Determination of indications for the choice of the preparation method of the root canal system with rotation and reciprocation instruments by I. V. Pyatkova, L. Yu. Orekhova, T. V. Porkhun, A. V. Silin

“…Compliance with the algorithms of endodontic treatment in most cases ensures the quality of treatment. …”
Get full text

Landslide Hazard Assessment Under Record-Breaking Extreme Rainfall: Integration of SBAS-InSAR and Machine Learning Models by Wenbo Zheng, Wen Fan, Yanbo Cao, Yalin Nan, Pengxu Jing

“…Among these, the DNN model exhibited the highest performance, achieving an AUC of 0.82 and Kappa coefficients of 0.833 (training) and 0.812 (prediction). Further analysis using SHAP values identified distance to rivers, cumulative rainfall, and the Topographic Wetness Index (TWI) as the most influential factors governing landslide occurrence under extreme rainfall conditions. …”
Get full text

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study by Cristina Tavares, Nathalia Nascimento, Paulo Alencar, Donald Cowan

“…In general, although the current literature provides a wide variety of ML techniques and algorithms, there is a lack of design approaches to support algorithm selection. …”
Get full text

Integrating bioinformatics and machine learning to investigate the mechanisms by which three major respiratory infectious diseases exacerbate heart failure by Yiding Yu, Quancheng Han, Juan Zhang, Jingle Shi, Huajing Yuan, Yitao Xue, Yan Li

“…DSigDB prediction results showed that 6 drugs such as acetohexamide may have potential therapeutic effects on HF aggravated by respiratory diseases. …”
Get full text

Notice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion by Ma Jingyi, Tiejun Zhang, Jing Guodong, Yan Wenjun, Yang Bin

“…The recognition of ground-based cloud images has rich application prospects in many aspects such as weather prediction, astronomical site selection, and meteorological observation. …”
Get full text

An Overview of Deep Learning Applications in Groundwater Level Modeling: Bridging the Gap between Academic Research and Industry Applications by Ahmed Shakir Ali Ali, Farhad Jazaei, Peyman Babakhani, Muhammad Masood Ashiq, Alireza Bakhshaee, Brian Waldron

“…As a critical component of sustainable water management, groundwater level prediction plays a vital role in mitigating droughts and ensuring adequate water supply. …”
Get full text

Detection of Malicious Office Open Documents (OOXML) Using Large Language Models: A Static Analysis Approach by Jonas Heß , Kalman Graffi

“…Existing machine learning approaches are capable of identifying sophisticated features that enable the prediction of a file’s nature, achieving sufficient results on existing samples. …”
Get full text

Integrating direct observation and environmental DNA data to enhance species distribution models in riverine environments by Luca Carraro

“…When eDNA and direct sampling sites are spatially arranged in an optimized fashion, the highest prediction skill for a fixed total number of sampling sites deployed is reached when both data types are included in the model fitting. …”
Get full text

The negative linear relationship between oxidative balance scores and constipation: a cross-sectional study from NHANES 2005–2010 by Liqian Xuan, Yang Chen, Chang Liu, Yahui Dai

“…The receiver operating characteristic analysis indicated that OBS had a good prediction efficiency on constipation, especially in participants without diabetes and with the status of married or living with a partner. …”
Get full text

Dynamic Monitoring and Precision Fertilization Decision System for Agricultural Soil Nutrients Using UAV Remote Sensing and GIS by Xiaolong Chen, Hongfeng Zhang, Cora Un In Wong

“…Real-time data processing algorithms enable rapid updates of soil nutrient status, while a time-series dynamic model captures seasonal variations and crop growth stage influences, improving prediction accuracy (RMSE reductions of 43–70% for nitrogen, phosphorus, and potassium compared to conventional laboratory-based methods and satellite NDVI approaches). …”
Get full text

Progress on the world’s primate hotspots and coldspots: modeling ensemble super SDMs in cloud-computers based on digital citizen-science big data and 200+ predictors for more susta... by Moriz Steiner, Falk Huettmann

