Urban Microcirculation Traffic Network Planning Method Based on Fast Search Random Tree Algorithm by Ning YU

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Predicting Ocean Current Temperature Off the East Coast of America with XGBoost and Random Forest Algorithms Using Rstudio by Lulut Alfaris, Anas Noor Firdaus, Ukta Indra Nyuswantoro, Ruben Cornelius Siagian, Aldi Cahya Muhammad, Rohana Hassan, Rodulfo T. Aunzo, Jr., Reza Ariefka

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Automated guided vehicle (AGV) path optimization method based on improved rapidly-exploring random trees by Zhigang Ren, Anjiang Cai, Feilong Xu

“…In response to the issues of low computational efficiency, slow convergence speed, curvy paths, and the tendency to fall into local optima in rapidly-exploring random tree (RRT) algorithms for automated guided vehicle (AGV) path planning, this article proposes an improved RRT algorithm that combines adaptive step-size optimization with K-dimensional tree (KD-Tree) based fast nearest neighbor search. …”
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Key technologies for path planning of coal mine wheeled robots integrating fast traversal random trees and Q-reinforcement learning by Tianfei WEN, Yu GAO, Quan WANG, Chuang YANG

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Research on vehicle scheduling for forest fires in the northern Greater Khingan Mountains by Jie Zhang, Junnan He, Shihao Ren, Pei Zhou, Jun Guo, Mingyue Song

“…Improvement of ordinary genetic algorithm, design of double population strategy selection operation, the introduction of chaotic search initialization population, to improve the algorithm’s solution efficiency and accuracy, through the northern pristine forest area of Daxing’anling real forest fire cases and generation of large-scale random fire point simulation experimental test to verify the effectiveness of the algorithm, to ensure that the effectiveness and reasonableness of the solution to the problem of forest fire emergency rescue vehicle scheduling program. …”
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A Linear Fitting Algorithm Based on Modified Random Sample Consensus by Yujin Min, Yun Tang, Hao Chen, Faquan Zhang

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Comparison of KNN and Random Forest Algorithms on E-Commerce Service Chatbot by Fardan Zamakhsyari, Bagas Adi Makayasa, R. Abudullah Hamami, Muhammad Tulus Akbar, Andi Cahyono, Amirullah Amirullah, Muhammad Zida Hisyamuddin, Maria Ulfah Siregar

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Novel Hybrid Feature Selection Using Binary Portia Spider Optimization Algorithm and Fast mRMR by Bibhuprasad Sahu, Amrutanshu Panigrahi, Abhilash Pati, Manmath Nath Das, Prince Jain, Ghanashyam Sahoo, Haipeng Liu

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Assessing the Effect of Data Augmentation on Occluded Frontal Faces Using DWT-PCA/SVD Recognition Algorithm by Louis Asiedu, Joseph Agyapong Mensah, Francis Ayiah-Mensah, Felix O. Mettle

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TS-SSA: An improved two-stage sparrow search algorithm for large-scale many-objective optimization problems. by Xiaozhi Du, Kai Chen, Hongyuan Du, Zongbin Qiao

“…Large-scale many-objective optimization problems (LSMaOPs) are a current research hotspot. However, since LSMaOPs involves a large number of variables and objectives, state-of-the-art methods face a huge search space, which is difficult to be explored comprehensively. …”
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Soft detection model of corrosion leakage risk based on KNN and random forest algorithms by Yang YANG, Chengzhi LI, Xuan DU, Xiao YU, Shaohua DONG

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A Hybrid Strategy Two‐Dimensional Concrete Aggregate Filling Algorithm by Xu Tian, Jun Ao, ChunBo Ma, JunJie Shi, HanJun Guo

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COMPARISON OF RANDOM FOREST AND NAÏVE BAYES METHODS FOR CLASSIFYING AND FORECASTING SOIL TEXTURE IN THE AREA AROUND DAS KALIKONTO, EAST JAVA by Henny Pramoedyo, Danang Ariyanto, Novi Nur Aini

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Machine Learning Algorithms Performance Evaluation for Intrusion Detection by Shyla ., Kapil Kumar, Vishal Bhatnagar

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A stacked ensemble machine learning model for the prediction of pentavalent 3 vaccination dropout in East Africa by Meron Asmamaw Alemayehu, Shimels Derso Kebede, Agmasie Damtew Walle, Daniel Niguse Mamo, Ermias Bekele Enyew, Jibril Bashir Adem

“…The objective is to identify predictors of dropout and enhance intervention strategies.MethodsThe study utilized seven base machine learning algorithms to create a stacked ensemble model with three meta-learners: Random Forest (RF), Generalized Linear Model (GLM), and Extreme Gradient Boosting (XGBoost). …”
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Ensemble machine learning (EML) based regional flood frequency analysis model development and testing for south-east Australia by Nilufa Afrin, Ataur Rahman, Ahmad Sharafati, Farhad Ahamed, Khaled Haddad

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NIRS identification of cashmere and wool fibers based on spare representation and improved AdaBoost algorithm by Zhu Yaolin, Li Zheng, Chen Xin, Chen Jinni, Zhang Hongsong

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Predicting diabetes using supervised machine learning algorithms on E-health records by Sulaiman Afolabi, Nurudeen Ajadi, Afeez Jimoh, Ibrahim Adenekan

“…Methods: This study investigates the early detection and management of diabetes by applying machine learning techniques to electronic health records. The research explores the effectiveness of three supervised machine learning algorithms: logistic regression, Random Forest, and k-nearest neighbors (KNN), in developing predictive models for diabetes. …”
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Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method by Behnam Seyedi, Octavian Postolache

“…The second phase focuses on optimal feature selection using a Genetic Algorithm enhanced with eagle-inspired search strategies. …”
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Enhanced Automatic Generation Control in Multiarea Power Systems: Crow Search Optimized Cascade FOPI‐TIDDN Controller With Integrated Renewable Solar Thermal Models and HVDC Lines... by Naladi Ram Babu, Pamarthi Sunitha, Ganesh Pardhu B. S. S., Sanjeev Kumar Bhagat, Adireddy Ramesh, Arindita Saha, Wulfran Fendzi Mbasso, Pradeep Jangir, Ahmed Hossam‐Eldin

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