Comparing Geodesic Filtering to State-of-the-Art Algorithms: A Comprehensive Study and CUDA Implementation by Pierre Boulanger, Sadid Bin Hasan

“…Additionally, we present a highly optimized GPU implementation featuring innovative wave-propagation algorithms and memory access optimization techniques that achieve a 200× speedup, making geodesic filtering practical for real-time applications. …”
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Water quality prediction and carbon reduction mechanisms in wastewater treatment in Northwest cities using Random Forest Regression model by Jingjing Sun, Xin Guan, Xiaojun Sun, Xiaojing Cao, Yepei Tan, Jiarong Liao

“…The RFR algorithm integrates Bagging ensemble learning and random subspace theory to construct multiple decision trees and aggregate their predictions, thereby enhancing the model’s prediction accuracy and stability. …”
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Dual-Closed-Loop Control System for Polysilicon Reduction Furnace Power Supply Based on Hysteresis PID and Predictive Control by Shihao Li, Tiejun Zeng, Shan Jian, Guiping Cui, Ziwen Che, Genghong Lin, Zeyu Yan

“…In the power system of a polysilicon reduction furnace, especially during the silicon rod growth process, the issue of insufficient temperature control accuracy arises due to the system’s nonlinear and time-varying characteristics. …”
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Design of weighted based divided-search enhanced Karnik–Mendel algorithms for type reduction of general type-2 fuzzy logic systems by Yang Chen

“…Here the weighted type-reduction algorithms based on the Newton and Cotes quadrature formulas of numerical methods of integration technique are first given, and the searching spaces are divided. …”
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GPS Spoofing Detection using CAF Images and Neural Networks Based on the Proposed Peak Mapping Dimensionality Reduction Algorithm and TCNN Model by M. J. Jahantab, S. Tohidi, Mohammad Reza Mosavi, Ahmad Ayatollahi

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Joint reduction of peak-to-average power ratio and out-of-band power based on subcarrier weighting in OFDM systems by Qing LI, Han-ying HU

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Research on Adaptive Education Path Dynamic Programming Algorithm Based on Reinforcement Learning and Cognitive Graphs by Hongli Lou, Pin Yue, Jianwen Chen

“…The proposed method combines dynamic learner models, real-time performance tracking, and predictive analytics, allowing for a highly adaptive educational system. …”
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Predictive analytics in gamified education: A hybrid model for identifying at-risk students by Devanshu Sawarkar, Latika Pinjarkar, Pratham Agrawal, Devansh Motghare

“…This research proposes a hybrid predictive model designed to identify at-risk students within a gamified education environment accurately. …”
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Development and validation of a deep learning algorithm for prediction of pediatric recurrent intussusception in ultrasound images and radiographs by Yu-feng Qian, Wan-liang Guo

“…Conclusions Deep learning algorithms developed using multimodal medical imaging may help predict recurrent intussusception. …”
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Retracted: Intelligent Recommendation Algorithm of Multimedia English Distance Education Resources Based on User Model by Journal of Mathematics

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AI optimization algorithms enhance higher education management and personalized teaching through empirical analysis by XiWen Xu

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Retracted: International Chinese Education Expert System Based on Artificial Intelligence and Machine Learning Algorithms by Journal of Mathematics

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Validation of a teaching model instrument for university education in Ecuador through an artificial intelligence algorithm by Jenniffer Sobeida Moreira-Choez, Tibisay Milene Lamus de Rodríguez, Aracelly Fernanda Núñez-Naranjo, Ángel Ramón Sabando-García, María Belén Reinoso-Ávalos, Cynthia Michel Olguín-Martínez, Daniel Omar Nieves-Lizárraga, Julieta Elizabeth Salazar-Echeagaray

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ALGORITHM FOR MASTERING THE PROCESS OF SKETCHES AND DRAFTS OF THE HUMAN FIGURE FOR STUDENTS OF HIGHER EDUCATION IN ART SPECIALTIES by І. Гуржій, Д. Коршунов

“…In the process of theoretical generalization of one's own artistic and pedagogical experience, an algorithm for mastering sketches and drafts of the human figure is developed, described and illustrated. …”
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Improving lameness detection in cows: A machine learning algorithm application by Elma Dervić, Caspar Matzhold, Christa Egger-Danner, Franz Steininger, Peter Klimek

“…A Random Forest classifier, using input features selected by the Boruta algorithm, was used for the prediction task; effects of individual features were further assessed using partial dependence plots. …”
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Comparative analysis of impact of classification algorithms on security and performance bug reports by Said Maryyam, Bin Faiz Rizwan, Aljaidi Mohammad, Alshammari Muteb

“…The aim of this research is to compare and analyze the prediction accuracy of machine learning algorithms, i.e., Artificial neural network (ANN), Support vector machine (SVM), Naïve Bayes (NB), Decision tree (DT), Logistic regression (LR), and K-nearest neighbor (KNN) to identify security and performance bugs from the bug repository. …”
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AEA-YOLO: Adaptive Enhancement Algorithm for Challenging Environment Object Detection by Abdulrahman Kariri, Khaled Elleithy

“…To address these issues, we provide an Adaptive Enhancement Algorithm YOLO (AEA-YOLO) framework that allows for an enhancement in each image for improved detection capabilities. …”
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Construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithms by ZHANG Min, GU Tingting, GUAN Wei, LIU Xiangxiang, SHI Junyao

“…Objective To identify risk factors for perimenopausal syndrome (PMS) among perimenopausal women using machine learning algorithms, and to construct a predictive model for the risk of developing moderate to severe PMS in perimenopausal women. …”
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Explainable and Interpretable Model for the Early Detection of Brain Stroke Using Optimized Boosting Algorithms by Yogita Dubey, Yashraj Tarte, Nikhil Talatule, Khushal Damahe, Prachi Palsodkar, Punit Fulzele

“…<b>Results:</b> The performance of three boosting algorithms is studied for stroke prediction, which include Gradient Boosting (GB), AdaBoost (ADB), and XGBoost (XGB) with XGB achieved the best outcome overall with a training accuracy of 96.97% and testing accuracy of 92.13%. …”
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Dye-cleaning prediction with a variant of nature-inspired algorithms coupled with extreme gradient boosting by Tiyasha Tiyasha, Chijioke Elijah Onu, Mohamed A. Ismail, Rama Rao Karri, Abdelfattah Amari, Vinay Kumar, Suraj Kumar Bhagat

“…Hyperparameter tuning via differential evolution (DE), genetic algorithm (GA), random search (RS), and grid search (GS) with the XGBoost model was conducted to achieve more accurate results. …”
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