Tree-Based Algorithms and Incremental Feature Optimization for Fault Detection and Diagnosis in Photovoltaic Systems by Khaled Chahine

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Distress-Based Pavement Condition Assessment Using Artificial Intelligence: A Case Study of Egyptian Roads by Mostafa M. Radwan, Sundus A. Faris, Ahmed Y. Barakat, Ahmad Mousa

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Predicting climate-driven shift of the East Mediterranean endemic Cynara cornigera Lindl by Heba Bedair, Heba Bedair, Yehia Hazzazi, Asmaa Abo Hatab, Marwa Waseem A. Halmy, Mohammed A. Dakhil, Mohammed A. Dakhil, Mubaraka S. Alghariani, Mubaraka S. Alghariani, Mari Sumayli, A. El-Shabasy, Mohamed M. El-Khalafy

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Comparative analysis of machine learning algorithms for predicting depression among individuals with diabetes by Hind Bourkhime, Noura Qarmiche, Soumaya Benmaamar, Nada Lazar, Mohammed Omari, Mohamed Berraho, Nabil Tachfouti, Samira El Fakir, Hanan El Ouahabi, Nada Otmani

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Advancing Knowledge on Machine Learning Algorithms for Predicting Childhood Vaccination Defaulters in Ghana: A Comparative Performance Analysis by Eliezer Ofori Odei-Lartey, Stephaney Gyaase, Dominic Asamoah, Thomas Gyan, Kwaku Poku Asante, Michael Asante

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Artificial intelligence survival models for identifying relevant risk factors for incident diabetes in Azar cohort population by Neda Gilani, Mohammadhossein Somi, Farzaneh Hamidi, Pasqualina Santaguida, Elnaz Faramarzi, Reza Arabi Belaghi

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Evaluating machine leaning algorithms for accuracy, stability, and among-predictors discriminability in modeling species-richness across ten datasets by Yong Cao, Tyler E. Schartel, David C. Houghton, Jared Ross, Dana M. Infante

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A Practical Method for Red-Edge Band Reconstruction for Landsat Image by Synergizing Sentinel-2 Data with Machine Learning Regression Algorithms by Yuan Zhang, Zhekui Fan, Wenjia Yan, Chentian Ge, Huasheng Sun

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Improved mapping of highland bamboo forests using Sentinel-2 time series and machine learning in Google Earth Engine by Dagnew Yebeyen, Binyam Tesfaw Hailu, Worku Zewdie, Temesgen Abera, Gudeta W. Sileshi, Melaku Getachew, Sileshi Nemomissa

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Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain by Ali Asghar Rostami, Mohammad Taghi Sattari, Halit Apaydin, Adam Milewski

“…In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). …”
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Impacts of farming and herding activities on land use and land cover changes in the north eastern corridor of Ghana: A comprehensive analysis by Nanii Yenibehit, Abubakari Abdulai, Joseph Amikuzuno, James Kofi Blay

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Sysmon event logs for machine learning-based malware detection by Riki Mi’roj Achmad, Dyah Putri Nariswari, Baskoro Adi Pratomo, Hudan Studiawan

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An integrated machine learning and fractional calculus approach to predicting diabetes risk in women by David Amilo, Khadijeh Sadri, Evren Hincal, Muhammad Farman, Kottakkaran Sooppy Nisar, Mohamed Hafez

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Progress on the world’s primate hotspots and coldspots: modeling ensemble super SDMs in cloud-computers based on digital citizen-science big data and 200+ predictors for more susta... by Moriz Steiner, Falk Huettmann

“…These Super SDMs are conducted using an ensemble of modern Machine Learning algorithms, including Maxent, TreeNet, RandomForest, CART, CART Boosting and Bagging, and MARS with the utilization of cloud supercomputers (as an add-on option for more powerful models). …”
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Cerebrospinal Fluid Leakage Combined with Blood Biomarkers Predicts Poor Wound Healing After Posterior Lumbar Spinal Fusion: A Machine Learning Analysis by Pang Z, Ou Y, Liang J, Huang S, Chen J, Huang S, Wei Q, Liu Y, Qin H, Chen Y

“…In the test group, logistic regression analysis, support vector machine (SVM), random forest (RF), decision tree (DT), XGboost, Naïve Bayes (NB), k-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) were used to identify specific variables. …”
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