Strength prediction and failure mode classification for SRC shear beams using GA-BP ANN method by Gangfeng Yao, Bingyi Li

“…Considering the advantages of machine-learning (ML) approaches, the back-propagation (BP) artificial neural network (ANN) method combined with genetic algorithm (GA) was employed to the prediction of strength and failure mode of SRC shear beams. …”
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Enhancing the prediction of vitamin D deficiency levels using an integrated approach of deep learning and evolutionary computing by Ahmed Alzahrani, Muhammad Zubair Asghar

“…Specifically, we employ a hybrid deep learning model that includes convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks to predict VDD data effectively. To improve the models effectiveness and guarantee the optimal choice of the features and hyper-parameters, we incorporate evolutionary computing methods, particularly genetic algorithms (GA). …”
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RSA modulus length regression prediction based on the Run Test and machine learning in the ciphertext-only scenarios by Ke Yuan, Chenmeng Zhao, Longwei Yang, Hanlin Sun, Sufang Zhou, Chunfu Jia

“…Abstract RSA is a classical public key cryptographic algorithm, over 40 years of widespread use has proven that its security is reliable when the key parameters are properly configured. …”
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Benchmarking Machine Learning Algorithms for Bearing Fault Classification Using Vibration Data: A Deployment-Oriented Study by Prasanta Kumar Samal, R. Srinidhi, Pramod Kumar Malik, H. J. Manjunatha, Imran M. Jamadar

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Optimization Design of Multi-Blade Centrifugal Fan Based on Variable Weight PSO-BP Prediction Model and Multi-Objective Beluga Optimization Algorithm by Wenyang Jin, Jiaxuan Wang, Junyu Li, Ren Xu, Ming Zhou, Qibai Huang

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Investigation of an Optimized Linear Regression Model with Nonlinear Error Compensation for Tool Wear Prediction by Lihua Shen, Baorui Du, He Fan, Hailong Yang

Subjects: “…tool wear prediction…”
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Editorial: Exploring machine learning applications in visceral surgery by Marialuisa Lugaresi, Christof Mittermair, Gabriel Sandblom

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Research on Path-Following Technology of a Single-Outboard-Motor Unmanned Surface Vehicle Based on Deep Reinforcement Learning and Model Predictive Control Algorithm by Bin Cui, Yuanming Chen, Xiaobin Hong, Hao Luo, Guanqiao Chen

“…This paper proposes a path-tracking control method for a single-outboard-motor USV based on a Deep Deterministic Policy Gradient (DDPG) algorithm and model predictive control (MPC) algorithm. …”
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A Predictive Model for the Shear Capacity of Ultra-High-Performance Concrete Deep Beams Reinforced with Fibers Using a Hybrid ANN-ANFIS Algorithm by Hossein Mirzaaghabeik, Nuha S. Mashaan, Sanjay Kumar Shukla

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Smart home power management algorithm using real-time model predictive control for a stand-alone PV system with battery energy storage by Aziz Watil

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Predictive modeling for mechanical characteristics of ultra high-performance concrete blended with eggshell powder and nano silica utilizing traditional technique and machine learning algorithm by Yi Zhang, Qizhi Zhang, Ali H. AlAteah, Ahmed Essam, Sahar A. Mostafa

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Predictive Performance of Anti-Lock Braking System with PID Controller Optimized by Gravitational Search Algorithm for a Quarter Car Model: Simulation Modeling and Control by Mohd Sabirin Rahmat, Fauzi Ahmad, Vimal Rau Aparow, Rizauddin Ramli, Sallehuddin Mohamed Haris, Mohd Anas Mohd Sabri, Mohd Anas Mohd Sabri, Meor Iqram Meor Ahmad, Mohd Muhyidin Mustafa

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Clustering of listed stock exchange companies active in the cement using the FPC clustering algorithm by Leila Safari-monjeghtapeh, Mansour Esmaeilpour

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The Local Path Planning Algorithm for Amphibious Robots Based on an Improved Dynamic Window Approach by Xiaoqiang Dai, Chengye Liu, Qiang Lai, Xin Huang, Qingjun Zeng, Ming Liu

“…The speed resolution adaptive adjustment algorithm improves the ability to pass through a complex multiple-obstacle area, and the dynamic obstacle prediction algorithm optimizes obstacle avoidance paths. …”
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Quality Evaluation Method for Base Baijiu Based on Support Vector Machine Optimized by Genetic and Bootstrap Aggregating Algorithm by PANG Tingting, ZHANG Guiyu, LIU Kecai, LI Xiaoping, TUO Xianguo, PENG Yingjie, ZENG Xianglin

Subjects: “…base baijiu; support vector machine; bootstrap aggregating; genetic algorithm; classification prediction…”
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Development and internal validation of a machine learning algorithm for the risk of type 2 diabetes mellitus in children with obesity by Jin-Xia Yang, Jin-Xia Yang, Yue Liu, Yue Liu, Rong Huang, Hai-ying Wu, Ya-yun Wang, Su-ying Cao, Guo-ying Wang, Jian-Min Zhang, Zi-Sheng Ai, Hui-min Zhou

“…Eight ML algorithms (Decision Tree, Logistic Regression, Support Vector Machine (SVM), Multilayer Perceptron, Adaptive Boosting, Random Forest, Gradient Boosting Decision Tree, and Extreme Gradient Boosting) were compared for their capacity to identify key clinical and laboratory characteristics of T2DM in children and to create a risk prediction model.ResultsForty-nine children were diagnosed with T2DM during the follow-up period. …”
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Enhancing landslide susceptibility predictions with XGBoost and SHAP: a data-driven explainable AI method by Danish Khan, Wasim Akram, Sajid Ullah

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A fuzzy-optimized hybrid ensemble model for yield prediction in maize-soybean intercropping system by Amna Ikram, Sunnia Ikram, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy, Adil Hussain, Amal H. Alharbi, Marwa M. Eid, Marwa M. Eid

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Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques by Hamed Shokrnia, Ashkan KhodabandehLou, Peyman Hamidi, Fedra Ashrafzadeh

“…The sensitivity assessment approves that the training and generalization abilities of the ELM and ANFIS models for the CS prediction of FRC are improved by their integration with the GWO algorithm. …”
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Using Supervised Machine Learning Algorithms to Predict Bovine Leukemia Virus Seropositivity in Florida Beef Cattle: A 10‐Year Retrospective Study by Ameer A. Megahed, Y. Reddy Bommineni, Michael Short, João H. J. Bittar

“…Objectives To compare six different supervised machine‐learning (SML) algorithms used to identify the most important risk factors for predicting BLV seropositivity in beef cattle in Florida. …”
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