Classification of Incidental Carcinoma of the Prostate Using Learning Vector Quantization and Support Vector Machines by Torsten Mattfeldt, Danilo Trijic, Hans‐Werner Gottfried, Hans A. Kestler

“…Tumour vascularization (angiogenesis) and epithelial texture were investigated by quantitative stereology. Learning vector quantization (LVQ) and support vector machines (SVM) were used for the purpose of prediction of tumour category from a set of 10 input variables (age, Gleason score, preoperative PSA value, immunohistochemical scores for proliferation and p53‐overexpression, 3 stereological parameters of angiogenesis, 2 stereological parameters of epithelial texture). …”
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Quantitative 3D reconstruction of viral vector distribution in rodent and ovine brain following local delivery by Roberta Poceviciute, Kenneth Mitchell, Angeliki Maria Nikolakopoulou, Suehyun K. Cho, Xiaobo Ma, Phillip Chen, Samantha Figueroa, Ethan J. Sarmiento, Aryan Singh, Oren Hartstein, William G. Loudon, Florent Cros, Alexander S. Kiselyov

“…This pipeline, which combined existing and newly developed machine-learning and other computational tools, effectively removed false positive artifacts abundant in large-scale images of uncleared tissue sections, and subsampling adequately predicted the dispersion of model viral vectors from the point of local drug delivery. …”
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A Method to Detect Concealed Damage in Concrete Tunnels Using a Radar Feature Vector and Bayesian Analysis of Ground-Penetrating Radar Data by Junfang Wang, Heng Chen, Jianfu Lin, Xiangxiong Li

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Comparing and Optimizing Four Machine Learning Approaches to Radar-Based Quantitative Precipitation Estimation by Miaomiao Liu, Juncheng Zuo, Jianguo Tan, Dongwei Liu

“…The key findings are as follows: (1) For models with a single-variable input, the KAN deep learning model outperformed Random Forest, Gradient Boosting Decision Trees, Support Vector Machines, and the traditional Z-R relationship. (2) When six features were used as inputs, the accuracy of the machine learning models improved significantly, with the KAN deep learning model outperforming other machine learning methods. …”
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Quantitative Assessment of Brix in Grafted Melon Cultivars: A Machine Learning and Regression-Based Approach by Uğur Ercan, Ilker Sonmez, Aylin Kabaş, Onder Kabas, Buşra Calık Zyambo, Muharrem Gölükcü, Gigel Paraschiv

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Machine-Learning Classification of Port Wine Stain With Quantitative Features of Optical Coherence Tomography Image by Shengnan Ai, Chengming Wang, Wenxin Zhang, Wenchao Liao, Juicheng Hsieh, Zhenyu Chen, Bin He, Xiao Zhang, Ning Zhang, Ying Gu, Ping Xue

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ML-Based Quantitative Analysis of Linguistic and Speech Features Relevant in Predicting Alzheimer’s Disease by Tripti Tripathi, Rakesh Kumar

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A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI. by Elizabeth M Sweeney, Joshua T Vogelstein, Jennifer L Cuzzocreo, Peter A Calabresi, Daniel S Reich, Ciprian M Crainiceanu, Russell T Shinohara

“…Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. …”
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Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy by Chenlong Fan, Ying Liu, Tao Cui, Mengmeng Qiao, Yang Yu, Weijun Xie, Yuping Huang

“…Near-infrared spectral data from different varieties of maize grain powder were collected, and quantitative analysis of protein content was conducted using Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Extreme Learning Machine (ELM) models. …”
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The value of machine learning based on spectral CT quantitative parameters in the distinguishing benign from malignant thyroid micro-nodules by Zuhua Song, Qian Liu, Jie Huang, Dan Zhang, Jiayi Yu, Bi Zhou, Jiang Ma, Ya Zou, Yuwei Chen, Zhuoyue Tang

“…To explore the application value of various machine learning (ML) algorithms based on dual-layer spectral computed tomography (DLCT) quantitative parameters in distinguishing benign from malignant thyroid micro-nodules. …”
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Lithological Classification Using ZY1-02D Hyperspectral Data by Means of Machine Learning and Deep Learning Methods in the Kohat–Pothohar Plateau, Khyber Pakhtunkhwa, Pakistan by Waqar Ahmad, Lei Liu, Zhenhua Guo, Yasir Shaheen Khalil, Nazir Ul Islam, Fakhrul Islam

