Design of intelligent optimization of sports strategy and training decision support system based on deep reinforcement learning by Hua Xu, Bing Lin, Long Liu

“…The data is preprocessed by a sliding window average filter algorithm to eliminate noise and outliers. The system adopts the DQN (Deep Q-Network) architecture and applies dual DQN technology to improve model stability. …”
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Optimization of MXene-based aqueous ionic liquids for solar systems using conventional and AI-based techniques by Mohamed Bechir Ben Hamida, Ali B. M. Ali, Narinderjit Singh Sawaran Singh, Loghman Mostafa

“…Response surface methodology (RSM) is used for predictive modeling, while enhanced hill climbing (EHC), non-dominated sorting genetic algorithm II (NSGA-II), and the multi-objective generalized normal distribution optimizer (MOGNDO) are applied for multi-objective optimization. …”
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Metaheuristic Optimization of Fractional Order Incremental Conductance (FO-INC) Maximum Power Point Tracking (MPPT) by Hossam Hassan Ammar, Ahmad Taher Azar, Raafat Shalaby, M. I. Mahmoud

“…This paper seeks to improve the photovoltaic (PV) system efficiency using metaheuristic, optimized fractional order incremental conductance (FO-INC) control. …”
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CFD-based optimization of dynamic cyclones with variable vortex length using GMDH artificial neural network by Hamed Safikhani, Somayeh Davoodabadi Farahani, Lakhbir Singh Brar, Faroogh Esmaeili

“…The final step involves optimizing the cyclone designs through the non-dominated sorting genetic algorithm (NSGA). …”
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Design and Analysis of an Interior Permanent Magnet Synchronous Motor for a Traction Drive Using Multiobjective Optimization by Yingying Xu, Yiguang Chen, Zhihua Fu, Mingxia Xu, Haiyu Liu, Li Cheng

“…Based on the genetic algorithm, a multiobjective optimization mechanism of the magnetic pole structure is established. …”
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DeepGenMon: A Novel Framework for Monkeypox Classification Integrating Lightweight Attention-Based Deep Learning and a Genetic Algorithm by Abdulqader M. Almars

“…This suggested framework leverages an attention-based convolutional neural network (CNN) and a genetic algorithm (GA) to enhance detection accuracy while optimizing the hyperparameters of the proposed model. …”
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Application of GA in Furniture Modeling Style Design by Heng Yu, Chunjing Liu

“…In this paper, an interactive innovation evolution system based on evolutionary algorithm is proposed. Taking full advantage of the evolution function of genetic algorithm and the modeling advantages of ACIS platform, a prototype innovation evolution system is developed based on ACIS/hoops platform with the genetic algorithm based on tree structure as the innovation support, which can help designers complete the innovation modeling design. …”
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Optimal State Estimation in Underwater Vehicle Discrete-Continuous Measurements via Augmented Hybrid Kalman Filter by Vadim Kramar, Kirill Dementiev, Aleksey Kabanov

“…State-vector augmentation is proposed to improve the estimation convergence. The authors present numerical modeling results demonstrating the algorithm’s efficiency on the example of motion parameter estimation for the autonomous underwater vehicle. …”
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Process parameter optimization for minimizing overcut in abrasive waterjet deep hole drilling of SS 316L by J. Bharani Chandar, N. Lenin, Alagar Karthick, Pradeep Kumar Singh, M. Siva Kumar, Md Irfanul Haque Siddiqui, Intesaaf Ashraf, Saurav Dixit, Nikolai Ivanovich Vatin, Choon Kit Chan

“…Additionally, to mitigate the impact of overcut and improve the drilling quality necessary for diverse sectors such as automotive, biomedical, and oil and gas, a metaheuristic optimization method, specifically the Grasshopper Optimization Algorithm (GHO), has been utilized. …”
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Development of compact mechanism for lithium-ion battery venting gas fires using Cantera ordinary differential equation neural network algorithm by Mengjie Li, Hao Hu, Li Lu, Huangwei Zhang

“…To address these issues, this work develops the Cantera Ordinary Differential Equation Neural Network (CODENN) algorithm, which combines the computational power of neural ordinary differential equations with Cantera's advanced chemical kinetics modeling. …”
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Multi-objective design optimization of a transonic axial fan stage using sparse active subspaces by Richard Amankwa Adjei, Chengwei Fan

