Assessment of Tumor Infiltrating Lymphocytes in Predicting Stereotactic Ablative Radiotherapy (SABR) Response in Unresectable Breast Cancer by Mateusz Bielecki, Khadijeh Saednia, Fang-I Lu, Shely Kagan, Danny Vesprini, Katarzyna J. Jerzak, Roberto Salgado, Raffi Karshafian, William T. Tran

“…Whole slide images (WSIs) were pre-processed, and then a pre-trained convolutional neural network model (CNN) was employed to identify the regions of interest. …”
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Identification of Earthquake Precursors Origin and AI Framework for Automatic Classification for One of These Precursors by Ghada Ali, Lotfy Samy, Omar M. Saad, Ali G. Hafez, El-Sayed Hasaneen, Kamal AbdElrahman, Ibrahim Salah, Mohammed S. Fnais, Hamed Nofel, Ahmed M. Mohamed

“…By automating the classification of these patterns, the true P-wave arrival can be determined in real-time processing, reducing the error in P-wave arrival timing. …”
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A temperature-responsive PLA-based nanosponge as a novel nanoadjuvant and efficient delivery carrier of Ag85B for effective vaccine against Mycobacterium tuberculosis by Jin-Seung Yun, Soo-Min Kim, Jin Sil Lee, Su Hyun Jeong, Hyeryeon Oh, Panmo Son, Sunghyun Kim, Young-Ran Lee, Eunkyung Shin, Sang-Jun Ha, Yong-Woo Jung, Dokeun Kim, Hye-Sook Jeong, Won Il Choi

“…Methods Ag85B was produced using an EZtag fusion tag vector, achieving high product yield and purity. It was then loaded into aPNS, a nanoparticle system with a PLA core and Pluronic shell, through a temperature-responsive process at 4 °C that preserved protein bioactivity. …”
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The role of Toll-like receptor 4 Asp299Gly genetic polymorphism in rehabilitation of patients with post-COVID syndrome by Leуla Sh. Dudchenko, Vladimir A. Beloglazov, Igor А. Yatskov, Galina N. Andreeva, Elena A. Solovyova, Irina V. Shulyak

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A GIS Approach to Modeling the Ecological Niche of an Ecotype of <i>Bouteloua curtipendula</i> (Michx.) Torr. in Mexican Grasslands by Alma Delia Baez-Gonzalez, Jose Miguel Prieto-Rivero, Alan Alvarez-Holguin, Alicia Melgoza-Castillo, Mario Humberto Royo-Marquez, Jesus Manuel Ochoa-Rivero

“…GIS software 10.3 was used to develop two potential distribution models: Model A, with variables obtained directly from a vector climate dataset, and Model B, with derived variables. …”
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A novel machine learning models for meteorological drought forecasting in the semi-arid climate  region by Chaitanya Baliram Pande, Dinesh Kumar Vishwakarma, Aman Srivastava, Kanak N. Moharir, Fahad Alshehri, Norashidah Md Din, Lariyah Mohd Sidek, Bojan Đurin, Abebe Debele Tolche

“…Given the limited studies on ensemble and Machine Learning (ML) models for drought forecasting, this research compares five ML models [Robust Linear Regression, Bagged Trees, Boosted Trees, Support Vector Machine (SVM), and Matern Gaussian Process Regression (GPR)] to determine superior accuracy in the regional context. …”
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Effects of urban sprawl on land use change in the peripheral villages of Tehran metropolis (case study: Tehran-Damavand axis) by ِAshkan Mohammadi, Naser Shafiei Sabet, Alireza Shakiba

“…After field operation and harvesting of samples with two-frequency GPS receivers and introducing it to the software, the classification of complications was performed by support vector machines with a mean total accuracy of 62.69% and a mean Kappa coefficient of 85.33%. …”
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Optimized Machine Learning-Augmented Hybrid Empirical Models for AlGaN/GaN HEMTs: A Comprehensive Analysis by Ahmad Khusro, Saddam Husain, Mohammad Hashmi

“…Thereafter, six extensively optimized ML regression models, namely decision tree (DT), ensemble learning (EL), support vector regression (SVR), kernel approximation regression (KAR), Gaussian process regression (GPR), and neural networks (NN) are employed to simulate the intrinsic behavior of GaN HEMTs. …”
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Prediction of Key Quality Parameters in Hot Air-Dried Jujubes Based on Hyperspectral Imaging by Quancheng Liu, Chunzhan Yu, Yuxuan Ma, Hongwei Zhang, Lei Yan, Shuxiang Fan

