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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Quantum machine learning for Lyapunov-stabilized computation offloading in next-generation MEC networks by Vandana Rani Verma, Dinesh Kumar Nishad, Vishnu Sharma, Vinay Kumar Singh, Anshul Verma, Dharti Raj Shah

“…Abstract Quantum computing and machine learning convergence enable powerful new approaches for optimizing mobile edge computing (MEC) networks. …”
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Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning by Mikhail Petrov, Nadezhda Makarova, Amir Monemian, Jean Pham, Małgorzata Lekka, Igor Sokolov

“…Recent studies have shown that atomic force microscopy (AFM), combined with pattern recognition machine learning, can detect bladder cancer by analyzing cells extracted from urine. …”
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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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Use of machine learning in occupational risk communication for healthcare workers: protocol for scoping review by Gabriela Laudares Albuquerque de Oliveira, Clarice Alves Bonow, Itiberê de Oliveira Castellano Rodrigues, Amanda Xavier Geraldo

“…Introduction With the development of technology, the use of machine learning (ML), a branch of computer science that aims to transform computers into decision-making agents through algorithms, has grown exponentially. …”
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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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Data Driven Classification of Opioid Patients Using Machine Learning–An Investigation by Lisan Al Amin, Md. Saddam Hossain Mukta, Md. Sezan Mahmud Saikat, Md. Ismail Hossain, Md. Adnanul Islam, Mohiuddin Ahmed, Sami Azam

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Predictive modeling for durability characteristics of blended cement concrete utilizing machine learning algorithms by Bo Fu, Hua Lei, Irfan Ullah, Mohammed El-Meligy, Khalil El Hindi, Muhammad Faisal Javed, Furqan Ahmad

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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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