Replicating Current Procedural Terminology code assignment of rhinology operative notes using machine learning by Christopher P. Cheng, Ryan Sicard, Dragan Vujovic, Vikram Vasan, Chris Choi, David K. Lerner, Alfred‐Marc Iloreta

“…Text was preprocessed and then vectorized using CountVectorizer (CV), term frequency‐inverse document frequency, and Word2Vec. The Decision Tree, Support Vector Machine, Logistic Regression and Naïve Bayes (NB) algorithms were used to train and test models on operative notes. …”
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A framework for scalable ambient air pollution concentration estimation by Liam J. Berrisford, Lucy S. Neal, Helen J. Buttery, Benjamin R. Evans, Ronaldo Menezes

“…The machine learning framework used is LightGBM, a gradient boosting algorithm based on decision trees, for efficient and scalable modeling. …”
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Smart UAV-assisted rose growth monitoring with improved YOLOv10 and Mamba restoration techniques by Fan Zhao, Zhiyan Ren, Jiaqi Wang, Qingyang Wu, Dianhan Xi, Xinlei Shao, Yongying Liu, Yijia Chen, Katsunori Mizuno

“…We introduced MambaIR, a novel SRR algorithm based on selective state-space models, which significantly outperforms existing methods in enhancing low-resolution UAV imagery (PSNR: 28.34 dB, SSIM: 77.07 %). …”
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Enhancing MANET Security Through Federated Learning and Multiobjective Optimization: A Trust-Aware Routing Framework by Saad M. Hassan, Mohd Murtadha Mohamad, Farkhana Binti Muchtar, Firoz Bin Yusuf Patel Dawoodi

“…MOO, particularly nondominated sorting genetic algorithm III, effectively balances conflicting network objectives, offering a 15% improvement in overall network performance compared with single-objective approaches. …”
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A hybrid ultra-short-term photovoltaic power prediction framework integrating ant colony optimization for clustering with Bi-GRU by Xinyi Liu, Zitao Wang, Yang Wang, Shanke Liu, Lijun Yu

“…This hybrid framework employs an improved Ant Colony Optimization algorithm fused with K-Means pre-clustering (K-MACO) to perform unsupervised learning on samples within the physical feature space of radiation patterns, humidity, and temperature dynamics, classifying weather scenarios into sunny, cloudy, and rainy types. …”
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PARAMETRIC SYNTHESIS OF MODELS FOR MULTICRITERIAL ESTIMATION OF TECHNOLOGICAL SYSTEMS by Vladimir Beskorovainyi

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A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction by Saba Taheri Kadkhoda, Babak Amiri

“…Five classification algorithms are employed to forecast financial distress across three scenarios. …”
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Computational models based on machine learning and validation for predicting ionic liquids viscosity in mixtures by Bader Huwaimel, Jowaher Alanazi, Muteb Alanazi, Tareq Nafea Alharby, Farhan Alshammari

“…The study focuses on crucial input parameters, namely the type of cation, the type of anion, the temperature (measured in Kelvin), and the concentration of the ionic liquid (expressed in mol%). This study assesses three influential machine learning algorithms that are based on the Decision Tree methodology. …”
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Comparison of Machine Learning Methods for Predicting Electrical Energy Consumption by Retno Wahyusari, Sunardi Sunardi, Abdul Fadlil

“…Data pre-processing, specifically min-max normalization, is crucial for improving the accuracy of distance-based algorithms like KNN. Decision tree-based algorithms like RF and CatBoost are less sensitive to data normalization. …”
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XAI for Point Cloud Data Using Perturbations Based on Meaningful Segmentation by Raju Ningappa Mulawade, Christoph Garth, Alexander Wiebel

“…This allows the users to analyze the performance of AI algorithms better and make appropriate decisions based on that analysis. …”
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Acute kidney injury, the present on admission (POA) indicator and sex disparities: observational study of inpatient real-world data in a Swiss tertiary healthcare system. Explorati... by Michael Simon, Sarah N Musy, Karen Triep, Olga Endrich