“…Results We present the first quantified pixel-based global primate hotspot prediction for Central and Northern South America, West Africa, East Africa, Southeast Asia, Central Asia, and Southern Africa. …”
Get full text

Classification and Recognition of Soybean Quality Based on Hyperspectral Imaging and Random Forest Methods by Man Chen, Zhichang Chang, Chengqian Jin, Gong Cheng, Shiguo Wang, Youliang Ni

“…The model parameters were optimized using particle swarm optimization (PSO) and differential evolution (DE) algorithms to improve performance. Experimental results showed that the RF classification model based on SPA-BC preprocessed spectra and DE-tuned parameters achieved an optimal prediction accuracy of 1.0000 during training. …”
Get full text

Fostering non-intrusive load monitoring for smart energy management in industrial applications: an active machine learning approach by Lukas Fabri, Daniel Leuthe, Lars-Manuel Schneider, Simon Wenninger

“…Our results indicate that our model reduces the user input, i.e., the labeled data, by up to 99% while achieving between 62 and 80% of the prediction accuracy compared to the benchmark with 100% labeled training data. …”
Get full text

A Contrastive-Augmented Memory Network for Anti-UAV Tracking in TIR Videos by Ziming Wang, Yuxin Hu, Jianwei Yang, Guangyao Zhou, Fangjian Liu, Yuhan Liu

“…The proposed CAMTracker conducts tracking through a two-stage scheme, searching for possible candidates in the first stage and matching the candidates with the template for final prediction. In the first stage, an instance-guided region proposal network (IG-RPN) is employed to calculate the correlation features between the templates and the searching images and further generate candidate proposals. …”
Get full text

Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators by Alexander Y. Sun, Peishi Jiang, Pin Shuai, Xingyuan Chen

“…Our approach is built upon DeepONet, a specific deep neural operator, and is designed to address several common problems in hydrology, namely, model parameter estimation, prediction at ungaged locations, and uncertainty quantification. …”
Get full text

Recent technical advancements and clinical applications of MR-guided radiotherapy in lung cancer treatment by Chi Ma, Xiao Wang, Ke Nie, Zhenyu Xiong, Keying Xu, Ning Yue, Yin Zhang

“…Recent technological innovations, including AI-powered auto-contouring algorithms, deep-learning (DL) based prediction models, and adaptive treatment strategies, further optimize MRgRT by improving workflow efficiency and reducing treatment time. …”
Get full text

Student employment forecasting model based on random forest and multi-features fusion by Zhenguo Xing, Xiao Wu, Jiangjiang Li

“…Secondly, in order to improve the accuracy of the prediction model, a feature selection model combining principal component analysis and random forest algorithm is used to select the optimal subset from the original features. …”
Get full text

Automatic Extraction and Visualization of Interaction Networks for German Fairy Tales by David Schmidt, Frank Puppe

“…After an evaluation of the individual algorithms, the predicted networks are evaluated against benchmarks established by networks based on manually annotated coreference and speaker/addressee information. …”
Get full text

Leveraging machine learning to identify determinants of zero utilization of maternal continuum of care in Ethiopia: Insights from SHAP analysis and the 2019 mini DHS. by Shimels Derso Kebede, Agmasie Damtew Walle, Daniel Niguse Mamo, Ermias Bekele Enyew, Jibril Bashir Adem, Meron Asmamaw Alemayehu

“…SHAP analysis was used to interpret model predictions and identify key predictors. lightGBM demonstrated robust performance with an accuracy of 84.47%, an AUC of 0.93, a recall of 0.80, a precision of 0.95, and an F1-score of 0.87 on test data. …”
Get full text

Single-cell and machine learning approaches uncover intrinsic immune-evasion genes in the prognosis of hepatocellular carcinoma by Jiani Wang, Xiaopeng Chen, Donghao Wu, Changchang Jia, Qinghai Lian, Yuhang Pan, Jiumei Yang

“…Time-dependent receiver operating characteristic analysis demonstrated the accurate predictive capability of the risk score model for HCC prognosis. …”
Get full text