“…Subsequently, a nonlinear support vector regression (SVR) model was used to perform regression modeling for the six quality parameters. …”
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STATIC OF ORTHODONTIC APPLIANCES WITH MOVABLE INCLINED PLANE FOR MESIAL BATE TREATMENT by P.S. Flis, A.I. Hryhorenko, N.M. Doroshenko, M.M. Tormakhov, V.V. Filonenko

“…Since the orthodontic force vector is in vestibular direction, the angle β is in the range from -30° to α. …”
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Integrating AI predictive analytics with naturopathic and yoga-based interventions in a data-driven preventive model to improve maternal mental health and pregnancy outcomes by Neha Irfan, Sherin Zafar, Kashish Ara Shakil, Mudasir Ahmad Wani, S. N. Kumar, A. Jaiganesh, K. M. Abubeker

“…A diverse set of machine learning models, including Random Forest, Decision Tree, Support Vector Machine (SVM), Logistic Regression, Gaussian Naive Bayes, and Multilayer Perceptron (MLP), were evaluated alongside ensemble methods to achieve robust and reliable predictions. …”
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Automating tephra fall building damage assessment using deep learning by E. Tennant, S. F. Jenkins, V. Miller, R. Robertson, B. Wen, S.-H. Yun, B. Taisne

“…The trained models were incorporated into a pipeline along with all the necessary image processing steps to generate spatial data (a georeferenced vector with damage state attributes) for rapid tephra fall building damage mapping. …”
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机器学习算法在食用植物油掺伪鉴别中应用的 研究进展Research progress on application of machine learning algorithms in adulteration identification of edible vegetable oils... by 吕壮，黄金，兰梓溶，代婷玉，许宙，陈茂龙，焦叶，文李，程云辉，丁利 LYU Zhuang, HUANG Jin, LAN Zirong, DAI Tingyu, XU Zhou, CHEN Maolong, JIAO Ye, WEN Li, CHENG Yunhui, DING Li

“…In order to provide a theoretical basis for the selection of algorithms in the adulteration identification of edible vegetable oils, the classification of machine learning algorithms and their application process in the adulteration identification of edible vegetable oils were briefly introduced. …”
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Skin Hydration Monitoring Using a Microwave Sensor: Design, Fabrication, and In Vivo Analysis by Shabbir Chowdhury, Amir Ebrahimi, Kamran Ghorbani, Francisco Tovar-Lopez

“…This advancement holds significant potential for skincare and biomedical applications, enabling detection without complex signal processing.…”
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Predicting distant metastasis of bladder cancer using multiple machine learning models: a study based on the SEER database with external validation by Xin Chang Zou, Xue Peng Rao, Jian Biao Huang, Jie Zhou, Hai Chao Chao, Tao Zeng

“…Based on the significant features identified, three ML algorithms were utilized to develop prediction models: logistic regression, support vector machine (SVM), and linear discriminant analysis (LDA). …”
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Development of hybrid stacking machine learning for evaluating parameters affecting refrigerated shrimp coated with chitosan-loaded Salvia officinalis nanoemulsions by Mehran Sayadi, Elahe Abedi, Najmeh Oliyaei, Maryam Mousavifard

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Identification and validation of endoplasmic reticulum autophagy-related potential biomarkers in periodontitis by Ruyue Wang, Jinyue Hu, Qing Sun, Shuixiang Guo, Gege Zhang, Ao Lu, Shuo Liu, Xue Yang, Lina Wang

“…Random forest, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature removal (SVM-RFE) algorithms were used to identify hub genes. …”
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An Explainable Machine Learning Approach for IoT-Supported Shaft Power Estimation and Performance Analysis for Marine Vessels by Yiannis Kiouvrekis, Katerina Gkirtzou, Sotiris Zikas, Dimitris Kalatzis, Theodor Panagiotakopoulos, Zoran Lajic, Dimitris Papathanasiou, Ioannis Filippopoulos

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Advancing patient care with AI: a unified framework for medical image segmentation using transfer learning and hybrid feature extraction by Nazife Çevik, Taner Çevik, Onur Osman, Shtwai Alsubai, Jawad Rasheed, Jawad Rasheed, Jawad Rasheed

“…Classification was performed using Support Vector Machines (SVM), and results were evaluated based on accuracy, recall (sensitivity), specificity, and the F-measure, alongside bias-variance analysis for model generalization capability.ResultsU-Net segmentation achieved high accuracy across datasets, with particularly notable results for polyps (98.00%) and brain tumors (99.66%). …”
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Coupled intelligent prediction model for medium- to long-term runoff based on teleconnection factors selection and spatial-temporal analysis. by Jintao Li, Ping Ai, Chuansheng Xiong, Yanhong Song

“…However, runoff formation is a complex process influenced by various natural and anthropogenic factors, resulting in nonlinearity, nonstationarity, and long prediction periods, which complicate forecasting efforts. …”
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