A new aggregation and riming discrimination algorithm based on polarimetric weather radars by A. Blanke, M. Gergely, S. Trömel, S. Trömel

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Graph Knowledge-Enhanced Iterated Greedy Algorithm for Hybrid Flowshop Scheduling Problem by Yingli Li, Biao Zhang, Kaipu Wang, Liping Zhang, Zikai Zhang, Yong Wang

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AI-Based Prediction of Visual Performance in Rhythmic Gymnasts Using Eye-Tracking Data and Decision Tree Models by Ricardo Bernardez-Vilaboa, F. Javier Povedano-Montero, José Ramon Trillo, Alicia Ruiz-Pomeda, Gema Martínez-Florentín, Juan E. Cedrún-Sánchez

“…Background/Objective: This study aims to evaluate the predictive performance of three supervised machine learning algorithms—decision tree (DT), support vector machine (SVM), and k-nearest neighbors (KNN) in forecasting key visual skills relevant to rhythmic gymnastics. …”
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Algorithms Facilitating the Observation of Urban Residential Vacancy Rates: Technologies, Challenges and Breakthroughs by Binglin Liu, Weijia Zeng, Weijiang Liu, Yi Peng, Nini Yao

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Optimal by criterion of minimum of average risk adaptive device for decision meaning considering correlation properties of the reflected signal by S. A. Gabets, S. U. Sedushev, R. A. Hutsau

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Metro Station Passenger Volume Prediction Algorithm Based on Improved LSTNet Model by XU Ling, GUAN Jianbo, XU Xiwei, BAN Yong

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Cooperative Data Processing Algorithm Based on Mobile Agent in Wireless Sensor Networks by Shukui Zhang, Yong Sun, Jianxi Fan, He Huang

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A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context by Guangxia Xu, Zhijing Tang, Chuang Ma, Yanbing Liu, Mahmoud Daneshmand

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African buffalo algorithm: Training the probabilistic neural network to solve classification problems by Mohammed Alweshah, Lobna Rababa, Mohammed Hashem Ryalat, Ammar Al Momani, Mohamed F. Ababneh

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Food and Beverage Menu Package Recommendation System Using Fp-Growth Algorithm by Aldi Nanda Pratama, Catur Eri Gunawan, Muhamad Son Muarie

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Algorithm for investigating risk analysis and factors affecting suicidal attempts under uncertainty by RuiHua Liang, Ni Duan, XueJing Liu, Chuanqin Liu

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LSTM-DQN-APF Path Planning Algorithm Empowered by Twins in Complex Scenarios by Ying Lu, Xiaodan Wang, Yang Yang, Man Ding, Shaochun Qu, Yanfang Fu

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Effective Breast Cancer Classification Using Deep MLP, Feature-Fused Autoencoder and Weight-Tuned Decision Tree by Nagham Rasheed Hameed Alsaedi, Mehmet Fatih Akay

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CKRT coagulation risk prediction and nursing feedback model based on intelligent algorithms by Xianrong Xu, Mou Chen, Lvjing Chen, Kaixing Huang, Shiqi Cao, Wenwen Gao, Kang Liu, Buyun Wu, Huijuan Mao

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Oil and Gas Industry Companies’ Classification and Ordering by the Level of Investment Attractiveness Algorithm by Chernova Natalia L., Serhiienko Olena A., Huz Ostap B.

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Modern Approach in Pattern Recognition Using Circular Fermatean Fuzzy Similarity Measure for Decision Making with Practical Applications by Revathy Aruchsamy, Inthumathi Velusamy, Prasantha Bharathi Dhandapani, Suleman Nasiru, Christophe Chesneau

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Evaluation of four clinical decision rules in children with minor head trauma: NEXUS II, PECARN, CHALICE, and CATCH by Majid Zamani, Farhad Heydari, Farzin Feyzollahi, Mehrdad Esmaillian, Amir Bahador Boroumand

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Development and validation of a decision tree model for prediction of insomnia risk among ischemic stroke convalescence patients by Xuefeng Sun, Zilin Wang, Yuqing Song, Deyu Cong, Shu Sun, Xinye Zhang, Ye Zhang, Hongshi Zhang

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Penerapan Decision Tree dan Random Forest dalam Deteksi Tingkat Stres Manusia Berdasarkan Kondisi Tidur by Sza Sza Amulya Larasati, Elok Nuraida Kusuma Dewi, Brahma Hanif Farhansyah, Fitra Abdurrachman Bachtiar, Fajar Pradana

“…Hyperparameter tuning is done using k-fold cross-validation, and the model is designed using the Decision Tree and Random Forest classification algorithms. …”
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Developing interpretable machine learning models to predict length of stay and disposition decision for adult patients in emergency departments by Abhishek Sharma, Timothy N Fazio, Long Song, Samantha Plumb, Uwe Aickelin, Mojgan Kouhounestani, Mark John Putland

“…This framework was designed to be easily adapted by other institutions for the development of their own ML models.Methods We analysed data from 297 392 ED visits of patients aged 18 and above at a quaternary hospital between 30 June 2019 and 31 December 2022. Eight ML algorithms were evaluated, and ultimately, twelve lasso models built from 21 features were trained to predict four outcomes of LOS and DD at three time points post-triage. …”
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