Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review by Shailesh S. Nayak, Saikiran Pendem, Girish R. Menon, Niranjana Sampathila, Prakashini Koteshwar

“…Various imaging modalities, including MRI, PET/CT, and advanced techniques like ASL and DTI, were utilized to extract radiomic features for analysis. Machine learning algorithms such as deep learning networks, support vector machines, random forests, and logistic regression were applied to develop predictive models. …”
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Integrating IoT sensors and machine learning for sustainable precision agroecology: enhancing crop resilience and resource efficiency through data-driven strategies, challenges, an... by Val Hyginus Udoka Eze, Esther Chidinma Eze, George Uwadiegwu Alaneme, Pius Erheyovwe BUBU, Ezekiel Oluwaseun Ejiofor Nnadi, Michael Ben Okon

“…Coupled with advanced ML algorithms, this data facilitates predictive analytics and real-time decision-making, optimizing resource utilization for irrigation, pest control, and yield prediction. …”
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Machine learning applications in the analysis of sedentary behavior and associated health risks by Ayat S Hammad, Ayat S Hammad, Ali Tajammul, Ismail Dergaa, Ismail Dergaa, Ismail Dergaa, Maha Al-Asmakh, Maha Al-Asmakh

“…The review highlights the utility of various ML approaches in classifying activity levels and significantly improving the prediction of sedentary behavior, offering a promising approach to address this widespread health issue.ConclusionML algorithms, including supervised and unsupervised models, show great potential in accurately detecting and predicting sedentary behavior. …”
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Detección y diagnóstico de fallas en motores mediante el análisis de vibraciones aplicando técnicas de inteligencia artificial. by Jair Elías Araujo Vargas, Dilan Yesid Franklin Coronel, Victor Manuel Arias Ruiz

“…Thus, artificial intelligence algorithms demonstrated high accuracy in fault detection and resolution, identifying various types of problems in a timely manner; the models contributed significantly to the overall analysis, offering a more reliable approach to predictive industrial maintenance, paving the way for future improvements and the adoption of new, more robust and adaptable algorithms.…”
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A new deep learning-based fast transcoding for internet of things applications by Jia Yang, Yonghong Peng, Linbo Qing, Yajuan Xue, Hong Yang

“…At the CU level, it reduces HEVC encoding complexity by accurately predicting CU partitions. At the PU level, predicting PU partition modes for non-split CUs further streamlines the encoding process. …”
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Identification and analysis of neutrophil extracellular trap-related genes in periodontitis via bioinformatics and experimental verification by Miao Yu, Zhenqi Ye, Zixin Ye, Yaping Wu, Xiang Wu

“…Then, machine learning algorithms were exploited to screen hub NRGs, and a predictive model was constructed based on these hub NRGs. …”
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Machine learning and thermodynamic modeling for optimizing hydrogen production via algae-biomass co-gasification by Thanadol Tuntiwongwat, Takashi Yukawa, Thongchai Rohitatisha Srinophakun, Kanit Manatura, Somboon Sukpancharoen, Seyedali Mirjalili

“…Three microalgae species (Chlorella vulgaris, Nannochloropsis oculata, Fucus serratus) were co-gasified with biomass feedstocks (Fir Pellet (FP), Palm Empty Fruit Bunch (PEFB), Pellet Pine Wood (PPW)) using Aspen Plus® simulation based on Gibbs free energy minimization. Six ML algorithms (XGB, RF, SVR, KNN, ANN, DT) with Shapley additive explanations (SHAP) analysis predicted H2 yield and syngas lower heating value (LHV) from 3609 data points across 24 input parameters. …”
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Construction and validation of HBV-ACLF bacterial infection diagnosis model based on machine learning by Neng Wang, Shuai Tao, Liang Chen

“…We utilized six machine learning algorithms—Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT)—to construct predictive models. …”
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Characterizing low femoral neck BMD in Qatar Biobank participants using machine learning models by Nedhal Al-Husaini, Rozaimi Razali, Amal Al-Haidose, Mohammed Al-Hamdani, Atiyeh M. Abdallah

“…Here we applied machine learning (ML) algorithms to predict low femoral neck BMD using standard demographic and laboratory parameters. …”
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Cetacean feeding modelling using machine learning: A case study of the Central-Eastern Mediterranean Sea by Carla Cherubini, Giulia Cipriano, Leonardo Saccotelli, Giovanni Dimauro, Giovanni Coppini, Roberto Carlucci, Carmelo Fanizza, Rosalia Maglietta

