Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Ibrahim Elbatal, Ehab M. Almetwally, Marwa M. Eid, El-Sayed M. El-Kenawy

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Development of algorithms for constructing two-dimensional optimal boundary-adaptive grids and their software implementation by A. E. Chistyakov, V. V. Sidoryakina, S. V. Protsenko

“…It is noted that the use of adaptive grids in calculations makes it possible to improve the accuracy and efficiency of computational algorithms without increasing the number of nodes. …”
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Machine Learning Innovations for Improving Mineral Recovery and Processing: A Comprehensive Review* by Korie, Josephmartin Izuchukwu*, Chudi-Ajabor, Ogochukwu Gabriela, Ezeonyema, Chukwudalu Chukwuekezie, Oshim, Francisca Ogechukwu

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Two-Stage Global–Local Aerodynamic/Stealth Optimization Method Based on Space Decomposition by Wei Zhang, Lin Zhou, Bowen Shu, Xian Chen, Zhenghong Gao, Jiangtao Huang

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Enhancing machine learning performance in cardiac surgery ICU: Hyperparameter optimization with metaheuristic algorithm. by Ali Bahrami, Morteza Rakhshaninejad, Rouzbeh Ghousi, Alireza Atashi

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Analysis of Weak Links in the Mechanized Mining of Underground Metal Mines: Insights from Machine Learning and SHAP Explainability Models by Chengye Yang, Keping Zhou, Jielin Li

“…By leveraging data from 88 stopes at Guangxi Tongkeng Mine over a decade, we constructed a comprehensive dataset encompassing drilling, charging, blasting, ventilation, support, ore drawing, and maintenance. The XGBoost algorithm was employed to model factors influencing stope production capacity (PC), with its parameters optimized using the Marine Predator Algorithm (MPA). …”
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Improving Earth surface temperature forecasting through the optimization of deep learning hyper-parameters using Barnacles Mating Optimizer by Zuriani Mustaffa, Mohd Herwan Sulaiman, Muhammad ‘Arif Mohamad

“…To improve the DL model's performance, an optimization algorithm called Barnacles Mating Optimizer (BMO) is integrated to optimize both weights and biases. …”
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Cold Chain Logistics Path Optimization with Adaptive Speed and Hybrid Genetic Algorithm Solution by Yuhui Sun, Dazhi Pan

“…Through comparison to other algorithms in the literature, the results show that the hybrid genetic algorithm not only improves customer satisfaction, but also maintains a lower total cost, which is obviously superior when solving the complex cold chain distribution path optimization problem; further comparison and analysis of the mathematical model in this paper with the single-dimension satisfaction model reveals that under the same satisfaction constraint threshold, the model in this paper can significantly reduce the system operating cost; we also deeply discuss the influence mechanism of vehicle traveling mode and customer point sparsity radius on distribution path planning.…”
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A Hybrid Multi-Strategy Differential Creative Search Optimization Algorithm and Its Applications by Yuanyuan Zhang, Longquan Yong, Yijia Chen, Jintao Yang, Mengnan Zhang

“…The comparison includes both recent state-of-the-art algorithms and improved optimization methods. Simulation results demonstrate that the incorporation of the refined set and clustering process, along with the table reinforcement learning model (double Q-learning model) mechanism, leads to superior convergence speed and higher optimization precision.…”
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A Lightweight Pavement Defect Detection Algorithm Integrating Perception Enhancement and Feature Optimization by Xiang Zhang, Xiaopeng Wang, Zhuorang Yang

“…To address the current issue of large computations and the difficulty in balancing model complexity and detection accuracy in pavement defect detection models, a lightweight pavement defect detection algorithm, PGS-YOLO, is proposed based on YOLOv8, which integrates perception enhancement and feature optimization. …”
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Research on Investment Estimation of Prefabricated Buildings Based on Genetic Algorithm Optimization Neural Network by Jin Gao, Wanhua Zhao

