Analysis of the sports action recognition model based on the LSTM recurrent neural network by Chen Ping, Peng Jiangui

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Offline System for 2D Indoor Navigation Utilizing Advanced Data Structures by Jorge Luis Veloz, Leo Sebastián Intriago, Jean Carlos Palma, Andrea Katherine Alcívar-Cedeño, Álvaro Antón-Sacho, Pablo Fernández-Arias, Edwan Anderson Ariza, Diego Vergara

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Fault Diagnosis of Oil Pumping Machine Retarder Based on Sound Texture-Vibration Entropy Characteristics and Gray Wolf Optimization-Support Vector Machine by Shutao Zhao, Ke Chang, Erxu Wang, Bo Li, Kedeng Wang, Qingquan Wu

“…Finally, joint eigenvectors were constructed and fed into SVM for learning. The gray wolf optimization (GWO) algorithm was used to optimize the parameters of the SVM model based on mixed kernel function, which reduces the impact of sensor frequency response, environmental noise, and load fluctuation disturbance on the accuracy of retarder fault diagnosis. …”
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A Fault Diagnosis Method for Oil Well Electrical Power Diagrams Based on Multidimensional Clustering Performance Evaluation by Xingyu Liu, Xin Meng, Ze Hu, Hancong Duan, Min Wang, Yaping Chen

“…Through simulations and experiments on 10 UCI datasets, the proposed effectiveness function accurately evaluates the clustering results and determines the optimal number of clusters, significantly improving the performance of the clustering algorithm. …”
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An EfficientNet integrated ResNet deep network and explainable AI for breast lesion classification from ultrasound images by Kiran Jabeen, Muhammad Attique Khan, Ameer Hamza, Hussain Mobarak Albarakati, Shrooq Alsenan, Usman Tariq, Isaac Ofori

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A Comparative Study Between Soft Actor-Critic (SAC) and Deep Deterministic Policy Gradient (DDPG) Algorithms for Solar PV MPPT Control Under Partial Shading Conditions by Sampson E. Nwachukwu, Komla A. Folly, Kehinde O. Awodele

“…However, due to the intermittent nature of PV arrays, the Maximum Power Point Tracking (MPPT) algorithm is typically employed to optimize the system’s energy production. …”
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Bilevel Programming Model of Urban Public Transport Network under Fairness Constraints by Jingjing Hao, Xinquan Liu, Xiaojing Shen, Nana Feng

“…It provides theoretical basis and model foundation for the optimization of public transit network, and it is a new attempt to improve the fairness of the traffic planning scheme.…”
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Research on Tracking Control of Unmanned Mine Trucks Based on Adaptive Preview by HUANG Yaoran, LIU Zhicong, KANG Yuanrong

“…Finally, the optimization function is solved by genetic algorithms (GA), and the optimal preview point is output to the rear wheel feedback controller to realize the optimal control of the vehicle in the global path tracking process. …”
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Design of an intelligent AI-based multi-layer optimization framework for grid-tied solar PV-fuel cell hybrid energy systems by Prashant Nene, Dolly Thankachan

“…The results validate its capability when compared against traditional methods such as Genetic Algorithms and Particle Swarm Optimization. With this, we now have a scalable and real-time energy-efficient solution for future smart grid systems. • Integrated Intelligence Stack: Combines RL-ENN, T-STFREP, FL-DEO, GNNHSCO, and Q-GAN-ESO into a unified architecture for real-time control, forecasting, decentralized optimization, network routing, and synthetic scenario generation. • Real-Time, Scalable, and Privacy-Preserving: Enables adaptive energy dispatch, federated optimization without compromising data privacy, and graph-based power routing, making it suitable for large-scale, smart grid deployments. • Proven Long-Term Performance: Achieved significant improvements over traditional methods (GA, PSO) with 27.5 % lower NPC, 18.2 % reduction in COE, and 30.2 % increase in battery life, validated using 30 years of meteorological data.…”
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Enhancing 3D A* path planning of intelligent bridge crane based on energy efficiency criteria by Heng YANG, Yue LI, Min LIU, Qing DONG

“…Subsequently, by comprehensively considering energy, time, and path length, the final evaluation value is formed to determine the optimal path. Finally, considering the spatial arrangement and operation of the bridge crane in a factory building as an example, environment modeling is conducted on the MATLAB platform, the virtual obstacle is built at the same scale, the operation scheme of the bridge crane lifting the weight at different heights is conducted, and the multi-scheme path planning is conducted before and after the improvement of the algorithm. …”
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Research on Deformation Prediction of Foundation Pit Based on PSO-GM-BP Model by Dongge Cui, Chuanqu Zhu, Qingfeng Li, Qiyun Huang, Qi Luo

