learning (ML) algorithm using publicly available data to predict medical device recalls by the FDA. …”
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Machine learning suggests climate and seasonal definitions should change under global warming by Milton Speer, Lance Leslie

“…The greater frequency and variability of floods, heatwaves, and droughts challenge traditional definitions of climate periods as 30-year means. Machine learning (ML) studies, focusing on southern Australia, identified the dominant attributes of these precipitation and temperature events. …”
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Machine learning enabled measurements of astrophysical (p,n) reactions with the SECAR recoil separator by P. Tsintari, N. Dimitrakopoulos, R. Garg, K. Hermansen, C. Marshall, F. Montes, G. Perdikakis, H. Schatz, K. Setoodehnia, H. Arora, G. P. A. Berg, R. Bhandari, J. C. Blackmon, C. R. Brune, K. A. Chipps, M. Couder, C. Deibel, A. Hood, M. Horana Gamage, R. Jain, C. Maher, S. Miskovich, J. Pereira, T. Ruland, M. S. Smith, M. Smith, I. Sultana, C. Tinson, A. Tsantiri, A. Villari, L. Wagner, R. G. T. Zegers

“…We used a novel approach that integrates machine learning with ion-optical simulations to find an ion-optical solution for the separator that enables the measurement of (p,n) reactions, despite the reaction leaving the mass of the nucleus nearly unchanged. …”
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