“…At the largest Swiss provider of inpatient acute healthcare, a clinic decision support algorithm ensures exact staging of KD (2012 KDIGO Kidney Disease Improving Global Outcomes Clinical Practice Guideline). …”
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Multiparametric radiomics signature for predicting molecular genotypes in adult-type diffuse gliomas utilizing 18F-FET PET/MRI by Jie Bai, Bixiao Cui, Fengqi Li, Xin Han, Hongwei Yang, Jie Lu

“…Each participant underwent hybrid PET/MRI scans, including FLAIR, 3D T1-CE, apparent diffusion coefficient (ADC), and 18F-FET PET. …”
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Predicting the subclinical carotid atherosclerosis in overweight and obese patients using a machine learning model by D. V. Gavrilov, T. Yu. Kuznetsova, M. A. Druzhilov, I. N. Korsakov, A. V. Gusev

“…When creating the model, 3 Random Forest algorithms, AdaBoostClassifier, KNeighborsClassifier and the Scikit-learn library were used. …”
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How to personalise cognitive–behavioural therapy for chronic primary pain using network analysis: study protocol for a single-case experimental design with multiple baselines by Julia Anna Glombiewski, Verena Eunike Hofmann, Felicitas Kininger, Saskia Scholten

“…After a second, randomised baseline (phase A'), participants will receive 1 out of 10 CBT interventions, selected by an algorithm matching targets to interventions, in up to 10 sessions (phase C). …”
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Machine learning and AVO class II workflow for hydrocarbon prospectivity in the Messinian offshore Nile Delta Egypt by Nadia Abd-Elfattah, Aia Dahroug, Manal El Kammar, Ramy Fahmy

“…This method is particularly useful for identifying low seismic amplitude anomalies, which are often challenging to detect with conventional seismic analysis. (1) This study developed a workflow to detect low seismic amplitude gas fields in near-field exploration. (2) It uses a machine learning algorithm to classify and explore low-seismic-amplitude gas sand reservoirs. (3) This approach helps estimate the likelihood of success and reduces the risk associated with hydrocarbon exploration wells.…”
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Advanced GIS-based Multi-Function Support System for Identifying the Best Route by Suhiar Mohammed Zeki Abd Alsammed

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Detection of child depression using machine learning methods. by Umme Marzia Haque, Enamul Kabir, Rasheda Khanam

“…As a result, our objective is to 1) create a model that can predict depression in children and adolescents aged 4-17 years old, 2) evaluate the results of ML algorithms to determine which one outperforms the others and 3) associate with the related issues of family activities and socioeconomic difficulties that contribute to depression.…”
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Assessment of the level of ICT development in the regions of Kazakhstan in the transition to Industry 4.0 by A. A. Kireyeva, M. T. Urdabayev, D. D. Yermekbayeva

“…In this scientific study, the authors proposed a methodological approaches (determining the scale and level of ICT development), which performed according to an algorithm consisting of three stages: assessment of the level of Internet use (the share of Internet users); assessment of the level of costs for the production of ICT; assessment of the level of digital literacy of the population (readiness of the population to widespread use of ICTs). …”
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ScalEdge: A framework for scalable edge computing in Internet of things–based smart systems by Mohammad Babar, Muhammad Sohail Khan

“…The proposed energy-efficient framework for IoT algorithm makes efficient computation offload decisions while considering energy and latency constraints. …”
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A framework of crop water productivity estimation from UAV observations: A case study of summer maize by Minghan Cheng, Ni Song, Josep Penuelas, Matthew F. McCabe, Xiyun Jiao, Yuping Lv, Chengming Sun, Xiuliang Jin

“…To address this challenge, our research develops an innovative UAV-based monitoring framework through systematic integration of long-term multispectral/thermal infrared observations with multi-model fusion: (1) Surface Energy Balance Algorithm for Land (SEBAL) and FAO-56 Penman-Monteith models for evapotranspiration (ET) estimation; (2) Random Forest algorithm incorporating four phenotypical growth indicators for yield estimation, ultimately enabling CWP quantification. …”
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