“…Behavioural data from April 2016 to October 2023, coupled with 20 environmental variables from Copernicus Marine Service and EMODnet-bathymetry datasets, were used to build Cetacean Feeding Models (CFMs) for the target species using Random Forest and RUSBoost algorithms. Multiple subsets of environmental predictors—physiographic, physical, inorganic, and bio-chemical—were employed to develop and evaluate ML models tailored to feeding prediction. …”
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Early Childhood Anemia in Ghana: Prevalence and Predictors Using Machine Learning Techniques by Maryam Siddiqa, Gulzar Shah, Mahnoor Shahid Butt, Asifa Kamal, Samuel T. Opoku

“…We used discrimination and calibration parameters to evaluate the performance of each machine learning algorithm. <b>Results</b>: Key predictors of childhood anemia are the father’s education, socioeconomic status, iron intake during pregnancy, the mother’s education, and the baby’s postnatal checkup within two months. …”
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Screening benzimidazole derivatives for atypical antipsychotic activity by K. Yu. Kalitin, O. Yu. Mukha, V. B. Voynov

“…The Neural Networks (MAE=0.019) and Random Forest (MAE=0.020) algorithms demonstrated the highest prediction performance. …”
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Identification of sepsis biomarkers through glutamine metabolism-mediated immune regulation: a comprehensive analysis employing mendelian randomization, multi-omics integration, an... by Zhuang’e Shi, Fuping Wang, Lishun Yang, Lishun Yang, Couwen Li, Bing Gong, Ruanxian Dai, Guobing Chen

“…The predictive models were constructed using the CatBoost, XGBoost, and NGBoost algorithms based on the data from GSE236713 and GSE28750. …”
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Research on Monitoring Nitrogen Content of Soybean Based on Hyperspectral Imagery by Yakun Zhang, Mengxin Guan, Libo Wang, Xiahua Cui, Yafei Wang, Peng Li, Shaukat Ali, Fu Zhang

“…Three spectral characteristic variables selection methods, including correlation coefficient analysis, stepwise regression, and spectral index analysis, were used to determine the spectral characteristic variables that are closely related to the soybean canopy nitrogen content. The predictive models for soybean canopy nitrogen content based on spectral characteristic variables were established using a multiple linear regression algorithm. …”
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A comprehensive review on safe reinforcement learning for autonomous vehicle control in dynamic environments by Rohan Inamdar, S. Kavin Sundarr, Deepen Khandelwal, Varun Dev Sahu, Nitish Katal

“…To operate safely in a dynamic environment, autonomous vehicles must possess the same level of predictive driving abilities as human drivers and must be capable of anticipating the future actions of other dynamic objects in the environment, especially those of neighboring vehicles. …”
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Blood pressure abnormality detection and interpretation utilizing explainable artificial intelligence by Hedayetul Islam, Md. Sadiq Iqbal, Muhammad Minoar Hossain

“…We have used several ML algorithms (extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), decision tree (DT), and logistic regression (LR)) to predict blood pressure abnormality based on patient's data. …”
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Simulation of bird’s symbiocenosis by A. M. Aramisov, M. K. Kozhokov, M. M. Shahmurzov, B. K. Laypanov, M. A. Koshokova, A. G. Bulatova

“…Logical-information modeling is not fully being implemented for the prediction of possible violations of the living ecosystems. …”
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Multi-task adversarial attribution method based on hierarchical structure by SUN Xu, ZHANG Wenqiong, LONG Xianzhong, LI Yun

“…This method simultaneously performed the attribution tasks of attack algorithms and victim models at different levels and employed hierarchical path prediction to learn the dependencies between these levels. …”
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The Line Pressure Detection for Autonomous Vehicles Based on Deep Learning by Xuexi Zhang, Ying Li, Ruidian Zhan, Jiayang Chen, Junxian Li

“…At present, the line pressure detection algorithms mainly include algorithms based on traditional features and models and algorithms based on deep learning. …”
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How we treat polycythemia vera by V. A. Shuvaev, I. S. Martynkevich

“…The article presents our own personalized algorithms for the diagnosis and treatment of polycythemia vera and the results of their use, demonstrating the possibility of a two-fold reduction in the incidence of thrombosis and an increase in overall survival.…”
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