Improving chlorophyll-a estimation using Sentinel-2 data: a comparative analysis of augmented datasets by Jinmyeong Lee, Do Hyuck Kwon, Heewon Jeong, Gibeom Nam, Euiho Hwang, Jin Hwi Kim, Kyung Hwa Cho, Hyo Gyeom Kim

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Quantitative assessment of factors that influence heat vulnerability in residential areas using machine learning and unmanned aerial vehicle by Jawoon Gu, Dongwoo Kim, Chulmin Jun, Seungwoo Son

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Bridging Predictive Insights and Retention Strategies: The Role of Account Balance in Banking Churn Prediction by Tahsien Al-Quraishi, Osamah Albahri, Ahmed Albahri, Abdullah Alamoodi, Iman Mohammed Sharaf

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Identification of a New Lung Cancer Biomarker Signature Using Data Mining and Preliminary In Vitro Validation by Ferid Ben Ali, Denis Mustafov, Maria Braoudaki, Sola Adeleke, Iosif Mporas

“…The analysis includes three experiments—the bioinformatic (in silico), in vitro, and machine learning analyses. …”
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An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms by Maithri Bairy, Krishnaraj Chadaga, Niranjana Sampathila, R. Vijaya Arjunan, G. Muralidhar Bairy

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Air temperature estimation and modeling using data driven techniques based on best subset regression model in Egypt by Ahmed Elbeltagi, Dinesh Kumar Vishwakarma, Okan Mert Katipoğlu, Kallem Sushanth, Salim Heddam, Bhaskar Pratap Singh, Abhishek Shukla, Vinay Kumar Gautam, Chaitanya Baliram Pande, Saddam Hussain, Subhankar Ghosh, Hossein Dehghanisanij, Ali Salem

“…This study aims to identify the most accurate forecasting model for daily minimum (Tmin) and maximum (Tmax) temperatures in a semi-arid environment. Five machine learning models—linear regression (LR), additive regression (AR), support vector machine (SVM), random subspace (RSS), and M5 pruned (M5P)—were compared for Tmax and Tmin forecasting in Gharbia Governorate, Egypt, using data from 1979 to 2014. …”
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Prediction of bank credit customers churn based on machine learning and interpretability analysis by Ying Li, Keyue Yan

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Sentiment Analysis of Product Reviews Using Machine Learning and Pre-Trained LLM by Pawanjit Singh Ghatora, Seyed Ebrahim Hosseini, Shahbaz Pervez, Muhammad Javed Iqbal, Nabil Shaukat

“…The motivation behind this work lies in the demand for more nuanced understanding of consumer sentiments that can drive data-informed business decisions. In this research, we applied machine learning-based classifiers, i.e., Random Forest, Naive Bayes, and Support Vector Machine, alongside the GPT-4 model to benchmark their effectiveness for sentiment analysis. …”
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Fault Diagnosis in Power Generators: A Comparative Analysis of Machine Learning Models by Quetzalli Amaya-Sanchez, Marco Julio del Moral Argumedo, Alberto Alfonso Aguilar-Lasserre, Oscar Alfonso Reyes Martinez, Gustavo Arroyo-Figueroa

“…This work presents a comparative analysis of machine learning (ML) models for the generator fault diagnosis. …”
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OCRClassifier: integrating statistical control chart into machine learning framework for better detecting open chromatin regions by Xin Lai, Xin Lai, Min Liu, Yuqian Liu, Xiaoyan Zhu, Jiayin Wang, Jiayin Wang

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Analysis of Microbiome for AP and CRC Discrimination by Alessio Rotelli, Ali Salman, Leandro Di Gloria, Giulia Nannini, Elena Niccolai, Alessio Luschi, Amedeo Amedei, Ernesto Iadanza

“…Microbiome data analysis is essential for understanding the role of microbial communities in human health. …”
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Sentiment Analysis of Online Vehicle Tax Renewal Application Users Using Support Vector Machine Algorithm by Muhamad Ilham Fauzy, Ferian Fauzi Abdulloh

