Utilizing machine-learning techniques on MRI radiomics to identify primary tumors in brain metastases by W. L. Yang, W. L. Yang, X. R. Su, S. Li, K. Y. Zhao, Q. Yue

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The impact of multiple representations on students' understanding of vector field concepts: Implementation of simulations and sketching activities into lecture-based recitations in... by Larissa Hahn, Pascal Klein

“…While the former is valuable for quantitative calculations, vector field diagrams are beneficial for showing many properties of a field at a glance. …”
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Optimization of urban green space in Wuhan based on machine learning algorithm from the perspective of healthy city by Xuechun Zhou, Xiaofei Zou, Wenzuixiong Xiong

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Quantitative image analysis of the extracellular matrix of esophageal squamous cell carcinoma and high grade dysplasia via two-photon microscopy by Kausalya Neelavara Makkithaya, Wei-Chung Chen, Chun-Chieh Wu, Ming-Chi Chen, Wei-Hsun Wang, Jackson Rodrigues, Ming-Tsang Wu, Nirmal Mazumder, I-Chen Wu, Guan-Yu Zhuo

“…Unlike previous studies on cancer diagnosis using two-photon microscopy, quantitative analysis or machine learning (ML) algorithms need to be used to determine the subtle structural changes in images and the structural features that are statistically meaningful in cancer development. …”
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Future of Alzheimer's detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence by Sahar Rezaei, Farzan Asadirad, Alireza Motamedi, Mohammadsadegh Kamran, Farzaneh Parsa, Haniyeh Samimi, Parna Ghannadikhosh, Mahdi Zahmatyar, Seyed Ali Hosseinzadeh, Hossein Arabi

“…Through systematic analysis of 11 key studies across multiple international databases, we evaluated various AI architectures, including machine learning algorithms and deep learning networks, applied to qEEG data for AD detection. …”
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Enhanced prediction of ionic liquid toxicity using a meta-ensemble learning framework with data augmentation by Safa Sadaghiyanfam, Hiqmet Kamberaj, Yalcin Isler

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Extraction of the essential elements for urban systems modelling – A word-to-vector approach by Tatenda Hatidani Katsumbe, Arnesh Telukdarie, Megashnee Munsamy, Christian Tshukudu

“…Through a systematic literature review, data on 13 key systems is qualitatively extracted from research databases such as Scopus and ScienceDirect, for the duration 2014 – 2024. Through word2vector analysis, machine learning techniques are utilised to perform the quantitative mapping of each urban system into corresponding system characteristics, and quantitatively illustrate them based on relative importance. …”
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THE CORRELATION BETWEEN HOUSING SANITATION AND EXISTENCE OF VECTORS IN KENDARI SUB DISTRICTS, INDONESIA by Zuing Putra Arisanto, Ramadhan Tosepu, Sitti Rabbani Karimuna, Asnia Zainuddin, Yasnani Yasnani, Nurmaladewi Nurmaladewi

“…Data were collected using Field Learning Experience Report, and analyzed using Chi-Square test. …”
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Quantitative ultrasound classification of healthy and chemically degraded ex-vivo cartilage by Angela Sorriento, Lorena Guachi-Guachi, Claudia Turini, Enrico Lenzi, Paolo Dolzani, Gina Lisignoli, Sajedeh Kerdegari, Gaetano Valenza, Claudio Canale, Leonardo Ricotti, Andrea Cafarelli

“…Abstract In this study, we explore the potential of ten quantitative (radiofrequency-based) ultrasound parameters to assess the progressive loss of collagen and proteoglycans, mimicking an osteoarthritis condition in ex-vivo bovine cartilage samples. …”
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Analysis and Prediction of Grouting Reinforcement Performance of Broken Rock Considering Joint Morphology Characteristics by Guanglin Liang, Linchong Huang, Chengyong Cao

“…Furthermore, multiple machine learning algorithms are employed to construct a robust predictive model. …”
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Characterization of defective coffee beans and blends differentiation based on 1H qNMR technique by Gui-Lin Hu, Chen-Xi Quan, Hao-Peng Dai, Ming-Hua Qiu

