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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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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Research on autonomous driving scenario modeling and application based on environmental perception data by Ming Cao, Wufeng Duan, Changqing Huo, Song Qiu, Mingchun Liu

“…Results indicated that the optimized autonomous driving algorithm significantly enhances vehicle performance in similar scenarios within highly realistic simulation scenarios, thereby improving the security of algorithm optimization and validation. …”
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Robust Cross-Validation of Predictive Models Used in Credit Default Risk by Jose Vicente Alonso, Lorenzo Escot

“…While many methodologies have been developed, cross-validation is perhaps the most widely accepted, often being part of the model development process by optimizing the hyperparameters of predictive algorithms. …”
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Adaptive lift chiller units fault diagnosis model based on machine learning. by Yang Guo, Zengrui Tian, Hong Wang, Mengyao Chen, Pan Chu, Yingjie Sheng

“…In this paper, a fault diagnosis model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. …”
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Energy-Efficient model for integrated berth allocation and quay crane management by Saeedeh Khalilpoor, Mehdi A. Kamran, Reza Babazadeh, Reza Kia

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Enhancing surface detection: A comprehensive analysis of various YOLO models by G. Deepti Raj, B. Prabadevi

“…This study presents an improved YOLOv5 detection model, exploiting the efficient channel attention (ECA) and coordinated attention (CoordAtt) mechanisms. …”
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Data-Driven Pavement Performance: Machine Learning-Based Predictive Models by Mohammad Fahad, Nurullah Bektas

“…A k-fold cross-validation technique was employed to optimize hyperparameters. Results indicate that LightGBM and CatBoost outperform other models, achieving the lowest mean squared error and highest R² values. …”
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Leveraging Agent-Based Modeling and IoT for Enhanced E-Commerce Strategies by Mohamed Shili, Sajid Anwar

“…This paper presents a novel approach for integrating e-commerce platforms with the Internet of Things (IoT) through the use of agent-based models. The key objective is to create a multi-agent system that optimizes interactions between IoT devices and e-commerce systems, thereby improving operational efficiency, adaptability, and user experience in online transactions. …”
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A deep dive into artificial intelligence with enhanced optimization-based security breach detection in internet of health things enabled smart city environment by S Jayanthi, Sodagudi Suhasini, N. Sharmili, E. Laxmi Lydia, V. Shwetha, Bibhuti Bhusan Dash, Mrinal Bachute

“…This study presents a Securing Attack Detection through Deep Belief Networks and an Advanced Metaheuristic Optimization Algorithm (SADDBN-AMOA) model in smart city-based IoHT networks. …”
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A Robust Heuristics for the Online Job Shop Scheduling Problem by Hugo Zupan, Niko Herakovič, Janez Žerovnik

“…The heuristics at the level of probabilistic rules for running the local queues is experimentally shown to provide the solutions of quality that is within acceptable approximation ratios to the best known solutions obtained by the best online algorithms. The probabilistic rule defines a model which is not unlike the spin glass models that are closely related to quantum computing. …”
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Short‐term electric power and energy balance optimization scheduling based on low‐carbon bilateral demand response mechanism from multiple perspectives by Juan Li, Yonggang Li, Huazhi Liu

“…The enhanced decision tree classifier (EDTC) algorithm is used to predict the electricity consumption behavior of transferable load (TL) users, and an improved particle swarm optimization (PSO) algorithm with “ε‐greedy” strategy is proposed to solve this model. …”
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Hyperspectral Anomaly Detection by Spatial–Spectral Fusion Based on Extreme Value-Entropy Band Selection and Cauchy Graph Distance Optimization by Song Zhao, Yali Lv, Wen Zhang, Lijun Wang, Zhiru Yang, Gaofeng Ren, Bin Wang, Xiaobin Zhao, Tongwei Lu, Jiayao Wang, Wei Li

“…Second, to enhance the model’s adaptability to non-Gaussian backgrounds and maximize spectral information utilization, we propose a Cauchy graph distance-optimized RX anomaly detection approach. …”
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