Emo-SL Framework: Emoji Sentiment Lexicon Using Text-Based Features and Machine Learning for Sentiment Analysis by Manar Alfreihat, Omar Saad Almousa, Yahya Tashtoush, Anas AlSobeh, Khalid Mansour, Hazem Migdady

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Architecture-Aware Augmentation: A Hybrid Deep Learning and Machine Learning Approach for Enhanced Parkinson’s Disease Detection by Madjda Khedimi, Tao Zhang, Hanine Merzougui, Xin Zhao, Yanzhang Geng, Khamsa Djaroudib, Pascal Lorenz

“…This study examines the performance of hybrid deep learning and machine learning models in detecting PD using spiral drawings, with a focus on the impact of data augmentation techniques. …”
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Machine Learning-Based Detection of Archeological Sites Using Satellite and Meteorological Data: A Case Study of Funnel Beaker Culture Tombs in Poland by Krystian Kozioł, Natalia Borowiec, Urszula Marmol, Mateusz Rzeszutek, Celso Augusto Guimarães Santos, Jerzy Czerniec

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Ensemble machine learning prediction accuracy: local vs. global precision and recall for multiclass grade performance of engineering students by Yagyanath Rimal, Yagyanath Rimal, Navneet Sharma

“…The primary goal is to address challenges in multiclass data preparation and evaluate the best machine learning models using both micro and macro accuracy metrics derived from baseline comparisons. …”
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Leveraging machine learning techniques for image classification and revealing social media insights into human engagement with urban wild spaces by Haider Khalid, Marcus J. Collier

“…This research explores how advanced machine learning techniques, leveraging social media data for image classification, can be used to gain deeper insights into public engagement with urban wild spaces. …”
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Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State by Qian Hu, Jessica Irwin, Qi Sun, Christopher Messenger, Lami Suleiman, Ik Siong Heng, John Veitch

“…The optimized computational cost of BNS signal analysis with our framework shows that machine learning has the potential to be an indispensable tool for future catalog-level BNS analyses, paving the way for large-scale investigations of BNS-related physics across the 3G observational landscape.…”
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A comparative performance analysis of machine learning models for compressive strength prediction in fly ash-based geopolymers concrete using reference data by Muhammad Kashif Anwar, Muhammad Ahmed Qurashi, Xingyi Zhu, Syyed Adnan Raheel Shah, Muhammad Usman Siddiq

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Future groundwater drought analysis under data scarcity using MedCORDEX regional climatic models and machine learning: The case of the Haouz Aquifer by El Bouazzaoui Imane, Ait Elbaz Aicha, Ait Brahim Yassine, Machay Hicham, Bougadir Blaid

“…The comparative analysis of the machine learning models shows that Random Forest stands out for its superior predictive performance, influenced by annual trends and long-term climatic indices, with significant contributions from geographical variables. …”
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Forecasting birth trends in Ethiopia using time-series and machine-learning models: a secondary data analysis of EDHS surveys (2000–2019) by Daniel Adane, Assefa Andargie Kassa, Berhanu Abebaw Mekonnen, Rahel Mulatie Anteneh, Chalachew Yenew, Meron Asmamaw Alemayehu, Tilahun Degu Tsega, Sintayehu Simie Tsega, Almaw Genet Yeshiwas, Abathun Temesegen, Habitamu Mekonen, Amare Genetu Ejigu, Abraham Teym, Gashaw Melkie Bayeh, Getaneh Atikilit, Tesfaneh Shimels, Wolde Melese Ayele, Ahmed Fentaw Ahmed, Birhanemaskal Malkamu, Wondimnew Desalegn Addis, Getasew Yirdaw, Chalachew Abiyu Ayalew, Kalaab Esubalew, Zeamanuel Anteneh Yigzaw

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Data-driven analysis in the selective oligomerization of long-chain linear alpha olefin on zeolite catalysts: A machine learning-based parameter study by Sung Woo Lee, Marcel Jonathan Hidajat, Seung Hyeok Cha, Gwang-Nam Yun, Dong Won Hwang

“…In this study, the oligomerization of 1-octene was investigated using various zeolites through both experimental and machine learning (ML) approaches. The structural characteristics of the zeolites and experimental conditions were used as input parameters to analyze the feature importance of each key factor on the oligomerization of 1-octene. …”
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A machine learning analysis of suicidal ideation and suicide attempt among U.S. youth and young adults from multilevel, longitudinal survey data by Molly M. Jacobs, Anne V. Kirby, Jessica M. Kramer, Nicole M. Marlow

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Deciphering the proteome of Escherichia coli K-12: Integrating transcriptomics and machine learning to annotate hypothetical proteins by Sagarika Chakraborty, Zachary Ardern, Habibu Aliyu, Anne-Kristin Kaster

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Enhancing airport services: data-driven analysis of passenger satisfaction and service quality in Southeast Asia by Pholsook Thitinan, Ramjan Sarawut, Wipulanusat Warit

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Cross‐Domain Foundation Model Adaptation: Pioneering Computer Vision Models for Geophysical Data Analysis by Zhixiang Guo, Xinming Wu, Luming Liang, Hanlin Sheng, Nuo Chen, Zhengfa Bi

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Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study by Zihan Li, Zihan Li, Yibo Zhang, Zixiang Chen, Jiangming Chen, Hui Hou, Cheng Wang, Zheng Lu, Xiaoming Wang, Xiaoping Geng, Fubao Liu

“…This study aimed to develop a model that dynamically predicts the risk of hepatolithiasis recurrence using a machine-learning (ML) approach based on multiple clinical high-order correlation data.Materials and methodsData from patients with RH who underwent surgery at five centres between January 2015 and December 2020 were collected and divided into training and testing sets. …”
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Learning behavior analysis and personalized recommendation system of online education platform based on machine learning by Feng Ma

“…A learning behavior analysis model is constructed using a machine learning algorithm by collecting a large amount of learning behavior data from online education platforms, such as learning duration, frequency of course visits, homework completion, interaction records, etc. …”
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System of complex data analysis of thematic sites ISCAD IS by I. I. Piletski, M. P. Batura, N. A. Volоrоva, P. A. Zorko, A. O. Kulevich

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Machine Learning and Deep Learning for Diagnosis of Lumbar Spinal Stenosis: Systematic Review and Meta-Analysis by Tianyi Wang, Ruiyuan Chen, Ning Fan, Lei Zang, Shuo Yuan, Peng Du, Qichao Wu, Aobo Wang, Jian Li, Xiaochuan Kong, Wenyi Zhu

“…Although increasing studies of traditional machine learning (TML) and deep learning (DL) were conducted in the field of diagnosing LSS and gained prominent results, the performance of these models has not been analyzed systematically. …”
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Insights into gender-equity in healthcare accessibility in northern Nigeria: descriptive and predictive approaches by Chika Yinka-Banjo, Olasupo Ajayi, Mary Akinyemi, David Tresner-Kirsch, Adekemi Omotubora

Subjects: “…Data analysis…”
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Machine Learning Model for Processing Aerospace Images of the Earthʼs Surface by T. F. Starovoitova, I. A. Starovoitov

“…The methodology for creating a machine learning model includes defining the goals and objectives of the model, selecting an appropriate learning algorithm (in this case, neural networks), collecting and preparing a data set, tuning the model, and testing on a test data set. …”
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