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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Evaluating integrated learning: A SECI model approach through importance-performance analysis by Sabda Alam Muhammadan, Mochamad Syamsul Ma'arif, Suhendi Suhendi

“…The research addresses the urgent need for effective knowledge integration in public sector organizations, particularly in light of the Ministry’s adoption of the 70:20:10 learning model. …”
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Evaluating integrated learning: A SECI model approach through importance-performance analysis by Sabda Alam Muhammadan, Mochamad Syamsul Ma'arif, Suhendi Suhendi

“…The research addresses the urgent need for effective knowledge integration in public sector organizations, particularly in light of the Ministry’s adoption of the 70:20:10 learning model. …”
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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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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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ANALYSIS OF EARNINGS TRENDS IN THE EDUCATION SECTOR IN ROMANIA by Monica LOGOFĂTU, Cristian ȘTEFĂNESCU

“…Secondly, we conducted a quantitative research to analyze and interpret the evolution of salaries in the education sector in Romania and to study possible correlations between these and a number of macroeconomic indicators. …”
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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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Perceived Leadership Support, Safety Citizenship, and Employee Safety Behavior in the Construction Industry: The Role of Safety Learning by Yousef Kadher, Ahmad Alzubi, Ayşen Berberoğlu, Tolga Öz

“…Drawing on Social Exchange Theory (SET), this study explores the impact of perceived leadership support (PLS) on employee safety behavior (ESB) and safety citizenship behavior (SCB), focusing on the mediating role of SCB and the moderating effect of safety learning (SL). A quantitative approach was employed, collecting a sample size of 410 construction workers from various companies within the Turkish construction sector. …”
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Green HRM and Organizational Sustainability: A Quantitative Corporate Study by Irfan Ul Haque, Asma Irfan Ali

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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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