Anemia Classification System Using Machine Learning by Jorge Gómez Gómez, Camilo Parra Urueta, Daniel Salas Álvarez, Velssy Hernández Riaño, Gustavo Ramirez-Gonzalez

“…We built a supervised learning approach and trained three models (Linear Discriminant Analysis, Decision Trees, and Random Forest) using an anemia dataset from a previous study by Sabatini in 2022. …”
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Intelligent hydraulic fracturing under industry 4.0—a survey and future directions by Jing Jia, Qinghu Fan, Jianglu Jing, Kehui Lei, Lichang Wang

“…It identifies four technical challenges: integrating heterogeneous data, developing intelligent decision-making algorithms, adaptive surface equipment adjustments, and multi-machine collaborative control. …”
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Primary Care Physician Use of Elastic Scattering Spectroscopy on Skin Lesions Suggestive of Skin Cancer by Stephen P. Merry, Ivana T. Croghan, Kimberly A. Dukes, Brian C. McCormick, Gerard T. Considine, Michelle J. Duvall, Curtis T. Thompson, David J. Leffell

“…The device misclassified as “ monitor ” rather than “ investigate further ” 4 keratinocyte carcinomas and 4 melanomas in patients aged 40 years or older (n = 8, 0.5% of lesions, 3.7% of cancers biopsied). Conclusions: The DermaSensor device is an easy-to-use, point-of-care, hand-held skin cancer adjunctive diagnostic device with high sensitivity and NPV to help inform PCP decision-making about skin lesions suspicious for cancer that need further evaluation and those that may be monitored.…”
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A clinical-radiomics nomogram based on automated segmentation of chest CT to discriminate PRISm and COPD patients by TaoHu Zhou, Yu Guan, XiaoQing Lin, XiuXiu Zhou, Liang Mao, YanQing Ma, Bing Fan, Jie Li, WenTing Tu, ShiYuan Liu, Li Fan

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A study on predicting the risk of coronary artery disease in OSAHS patients based on a four-variable screening tool potential predictive model and its correlation with the severity... by Yanli Yao, Yu Li, Yulan Chen, Xuan Qiu, Gulimire Aimaiti, Ayiguzaili Maimaitimin

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A designed predictive modelling strategy based on data decomposition and machine learning to forecast solar radiation by Mumtaz Ali, Ramendra Prasad, Salman Alamery, Aitazaz Ahsan Farooque

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A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures by Sajjad Hussain, Carman Ka Man Lee, Yung Po Tsang, Saad Waqar

“…This dataset was used to train both ML and DL algorithms. ML algorithms included Bayesian regression (BR), K-nearest neighbors (KNN), Random Forest (RF), Decision Tree (DT), and DL algorithm convolutional neural network (CNN). …”
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Integrated rapid risk assessment for dengue fever in settings with limited diagnostic capacity and uncertain exposure: Development of a methodological framework for Tanzania. by Matthias Hans Belau, Juliane Boenecke, Jonathan Ströbele, Mirko Himmel, Daria Dretvić, Ummul-Khair Mustafa, Katharina Sophia Kreppel, Elingarami Sauli, Johanna Brinkel, Ulfia Annette Clemen, Thomas Clemen, Wolfgang Streit, Jürgen May, Amena Almes Ahmad, Ralf Reintjes, Heiko Becher

“…<h4>Results</h4>The result of the suspected case refinement algorithm for clinically defined syndromic cases was consistent with the laboratory-confirmed diagnosis (dengue yes or no). …”
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Active Reinforcement Learning for the Semantic Segmentation of Urban Images by Mahya Jodeiri Rad, Costas Armenakis

“…Using the Cityscapes and GTAv urban datasets, three baseline image segmentation networks (FPN, DeepLabV3, DeepLabV3+) trained with image regions selected by the proposed FWA IoU metric performed better compared to baseline region selection by active learning methods such as the Random selection, Entropy-based selection, and Bayesian Active Learning by Disagreement. …”
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Lung Cancer Prediction Using an Enhanced Neutrosophic Set Combined with a Machine Learning Approach by Vakeel A. Khan, Asheesh Kumar Yadav, Mohammad Arshad, Nadeem Akhtar