“…The 1H NMR from water-soluble content was shown to be more effective than that of oil fraction for qualitative of DCB blends, regardless of whether partial least squares discriminant analysis (PLS-DA) or machine learning (ML) algorithms were used. Support vector machine (SVM) was proved to be excellent for distinguishing DCB blends. …”
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Research on Rapid and Non-Destructive Detection of Coffee Powder Adulteration Based on Portable Near-Infrared Spectroscopy Technology by Fujie Zhang, Xiaoning Yu, Lixia Li, Wanxia Song, Defeng Dong, Xiaoxian Yue, Shenao Chen, Qingyu Zeng

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Two-dimensional QSAR-driven virtual screening for potential therapeutics against Trypanosoma cruzi by Naseer Maliyakkal, Sunil Kumar, Ratul Bhowmik, Harish Chandra Vishwakarma, Prabha Yadav, Bijo Mathew

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Enhancing mathematical modelling education at agricultural universities: A comparative study of dynamic vector diagrams using GeoGebra by Leonid O. Flehantov, Yuliia I. Ovsiienko, Anatolii V. Antonets

“…We examine the hypothesis that dynamic vector diagrams representing mechanical motion characteristics (velocity, acceleration, and force) enhance student learning outcomes. …”
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Identifying fetal yawns based on temporal dynamics of mouth openings: A preterm neonate model using support vector machines (SVMs). by Damiano Menin, Angela Costabile, Flaviana Tenuta, Harriet Oster, Marco Dondi

“…In Study 2 we developed and tested a new machine learning system based on support vector machines (SVM) for identifying yawns. …”
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Quantitative modelling for dengue and Aedes mosquitoes in Africa: A systematic review of current approaches and future directions for Early Warning System development. by Lembris Laanyuni Njotto, Wilfred Senyoni, Ottmar Cronie, Michael Alifrangis, Anna-Sofie Stensgaard

“…This review aims to provide an updated overview of important covariates and quantitative modelling techniques used to predict or forecast dengue and/or its vector Aedes mosquitoes in Africa. …”
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Quantitative Prediction of Low-Permeability Sandstone Grain Size Based on Conventional Logging Data by Deep Neural Network-Based BP Algorithm by Hongjun Fan, Xiaoqing Zhao, Zongjun Wang, Zheqing Zhang, Ao Chang

“…The best model was obtained by using decision tree, support vector machine, shallow and deep neural networks to model the median rock grain size and predict neighboring wells, and a comparative analysis showed that for the problem of predicting the median rock grain size in low-permeability sandstone reservoirs, the deep neural network improved significantly over the shallow one and was much stronger than other machine learning methods. …”
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Federated Learning Enhanced MLP–LSTM Modeling in an Integrated Deep Learning Pipeline for Stock Market Prediction by Jayaraman Kumarappan, Elakkiya Rajasekar, Subramaniyaswamy Vairavasundaram, Ketan Kotecha, Ambarish Kulkarni

“…This suggests that using Federated Learning along with MLP and LSTM as the components of this vector enhanced the function increasing its capacity and reliability in predicting the trends of stocks. …”
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Stroke Prediction Based on Machine Learning by Zhang Yuhan

“…Stroke has become an important cause of death and disability worldwide, which highlights the need for early detection and intervention. Machine learning technology can analyze patients’ historical health data and biometrics to identify high-risk individuals in a timely manner, thereby effectively predicting stroke.This paper evaluates the predictive performance Random Forest and Support Vector Machine (SVM). …”
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Integration of Genetic Algorithm with Machine Learning for Properties Prediction by Rathachai Chawuthai, Siripan Murathathunyaluk, Nalin Amornratthamrong, Run Arunchaipong, Amata Anantpinijwatna

“…Numerous studies have demonstrated that machine learning (ML) provides more accurate estimations of properties for oxygenated organic derivatives compared to the conventional Quantitative Structure-Property Relationship (QSPR) method. …”
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