“…Starting from the investment decision-making stage of construction projects, this paper analyses the characteristics of prefabricated investment estimation and the relevant literature on the characteristics of prefabricated construction projects, uses the rough set attribute reduction algorithm to screen the key engineering characteristic factors, and establishes a BP neural network model optimized by genetic algorithm to estimate and analyze the investment of completed prefabricated construction projects. …”
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Configurational Comparison of a Binary Logic Transmission Unit Applicable to Agricultural Tractor Hydro-Mechanical Continuously Variable Transmissions and Its Wet Clutch Optimizati... by Wenjie Li, Zhun Cheng, Mengchen Yang

“…The WOA improved the spread value in the GRNN algorithm, establishing a GRNN to predict the optimal range for wet clutch design values in BLT-U; the model validation showed an average correlation coefficient of 0.92 for speed curves and an average relative error of 5.58% for dynamic loads. …”
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SGD-TripleQNet: An Integrated Deep Reinforcement Learning Model for Vehicle Lane-Change Decision by Yang Liu, Tianxing Yang, Liwei Tian, Jianbiao Pei

“…This method integrates three types of deep Q-learning networks (DQN, DDQN, and Dueling DDQN) and uses the Stochastic Gradient Descent (SGD) optimization algorithm to dynamically adjust the network weights. …”
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Deep deterministic policy gradient-based energy efficiency optimization algorithm for CR-NOMA by ZHANG Yun

“…Therefore, for CR-NOMA, deep deterministic policy gradient-based energy efficiency optimization (DPEE) algorithm was proposed. By jointly optimizing the transmission power and time slot splitting coefficient, the energy efficiency of sensor devices was improved. …”
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Optimization and Scheduling of Integrated Energy Systems with Hydrogen-electricity Coupling Based on PKO Algorithm by Wenli QIN, Na RONG, Xingyan LIU

“…To address the problems of getting stuck in local optima and slow convergence speed during the solution process, the Pied Kingfisher Optimizer (PKO) algorithm was introduced. [Result] The model aims to minimize the total system cost as the objective function, and solves for the optimal scheduling results of the output of each energy network unit; Compared with traditional optimization algorithms, PKO has a faster convergence speed and is better able to achieve the goal of global optimal solution.…”
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Route Optimization of Pipeline in Gas-Liquid Two-Phase Flow Based on Genetic Algorithm by Jun Zhou, Guangchuan Liang, Tao Deng, Jing Gong

“…This paper describes the problems in route optimization of two-phase pipelines. Combining the hydraulic calculation with route optimization theory, this paper establishes an automatic route optimization model and adopts the general genetic algorithm (gGA) and steady-state genetic algorithm (ssGA) to solve the model, respectively, gets the optimal route, and discusses the influence of parameters setting to the result. …”
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UWB Indoor Localization Based on Artificial Rabbit Optimization Algorithm and BP Neural Network by Chaochuan Jia, Can Tao, Ting Yang, Maosheng Fu, Xiancun Zhou, Zhendong Huang

“…The ARO algorithm optimizes the initial weights and thresholds of the BPNN, enabling the model to escape local optima and converge to a global solution. …”
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Arrhythmia detection with transfer learning architecture integrating the developed optimization algorithm and regularization method by Fatma Akalın, Pınar Dervişoğlu Çavdaroğlu, Mehmet Fatih Orhan

“…In this direction, Proposed Optimization Algorithm V5 and Proposed Regularization Method V5 approaches have been integrated into the MobileNetv2 transfer learning model. …”
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Tree-Based Algorithms and Incremental Feature Optimization for Fault Detection and Diagnosis in Photovoltaic Systems by Khaled Chahine

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Research on Optimized Algorithm for Deep Learning Based Recognition of Sediment Particles in Turbulent Flow by WANG Hao, YANG Feiqi, ZHANG Lei, WU Wei, XIE Haonan, ZHAO Lin

“…This enhanced performance in practical detection surpasses that of the original YOLOv5 model. The number of observed particle chains increases following optimization by integrating the improved YOLOv5 model with the grayscale subtraction technique for detecting particle motion. …”
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