Machine Learning-Assisted Drug Repurposing Framework for Discovery of Aurora Kinase B Inhibitors by George Nicolae Daniel Ion, George Mihai Nitulescu, Dragos Paul Mihai

“…<b>Results:</b> The machine learning models trained for drug repurposing showed satisfying performance and yielded the identification of saredutant, montelukast, and canertinib as potential AurB inhibitors. …”
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Species determination using AI machine-learning algorithms: Hebeloma as a case study by Peter Bartlett, Ursula Eberhardt, Nicole Schütz, Henry J. Beker

“…Based on these data an Artificial Intelligence (AI) machine-learning species identifier has been developed that takes as input locality data and a small number of the morphological parameters. …”
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Interpretable Machine Learning Approaches for Forecasting and Predicting Air Pollution: A Systematic Review by Anass Houdou, Imad El Badisy, Kenza Khomsi, Sammila Andrade Abdala, Fayez Abdulla, Houda Najmi, Majdouline Obtel, Lahcen Belyamani, Azeddine Ibrahimi, Mohamed Khalis

“…Abstract Many studies use machine learning to predict atmospheric pollutant levels, prioritizing accuracy over interpretability. …”
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Diabetes Prediction Through Linkage of Causal Discovery and Inference Model with Machine Learning Models by Mi Jin Noh, Yang Sok Kim

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Machine learning-driven optimization for predicting compressive strength in fly ash geopolymer concrete by Maryam Bypour, Mohammad Yekrangnia, Mahdi Kioumarsi

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Discrimination of superficial lymph nodes using ultrasonography and tissue metabolomics coupled with machine learning by Lu Li, Xinyue Wang, Hongyan Deng, Wenjuan Lu, Yasu Zhou, Xinhua Ye, Yong Li, Jie Wang

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Taba Binary, Multinomial, and Ordinal Regression Models: New Machine Learning Methods for Classification by Mohammad Tabatabai, Derek Wilus, Chau-Kuang Chen, Karan P. Singh, Tim L. Wallace

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Retracted: Research on Automatic Classification Method of Ethnic Music Emotion Based on Machine Learning by Journal of Mathematics

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Performance of machine learning tools. Comparve analysis of libraries in interpreted and compiled programming languages by Tomasz Wiejak, Jakub Smołka

Subjects: “…machine learning…”
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Prediction the Choice of Financing for Start-ups using Machine Learning Algorithms and Behavioral Biases by Naimeh Niazi, Hamideh Razavi

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Machine learning validation of the AVAS classification compared to ultrasound mapping in a multicentre study by Katerina Lawrie, Petr Waldauf, Peter Balaz, Radoslav Bortel, Ricardo Lacerda, Emma Aitken, Krzysztof Letachowicz, Mario D’Oria, Vittorio Di Maso, Pavel Stasko, Antonio Gomes, Joana Fontainhas, Matej Pekar, Alena Srdelic, VAVASC Study Group, Stephen O’Neill

“…Here, AVAS performance was tested against multiple ultrasound mapping measurements using machine learning. A prospective multicentre international study (NCT04796558) with patient recruitment from March 2021-July 2024. …”
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Predicting Depression: a comparative study of machine learning approaches based on language usage. by Luciana Mariñelarena-Dondena, Edgardo Ferretti, Manolis Maragoudakis, Maximiliano Sapino, Marcelo Luis Errecalde

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Constructing a fall risk prediction model for hospitalized patients using machine learning by Cheng-Wei Kang, Zhao-Kui Yan, Jia-Liang Tian, Xiao-Bing Pu, Li-Xue Wu

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Comprehensive Evaluation of Bankruptcy Prediction in Taiwanese Firms Using Multiple Machine Learning Models by Hung V. Pham, Tuan Chu, Tuan M. Le, Hieu M. Tran, Huong T.K. Tran, Khanh N. Yen, Son V. T. Dao

“…This study developed an advanced bankruptcy prediction model using Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Network (ANN) algorithms based on datasets from the UCI machine learning repository. The core contribution of this research is the establishment of a hybrid model that effectively combines multiple machine learning (ML) algorithms with advanced data with the Synthetic minority oversampling technique Tomek (SMOTE Tomek) or SMOTE- Edited Nearest Neighbor (SMOTE-ENN) resampling data technique to improve bankruptcy prediction accuracy. …”
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Advanced machine learning approach with dynamic kernel weighting for accurate electrical load forecasting by C. Jeevakarunya, V. Manikandan

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Dynamic Impact-Based Heavy Rainfall Warning with Multi-classification Machine Learning Approaches by Anand Shankar

Subjects: “…impact-based heavy rainfall warning, multi-classification machine learning, impacts of floods, flood assessment, cascading impact…”
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Scalable and Privacy-Preserving Inter-AS Routing Through Machine-Learning-Based Graph Pruning by Davide Andreoletti, Cristina Rottondi, Silvia Giordano, Andrea Bianco

“…In this paper, we exploit machine learning (ML) techniques to prune the network graph by removing the nodes with a low likelihood of being traversed by the shortest path. …”
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Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks by Marcos Luengo Viñuela, Jesús-Ángel Román Gallego

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The Potential of SHAP and Machine Learning for Personalized Explanations of Influencing Factors in Myopic Treatment for Children by Jun-Wei Chen, Hsin-An Chen, Tzu-Chi Liu, Tzu-En Wu, Chi-Jie Lu

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Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma. by Md Al Mehedi Hasan, Md Maniruzzaman, Jie Huang, Jungpil Shin

“…To solve these problems, we have taken datasets from multiple platforms and designed a statistical and machine learning-based system to determine platform-independent key genes (KGs) for HCC patients. …”
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