A recurrence model for non-puerperal mastitis patients based on machine learning. by Gaosha Li, Qian Yu, Feng Dong, Zhaoxia Wu, Xijing Fan, Lingling Zhang, Ying Yu

“…The aim of this research is to create and validate a recurrence model using machine learning for patients with non-puerperal mastitis.…”
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Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling by Daphne N. Katsarou, Eleni I. Georga, Maria A. Christou, Panagiota A. Christou, Stelios Tigas, Costas Papaloukas, Dimitrios I. Fotiadis

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Machine learning-driven benchmarking of China's wastewater treatment plant electricity consumption by Minjian Li, Chongqiao Tang, Junhan Gu, Nianchu Li, Ahemaide Zhou, Kunlin Wu, Zhibo Zhang, Hui Huang, Hongqiang Ren

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Letter and Person Recognition in Freeform Air-Writing Using Machine Learning Algorithms by Huseyin Kunt, Zeki Yetgin, Furkan Gozukara, Turgay Celik

“…Fourier and wavelet transforms are used to extract features and the performances of various machine learning algorithms, namely Decision Tree, Random-Forest, K-Nearest Neighbors, Support Vector Machine, Artificial Neural Networks, and SubSpace KNN, are comparatively studied. …”
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Perovskite Solar Cell: Chemical Composition and Bandgap Energy via Machine Learning by Filipi França dos Santos, Kelly Cristine Da Silveira, Gesiane Mendonça Ferreira, Daniella Herdi Cariello, Mônica Calixto de Andrade

“…This study utilized the comprehensive MaterialsZone database to feed machine learning algorithms, focusing on Support Vector Machine (SVM) and Random Forest (RF) methodologies to predict the bandgap energy in a targeted perovskite composition. …”
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Exploring Early Learning Challenges in Children Utilizing Statistical and Explainable Machine Learning by Mithila Akter Mim, M. R. Khatun, Muhammad Minoar Hossain, Wahidur Rahman, Arslan Munir

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Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models by Khabat Khosravi, Aitazaz A. Farooque, Amir Naghibi, Salim Heddam, Ahmad Sharafati, Javad Hatamiafkoueieh, Soroush Abolfathi

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The performance of a machine learning model in predicting accelerometer-derived walking speed by Aleksej Logacjov, Tonje Pedersen Ludvigsen, Kerstin Bach, Atle Kongsvold, Mats Flaaten, Tom Ivar Lund Nilsen, Paul Jarle Mork

“…The aim of this study was to develop and evaluate the performance of a machine learning classifier in predicting slow (≤4 km/h), moderate (4.1–5.4 km/h), and brisk (≥5.5 km/h) walking speeds in adults based on dual and single accelerometer set-ups. …”
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Solar Irradiance Prediction for Zaria Town Using Different Machine Learning Models by Ibrahim Abdulwahab, Sulaiman Haruna Sulaiman, Umar Musa, Ibrahim Abdullahi Shehu, Abdullahi Kakumi Musa, Ismaila Mahmud, Mohammed Musa, Abdullahi Abubakar, Abdulrahman Olaniyan

Subjects: “…Machine Learning, Renewable Energy, Solar Irradiance, Weather Prediction.…”
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What drives the profitability of banking sectors in the European Union? The machine learning approach by Bernardelli Michał, Korzeb Zbigniew, Niedziółka Paweł

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Evaluation of Price Prediction of Houses in a Real Estate via Machine Learning by A. A. Ibrahim, O. A. Ayilara-Adewale, A. A. Alabi, D. A. Olusesi

Subjects: “…Prediction system; used car; extra tree regression; random forest regression; machine learning…”
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Machine Learning and Medical Data: Predicting ICU Mortality and Re-admission Risks by Runia Roy, Ulya Bayram

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Integrating sustainability into cybersecurity: insights from machine learning based topic modeling by Krishnashree Achuthan, Sriram Sankaran, Swapnoneel Roy, Raghu Raman

“…Our unique study investigates the integration of environmental sustainability into cybersecurity practices by identifying six pivotal themes through a textual analysis of related publications via machine learning based topic modeling. These themes highlight the convergence of cybersecurity with sustainable development goals (SDGs), particularly SDGs 7 (Affordable and Clean Energy), 9 (Industry, Innovation, and Infrastructure), and 8 (Decent Work and Economic Growth). …”
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Statistical Analysis of Public Sentiment on the Ghanaian Government: A Machine Learning Approach by John Andoh, Louis Asiedu, Anani Lotsi, Charlotte Chapman-Wardy

“…Through natural language processing and machine learning techniques, unstructured data forms from these sources can be analyzed using traditional statistical learning. …”
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Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning by Damir Mulc, Jaksa Vukojevic, Eda Kalafatic, Mario Cifrek, Domagoj Vidovic, Alan Jovic

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Machine Learning-Based Diabetes Risk Prediction Using Associated Behavioral Features by Ayodeji O. J. Ibitoye, Joseph D. Akinyemi, Olufade F. W. Onifade

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Machine Learning-Based Normal White Blood Cell Multi-Classification Optimization by Taeyeon Gil, Sukjun Lee, Onseok Lee

“…Several studies have employed deep learning (DL) or machine learning (ML) methods; no significant difference in performance between the two methods has been demonstrated. …”
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Machine learning to identify environmental drivers of phytoplankton blooms in the Southern Baltic Sea by Maximilian Berthold, Pascal Nieters, Rahel Vortmeyer-Kley

Subjects: “…Scientific machine learning…”
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Spatio-temporal risk prediction of leptospirosis: A machine-learning-based approach by Rodrigue Govan, Romane Scherrer, Baptiste Fougeron, Christine Laporte-Magoni, Roman Thibeaux, Pierre Genthon, Philippe Fournier-Viger, Cyrille Goarant, Nazha Selmaoui-Folcher

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Recycled integrated circuit detection using reliability analysis and machine learning algorithms by Udaya Shankar Santhana Krishnan, Kalpana Palanisamy

“…In this work, three machine learning methods, namely K‐means clustering, back propagation neural network (BPNN) and support vector machines (SVMs), are used to detect the recycled IC aged for a shorter period (1 day) with minimum data size. …”
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