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An Enhanced Gas Sensor Data Classification Method Using Principal Component Analysis and Synthetic Minority Over-Sampling Technique Algorithms by Xianzhang Zeng, Muhammad Shahzeb, Xin Cheng, Qiang Shen, Hongyang Xiao, Cao Xia, Yuanlin Xia, Yubo Huang, Jingfei Xu, Zhuqing Wang

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Application and Research of Attention Mechanism Combined with Data Visualisation for Entrepreneurial Learning Course Recommendation System in Universities and Colleges by Chunhua Dong

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Big Data, Big Insights: Leveraging Data Analytics to Unravel Cardiovascular Exposome Complexities by Ramzi Ibrahim, Hoang Nhat Pham, Khurram Nasir, Omar Hahad, Ashutosh Sabharwal, Sadeer Al-Kindi

“…Interactions with the social, natural, and built components of the exposome significantly impact cardiovascular disease prevalence and mortality. Robust data analytics, including machine learning and geospatial analysis, have advanced our understanding of how these factors converge to influence cardiovascular disease risk. …”
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Efficiency enhancement of energy supply chains using a machine learning-driven network evaluation framework for blockchain adoption by Ardavan Babaei, Erfan Babaee Tirkolaee, Shahryar Sorooshian, Sadia Samar Ali, Gongming Wang

“…Furthermore, a an efficient Machine Learning (ML) technique; i.e., Principal Component Analysis (PCA), is utilized to further assess blockchain adoption strategies. …”
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Assessment of Machine Learning Methods for Concrete Compressive Strength Prediction by Oluwafemi Omotayo, Chinwuba Arum, Catherine Ikumapayi

“…This research sought to forecast concrete compressive strength through six machine learning (ML) algorithms namely Linear Regression (LR), Random Forest (RF), Decision Trees (DT), Gradient Boost (GB), Support Vector Machine (SVM), and Categorical Gradient Boost (CatBoost), and to examine the significance of the input factors on the concrete compressive strength. …”
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Predicting PV Severity: A Cross-Sectional Study Using Machine Learning Methods and Multifactorial Data Analysis From a High Prevalence Region by Soraiya Ebrahimpour-Koujan, Anahita Najafi, Rahil Salari-Baghoonabad, Kamran Balighi, Maryam Daneshpazhooh

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Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the "RegulaRN Leitos Gerais" platform. by Tiago de Oliveira Barreto, Fernando Lucas de Oliveira Farias, Nicolas Vinícius Rodrigues Veras, Pablo Holanda Cardoso, Gleyson José Pinheiro Caldeira Silva, Chander de Oliveira Pinheiro, Maria Valéria Bezerra Medina, Felipe Ricardo Dos Santos Fernandes, Ingridy Marina Pierre Barbalho, Lyane Ramalho Cortez, João Paulo Queiroz Dos Santos, Antonio Higor Freire de Morais, Gustavo Fontoura de Souza, Guilherme Medeiros Machado, Márcia Jacyntha Nunes Rodrigues Lucena, Ricardo Alexsandro de Medeiros Valentim

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Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the “RegulaRN Leitos Gerais” platform by Tiago de Oliveira Barreto, Fernando Lucas de Oliveira Farias, Nicolas Vinícius Rodrigues Veras, Pablo Holanda Cardoso, Gleyson José Pinheiro Caldeira Silva, Chander de Oliveira Pinheiro, Maria Valéria Bezerra Medina, Felipe Ricardo dos Santos Fernandes, Ingridy Marina Pierre Barbalho, Lyane Ramalho Cortez, João Paulo Queiroz dos Santos, Antonio Higor Freire de Morais, Gustavo Fontoura de Souza, Guilherme Medeiros Machado, Márcia Jacyntha Nunes Rodrigues Lucena, Ricardo Alexsandro de Medeiros Valentim

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