“…Active subspace was used to compute the active variables via singular value decomposition and a hybrid polynomial correlated function expansion was used to construct a surrogate model on the active subspace. Coupled with freeform method and multi-objective genetic algorithm, an automated optimization loop was run at a single operating condition. …”
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Three-Dimensional Aerodynamic Optimization of Single-Layer Reticulated Cylindrical Roofs Subjected to Mean Wind Loads by Bingbing San, Chen Xu, Ye Qiu

“…This paper presents a procedure to optimize the rise-to-span ratio of single-layer reticulated cylindrical roofs to improve their aerodynamic performance, by coupling the optimization method with computational fluid dynamics (CFD) and finite element analysis (FEA). …”
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Suicide Risk Screening in Jails: Protocol for a Pilot Study Leveraging the Mental Health Research Network Algorithm and Health Care Data by Erin B Comartin, Grant Victor, Athena Kheibari, Brian K Ahmedani, Bethany Hedden-Clayton, Richard N Jones, Ted R Miller, Jennifer E Johnson, Lauren M Weinstock, Sheryl Kubiak

“…This study will use administrative data and ML modeling to improve suicide risk detection at jail booking. …”
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HiGMA-DADCN: Hirudinaria granulosa multitropic algorithm optimised double attention enabled deep convolutional neural network for psoriasis classification by Soumya C S, Jayanna H S

“…This model integrates two key components the HiGMA algorithm and a double attention module designed to enhance segmentation and boost overall classification performance. …”
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Parametric design and multiple objective intelligent optimization of marine nuclear turbine aerothermodynamics under variable conditions by Lei Zhang, Guobing Chen, Yu-Ang Shi, LuoTao Xie

“…By combining the blade parameterized reconstruction method with a Kriging approximation model and a multi-objective genetic algorithm (GA), we developed an optimized system for thermodynamic performance in turbines. …”
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Optimizing a Dynamic Vehicle Routing Problem with Deep Reinforcement Learning: Analyzing State-Space Components by Anna Konovalenko, Lars Magnus Hvattum

“…<i>Results:</i> Our findings demonstrate that a carefully designed state space in the formulation of the DVRP significantly improves RL performance. Notably, incorporating derived features and selectively applying feature transformation enhanced the model’s decision-making capabilities. …”
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A study on optimization of passenger flow density of rail transit platform based on balanced train diagram by XIAO Lijun, LIANG Ye, LI Juanjuan, XIAO Xiyu, LI Denggen, TAN Junhua

“…With the development of urban rail transit, metro has become the preferred mode of public transportation for citizens. In order to improve passengers' travel experience, realize the matching of transport capacity and transport demands, and reduce the congestion in waiting areas of urban rail transit platform, in this paper, the optimization of the passenger flow density on platform was researched based on the balance indexes of running chart. …”
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Deep Reinforcement Learning-Based Two-Phase Hybrid Optimization for Scheduling Agile Earth Observation Satellites by Guanghui Zhou, Zhicheng Jin, Dongning Liu

“…To balance solution quality and computational efficiency, a deep reinforcement learning (DRL)-based algorithmic framework is proposed. A Markov decision process (MDP) is formulated as the foundational model for the DRL architecture. …”
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An Intelligent Crow Search Optimization and Bi-GRU for Forest Fire Detection System Using Internet of Things by Syed Abdul Moeed, Bellam Surendra Babu, M. Sreevani, B. V. Devendra Rao, R. Raja Kumar and Gouse Baig Mohammed

“…CSO is inspired by the intelligent foraging behavior of crows, and when combined with fractional calculus, it provides a robust optimization framework that improves the accuracy and efficiency of the AI model. …”
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Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences by Eugenia Mylona, Dimitrios I. Zaridis, Charalampos Ν. Kalantzopoulos, Nikolaos S. Tachos, Daniele Regge, Nikolaos Papanikolaou, Manolis Tsiknakis, Kostas Marias, ProCAncer-I Consortium, Dimitrios I. Fotiadis

“…Key Points Radiomics is a growing field that can still be optimized. Feature selection method impacts radiomics models’ performance more than ML algorithms. …”
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