“…In this study, ZY1-02D hyperspectral image (HSI) data with moderate spectral and very high spatial resolution were employed for lithological mapping using spectral indices along with support vector machine (SVM) machine learning and spatial–spectral transformer (SSTF) deep learning methods in the Kohat–Pothohar Plateau at the eastern edge of the Main Boundary Thrust (MBT) in Pakistan. …”
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Analyzing customer churn behavior using datamining approach: hybrid support vector machine and logistic regression in retail chain by Mohammad Barzegar, Aliakbar Hasani

“…In the second stage, the support vector machine algorithm, a critical supervised learning algorithm, was used to classify the customers and rank the essential features. …”
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Hydrogen-centric machine learning approach for analyzing properties of tricyclic anti-depressant drugs by Simran Kour, J. Ravi Sankar

“…Additionally, hydrogen representation had a stronger impact on SVR's predictions.DiscussionThese findings highlight the potential of using machine learning techniques in quantitative structure-property relationship (QSPR) models for more efficient and reliable predictions of drug properties.…”
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Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis by Qianqian Zhao, Yijie Li, Chunliu Zhao, Ran Dong, Jiaxin Tian, Ze Zhang, Lin Huang, Jingwen Huang, Junhai Yan, Zhitao Yang, Jiangnan Ruan, Ping Wang, Li Yu, Jieming Qu, Min Zhou

“…This study aimed to develop a machine learning-based predictive model integrating quantitative CT (qCT) radiomics and clinical features to assess the risk of lung fibrosis in COVID-19 patients. …”
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Scoping Review of Machine Learning Techniques in Marker-Based Clinical Gait Analysis by Kevin N. Dibbern, Maddalena G. Krzak, Alejandro Olivas, Mark V. Albert, Joseph J. Krzak, Karen M. Kruger

“…The recent proliferation of novel machine learning techniques in quantitative marker-based 3D gait analysis (3DGA) has shown promise for improving interpretations of clinical gait analysis. …”
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Advanced Machine Learning for Preschooler Magnetic Resonance Imaging Analysis in Classification of Anxiety Disorders by Salik Mian, Pranav Kunderu, Shivm Patel

“…These data were used to train machine learning models: Support Vector Machines (SVMs), decision trees, and Logistic Regression. …”
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Logging Prediction Method of Organic Carbon in Mixed Deposits Based on Machine Learning by CHEN Liangyu, HU Lang, XIN Jintao, LI Yonggui, CHEN Zhi, FU Jianwei

“…In this paper, three machine learning methods, namely XGBoost, random forest and support vector regression (SVR), are used to predict TOC content in the study area by selecting the logging properties sensitive to TOC content, such as natural gamma ray, sonic time difference, neutron and compensation density. …”
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Enhancing Stroke Risk Prediction: Leveraging Machine Learning and Magnetic Resonance Imaging Data for Advanced Assessment by Pranav Kunderu, Salik Mian, Shivm Patel

“…We used MRI scan data obtained from OpenNeuro, specifically images showing the signs of pre-stroke and post-stroke. We trained machine learning models using the data, including support vector machines (SVMs), K-nearest neighbors (KNNs), and random forests. …”
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Comparison of Multiple NIR Instruments for the Quantitative Evaluation of Grape Seed and Other Polyphenolic Extracts with High Chemical Similarities by Matyas Lukacs, Flora Vitalis, Adrienn Bardos, Judit Tormási, Krzysztof B. Bec, Justyna Grabska, Zoltan Gillay, Rita A. Tömösközi-Farkas, László Abrankó, Donatella Albanese, Francesca Malvano, Christian W. Huck, Zoltan Kovacs

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Develop Approach to Predicate Software Reliability Growth Model Parameters Based on Machine learning by anfal A. Fadhil, Asmaa’ H. AL_Bayati, Ibrahim Ahmed Saleh

“…The parameters are evaluated using three algorithms: machine learning decision tree (DT), support vector machine (SVM), and K-nearest neighbors (K-NN). …”
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