“…To address this issue, we propose an Enhanced Neutrosophic Set (ENS) framework integrated with machine learning algorithms to improve the prediction accuracy of lung cancer. …”
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Racial bias in AI-mediated psychiatric diagnosis and treatment: a qualitative comparison of four large language models by Ayoub Bouguettaya, Elizabeth M. Stuart, Elias Aboujaoude

“…These findings underscore critical concerns about the potential for AI to perpetuate racial disparities in mental healthcare, emphasizing the necessity of rigorous bias assessment in algorithmic medical decision support systems.…”
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Advancing breast cancer prediction: Comparative analysis of ML models and deep learning-based multi-model ensembles on original and synthetic datasets. by Kazi Arman Ahmed, Israt Humaira, Ashiqur Rahman Khan, Md Shamim Hasan, Mukitul Islam, Anik Roy, Mehrab Karim, Mezbah Uddin, Ashique Mohammad, Md Doulotuzzaman Xames

“…This study employs various machine learning (ML) algorithms, including KNN, SVM, ANN, RF, XGBoost, ensemble models, AutoML, and deep learning (DL) techniques, to enhance breast cancer diagnosis. …”
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Minimizing charging task time of WRSN assisted with multiple MUVs and laser-charged UAVs by Jian Zhang, Chuanwen Luo, Ning Liu, Yi Hong, Zhibo Chen

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Diagnostic and prognostic implications of family history of fibrotic interstitial lung diseases by D. Duminy-Luppi, A. Alcaide-Aldeano, L. Planas-Cerezales, G. Bermudo, V. Vicens-Zygmunt, P. Luburich, B. Del Río-Carrero, R. Llatjós, L. Pijuan, I. Escobar, F. Rivas, A. Montes-Worboys, Y. Gutiérrez-Rodríguez, D. Rodríguez-Plaza, A. Padró-Miquel, A. Esteve-Garcia, B. Fernández-Varas, C. Flores, M. Fuentes, J. Dorca, S. Santos, R. Perona, A. Günther, J. Shull, M. Molina-Molina

“…Current guidelines do not consider family aggregation or genetic information in the diagnostic algorithm for idiopathic pulmonary fibrosis or other fibrotic ILDs. …”
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Machine Learning Modelling for Soil Moisture Retrieval from Simulated NASA-ISRO SAR (NISAR) L-Band Data by Dev Dinesh, Shashi Kumar, Sameer Saran

“…This study aimed to assess soil moisture and dielectric constant retrieval over agricultural land using machine learning (ML) algorithms and decomposition techniques. Three polarimetric decomposition models were used to extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. …”
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Early prediction of sepsis associated encephalopathy in elderly ICU patients using machine learning models: a retrospective study based on the MIMIC-IV database by Yupeng Han, Xiyuan Xie, Jiapeng Qiu, Yijie Tang, Zhiwei Song, Wangyu Li, Xiaodan Wu, Xiaodan Wu

“…This study aimed to develop a predictive model for SAE in elderly ICU patients.MethodsThe data of elderly sepsis patients were extracted from the MIMIC IV database (version 3.1) and divided into training and test sets in a 7:3 ratio. …”
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Leader-Following Consensus Stability of Discrete-Time Linear Multiagent Systems with Observer-Based Protocols by Bingbing Xu, Lixin Gao, Yan Zhang, Xiaole Xu

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Performance of Machine Learning Classifiers for Diabetes Prediction by Mijala Manandhar, Shaikat Baidya, Babalpreet Kaur, Katia Atoji

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Modeling Canopy Height of Forest–Savanna Mosaics in Togo Using ICESat-2 and GEDI Spaceborne LiDAR and Multisource Satellite Data by Arifou Kombate, Guy Armel Fotso Kamga, Kalifa Goïta

“…We tested four methods: Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN). The RF algorithm obtained the best predictions using 98% relative height (RH98). …”
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The Application of Reinforcement Learning to Pumps—A Systematic Literature Review by Adetoye Ayokunle Aribisala, Usama Ali Salahuddin Ghori, Cristiano A. V. Cavalcante

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