Applications of machine learning-assisted extracellular vesicles analysis technology in tumor diagnosis by Liang Xu, Jing Li, Wei Gong

“…Extracellular vesicles (EVs), as a category of nanoparticles, carry a wealth of biological information and play a crucial role in tumor initiation and progression, thereby offering novel approaches for early tumor diagnosis. In recent years, machine learning (ML) technology in the medical field has gained momentum, which utilize various algorithms to analyze input data, identify potential patterns and trends, develop predictive models, and generate high-precision predictions of unknown data, demonstrating its clinical potential in disease diagnosis. …”
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Tools for forecasting regional economic growth using big data and business intelligence technologies by Afanasev Kirill, Kalinin Aleksandr

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Classifying Dry Eye Disease Patients from Healthy Controls Using Machine Learning and Metabolomics Data by Sajad Amouei Sheshkal, Morten Gundersen, Michael Alexander Riegler, Øygunn Aass Utheim, Kjell Gunnar Gundersen, Helge Rootwelt, Katja Benedikte Prestø Elgstøen, Hugo Lewi Hammer

“…<b>Methods:</b> To address this challenge, we conducted a comparative analysis of eight machine learning models on two metabolomics data sets from cataract patients with and without dry eye disease. …”
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Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis by Zishen Zhang, Hong Cheng, Meiyu Chen, Lixin Zhang, Yudou Cheng, Wenjuan Geng, Junfeng Guan

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Predicting Cardiovascular Aging Risk Based on Clinical Data Through the Integration of Mathematical Modeling and Machine Learning by Kuat Abzaliyev, Madina Suleimenova, Siming Chen, Madina Mansurova, Symbat Abzaliyeva, Ainur Manapova, Almagul Kurmanova, Akbota Bugibayeva, Diana Sundetova, Raushan Bitemirova, Nazipa Baizhigitova, Merey Abdykassymova, Ulzhas Sagalbayeva

“…A Random Forest classifier was trained to distinguish between high-risk and low-risk individuals using the same feature set. These machine learning approaches were used as complementary tools to enhance the robustness and interpretability of the modeling results. …”
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Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned by Marketa Ciharova, Khadicha Amarti, Ward van Breda, Ward van Breda, Martin J. Gevonden, Sina Ghassemi, Annet Kleiboer, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Sophia Trofimova, Alexander C. Cooper, Xianhua Peng, Xianhua Peng, Mieke Schulte, Mieke Schulte, Eirini Karyotaki, Eirini Karyotaki, Eirini Karyotaki, Pim Cuijpers, Pim Cuijpers, Pim Cuijpers, Heleen Riper, Heleen Riper

“…Use of multimodal data, meaning data coming from multiple sources, might contribute to machine-learning stress severity detection. …”
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COMPARATIVE ANALYSIS THE PERFORMANCE OF CLIENT-SIDE AND SERVER-SIDE MACHINE LEARNING TECHNOLOGIES by I. Mysiuk, Roman Shuvar

“…The performance analysis of client-side and server-side machine learning technologies is important for understanding the optimal way to model optimization. …”
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Applications and Trends of Machine Learning in Building Energy Optimization: A Bibliometric Analysis by Jingyi Liu, Jianfei Chen

“…With the rapid advancement of machine learning (ML) technologies, their innovative applications in enhancing building energy efficiency are increasingly prominent. …”
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Using data analytics to distinguish legitimate and illegitimate shell companies by Milind Tiwari, Adrian Gepp, Kuldeep Kumar

“…We use a hybrid approach combining graph analytics and supervised machine learning. The resulting detection models have an impressive classification accuracy ranging between 88.17 % and 97.85 %. …”
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Predictive analysis of Somalia’s economic indicators using advanced machine learning models by Bashir Mohamed Osman, Abdillahi Mohamoud Sheikh Muse

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Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach by Eui-Jin Kim

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An empirical analysis of homicides in Mexico through Machine Learning and statistical design of experiments by Jose Eliud Silva Urrutia, Miguel A. Villalobos

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Optimizing Curriculum for Students: A Machine Learning Approach to Time Management Analysis by Jianmin Dong

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Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization by Mohammed Alqarni, Ali Alqarni

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Advanced analysis of defect clusters in nuclear reactors using machine learning techniques by Shuai Ren, Xinyu Zhang, Huizhao Li, Changjun Hu, Dandan Chen

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Harnessing Machine Learning for Predictive Analysis of Crop Resistance to Extreme Weather Conditions by Pan Susovan Kumar, Shivaj Ghorpade Bipin

“…With its use in LoRaWAN, remote fields quickly collect real time weather and crop health data and a complete dataset is generated for analysis. …”
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Machine learning analysis of sex and menopausal differences in the gut microbiome in the HELIUS study by Esther M. C. Vriend, Henrike Galenkamp, Hilde Herrema, Max Nieuwdorp, Bert-Jan H. van den Born, Barbara J. H. Verhaar

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A Review on Machine Learning-Aided Hydrothermal Liquefaction Based on Bibliometric Analysis by Lili Qian, Xu Zhang, Xianguang Ma, Peng Xue, Xingying Tang, Xiang Li, Shuang Wang

“…However, the HTL process is influenced by various complex factors such as operating conditions, feedstock properties, and reaction pathways. Machine learning (ML) methods can utilize existing HTL data to develop accurate models for predicting product yields and properties, which can be used to optimize HTL operation conditions. …”
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Estimation of Mango Fruit Production Using Image Analysis and Machine Learning Algorithms by Liliana Arcila-Diaz, Heber I. Mejia-Cabrera, Juan Arcila-Diaz

“…This study presents an analysis of mango fruit detection using machine learning algorithms, specifically YOLO version 8 and Faster R-CNN. …”
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Classifying Dementia Severity Using MRI Radiomics Analysis of the Hippocampus and Machine Learning by Dong-Her Shih, Yi-Huei Wu, Ting-Wei Wu, Yi-Kai Wang, Ming-Hung Shih

“…Machine learning and deep learning methods are then used for classification with cross-validation, achieving the highest accuracy with support vector machine (SVM). …”
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