“…Against with low accuracy and limited applicability of a single model in forecasting, a PSO-GM-BP model was established, which used the PSO optimization algorithm to optimize and improve the GM (1, 1) model and the BP network model, respectively. …”
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A Capacity Optimization Configuration Method for Photovoltaic and Energy Storage System of 5 G Base Station Considering Time-of-Use Electricity Price by Ziyan HAN, Shouxiang WANG, Qianyu ZHAO, Zhijie ZHENG

“…Then, the quantum-behaved particle swarm optimization algorithm is used to calculate the minimum comprehensive cost of the photovoltaic and energy storage system of 5G base station in a typical day to determine the optimal capacity of photovoltaic power generation and energy storage. …”
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Active Magnetic Bearing Rotor Model Updating Using Resonance and MAC Error by Yuanping Xu, Jin Zhou, Long Di, Chen Zhao, Qintao Guo

“…Modelling error is minimized by applying a numerical optimization Nelder-Mead simplex algorithm to properly adjust FE model parameters. …”
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An Integration of Deep Neural Network-Based Extended Kalman Filter (DNN-EKF) Method in Ultra-Wideband (UWB) Localization for Distance Loss Optimization by Chanthol Eang, Seungjae Lee

“…The results clearly show that the proposed model outperforms existing methods, including NN-EKF, LPF-EKF, and other traditional approaches. …”
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Design Optimization of Compliant Mechanisms for Vibration- Assisted Machining Applications Using a Hybrid Six Sigma, RSM-FEM, and NSGA-II Approach by Huy-Tuan Pham, Van-Khien Nguyen, Quang-Khoa Dang, Thi Van Anh Duong, Duc-Thong Nguyen, Thanh-Vu Phan

“…This paper proposes the design of a new 2-DOF high-precision compliant positioning mechanism using an optimization process combining the response surface method, finite element method, and Six Sigma analysis into a multi-objective genetic algorithm. …”
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Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing by Ahmet Hakan İnce, Serkan Özbay

“…Student abilities (θ), item difficulties (b), and time–effect parameters (λ) were estimated using the L-BFGS-B algorithm to ensure numerical stability. The results indicate that subtractive models, particularly DTA-IRT, achieved the lowest AIC/BIC values, highest AUC, and improved parameter stability, confirming their effectiveness in penalizing excessive response times without disproportionately affecting moderate-speed students. …”
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Secure THz Communication in 6G: A Two-Stage DRL Approach for IRS-Assisted NOMA by Muhammad Shahwar, Manzoor Ahmed, Touseef Hussain, Muhammad Sheraz, Wali Ullah Khan, Teong Chee Chuah

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BAYESIAN FINITE ELEMENT MODEL UPDATING BASED ON MARKOV CHAIN POPULATION COMPETITION by YE Ling, JIANG HongKang, ZOU YuQing, CHEN HuaPeng, WANG LiCheng

“…The traditional Markov Chain Monte Carlo（MCMC） simulation method is inefficient and difficult to converge in high dimensional problems and complicated posterior probability density.In order to overcome these shortcomings，a Bayesian finite element model updating algorithm based on Markov chain population competition was proposed.First，the differential evolution algorithm was introduced in the traditional method of Metropolis-Hastings algorithm.Based on the interaction of different information carried by Markov chains in the population，optimization suggestions were obtained to approach the objective function quickly.It solves the defect of sampling retention in the updating process of high-dimensional parameter model.Then，the competition algorithm was introduced，which has constant competitive incentives and a built-in mechanism for losers to learn from winners.Higher precision was obtained by using fewer Markov chains，which improves the efficiency and precision of model updating.Finally，a numerical example of finite element model updating of a truss structure was used to verify the proposed algorithm in this paper.Compared with the results of standard MH algorithm，the proposed algorithm can quickly update the high-dimensional parameter model with high accuracy and good robustness to random noise.It provides a stable and effective method for finite element model updating of large-scale structure considering uncertainty.…”
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Optimized customer churn prediction using tabular generative adversarial network (GAN)-based hybrid sampling method and cost-sensitive learning by I Nyoman Mahayasa Adiputra, Paweena Wanchai, Pei-Chun Lin

“…Additionally, this study provided a robustness measurement for algorithms, demonstrating that CostLearnGAN outperforms other sampling methods in improving the performance of classical machine learning models with a 5.68 robustness value on average.…”
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AI-Driven predicting and optimizing lignocellulosic sisal fiber-reinforced lightweight foamed concrete: A machine learning and metaheuristic approach for sustainable construction by Mohamed Sahraoui, Aissa Laouissi, Yacine Karmi, Abderazek Hammoudi, Mostefa Hani, Yazid Chetbani, Ahmed Belaadi, Ibrahim M.H. Alshaikh, Djamel Ghernaout

“…Six predictive models were assessed for accuracy and generalization: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Linear Model (LM), Dragonfly Algorithm-based Deep Neural Network (DNN-DA), and Improved Grey Wolf Optimizer-based Deep Neural Network (DNN-IGWO). …”
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