Dynamic and static integrated classification model of gas well based on XGBoost algorithm—an example from block S of Sulige tight sandstone gas field by Shuangshuang Zhang, Shuangshuang Zhang, Xiangdong Yu, Xiuli Gao, Donglin Li, Shijun Huang

“…Aiming at this problem, this paper establishes a set of dynamic and static integrated classification model of tight sandstone gas wells in Sulige based on XGBoost algorithm. …”
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Research on Gating Fusion Algorithm for Power Grid Survey Data Based on Enhanced Mamba Spatial Neighborhood Relationship by Aiyuan Zhang, Jinguo Lv, Yu Geng, Xiaolei Wang, Xianhu Li

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A New Approach to Environmental Economic Dispatch Using Multiobjective Differential Evolution: A Case Study by Carine Nogueira Santino, Jorge Laureano Moya Rodriguez, Cristiano Hora De Oliveira Fontes

“…In electricity generation systems, problems related to economic and environmental dispatch represent potential for improvement in generating plants. The Environmental Economic Dispatch (EED) consists of minimizing the cost of generation and emission of pollutants such as CO2, SO2 and NOx. …”
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Energy management for microgrids integrating renewable sources and hybrid electric vehicles by Wanying Liu, Chunqing Rui, Zilin Liu, Jinxin Chen

“…It also incorporates demand response mechanisms for greater resilience. The Kepler Optimization Algorithm (KOA), inspired by Kepler's laws of planetary motion, is employed to tackle the nonlinear optimization problem. …”
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Integrated Operational Planning of Battery Storage Systems for Improved Efficiency in Residential Community Energy Management Using Multistage Stochastic Dual Dynamic Programming:... by Pattanun Chanpiwat, Fabricio Oliveira, Steven A. Gabriel

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Multi-Objective Optimal Scheduling of Water Transmission and Distribution Channel Gate Groups Based on Machine Learning by Yiying Du, Chaoyue Zhang, Rong Wei, Li Cao, Tiantian Zhao, Wene Wang, Xiaotao Hu

“…This study develops a synergistic optimization method of multiple gates integrating hydrodynamic simulation and data-driven methods, with the goal of improving the accuracy of water distribution and regulation efficiency. …”
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Optimal control of asynchronous drive of auxiliary machines of electric rolling stock by Yu. M. Kulinich, S. A. Shukharev, V. K. Dukhovnikov, D. A. Starodubtsev

“…The proposed system of optimal control of electric locomotive auxiliary machines is designed to improve the energy efficiency of the drive with a new algorithm for selecting the optimal value of the rotor flux linkage by reducing the current consumed by the drive. …”
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A simulation-driven computational framework for adaptive energy-efficient optimization in machine learning-based intrusion detection systems by Ripal Ranpara, Osamah Alsalman, Om Prakash Kumar, Shobhit K. Patel

“…Extensive simulations conducted on the KDD 1999 dataset demonstrate that GreenMU achieves a detection accuracy close to 99%, significantly surpassing standard baseline models while reducing energy consumption by 31%. Furthermore, the framework improves computational efficiency, reducing processing time by 15% and making it highly effective for resource-constrained environments such as IoT and edge computing. …”
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Challenges in Unifying Physically Based and Machine Learning Simulations Through Differentiable Modeling: A Land Surface Case Study by Shahryar K. Ahmad, Sujay V. Kumar, Clara Draper, Rolf H. Reichle

“…Scaling and bias correction factors, often used in ML approaches for enhancing generalizability, were found to limit the transferability of the optimized physical parameters to the land model. The global objective function further compromises the algorithm's ability to simultaneously capture contrasting moisture regimes. …”
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Optimizing Social Security Contributions for Spanish Self-Employed Workers: Combining Data Preprocessing and Ensemble Models for Accurate Revenue Estimation by Luis Palomero, Vicente García, José Salvador Sánchez

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Optimized YOLOv8 for enhanced breast tumor segmentation in ultrasound imaging by Ayman Mohamed Mostafa, Alaa S. Alaerjan, Bader Aldughayfiq, Hisham Allahem, Alshimaa Abdelraof Mahmoud, Wael Said, Hosameldeen Shabana, Mohamed Ezz

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Artificial intelligence-optimized shield parameters for soft ground tunneling in urban environment: A case study of Bangkok MRT Blue Line by Sahatsawat Wainiphithapong, Chana Phutthananon, Sompote Youwai, Pitthaya Jamsawang, Phattarawan Malaisree, Ochok Duangsano, Pornkasem Jongpradist

“…For simplification and practical field implementation, the same set of SOP values is applied across all 11 timesteps during the optimization process. Using the proposed optimization framework, the optimal results demonstrate improvements in Pavg, increasing by up to 109.8% (from 13.99 to 29.35 mm) and in S, reducing up to 79.6% (from 34.55 to 7.06 mm) when MOO is conducted as a time series using the simplified method. …”
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Seismic Optimization of Fluid Viscous Dampers in Cable-Stayed Bridges: A Case Study Using Surrogate Models and NSGA-II by Qunfeng Liu, Zhen Liu, Jun Zhao, Yuhang Lei, Shimin Zhu, Xing Wu

“…The second strategy employs a data-driven surrogate model, specifically an Artificial Neural Network (ANN), integrated with the NSGA-II optimization algorithm. …”
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Crucial heat damage analysis and optimization of a mid-sized pickup truck based on a deep Gaussian process model by Zebin Zhang, Sisi Liu, Xianzong Meng, Tingting Wang, Shizhao Jing, Chuanrui Wang, Dongchen Qin

“…Based on simulation results, a multi-objective two-layer deep Gaussian process model predicted heat source temperatures. The positions of cooling components were optimized using a genetic algorithm with heat-sensitive locations as the objectives. …”
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Alzheimer’s Prediction Methods with Harris Hawks Optimization (HHO) and Deep Learning-Based Approach Using an MLP-LSTM Hybrid Network by Raheleh Ghadami, Javad Rahebi

“…<b>Method:</b> This proposal methodology involves sourcing Alzheimer’s disease-related MRI images and extracting features using convolutional neural networks (CNNs) and the Gray Level Co-occurrence Matrix (GLCM). The Harris Hawks Optimization (HHO) algorithm is applied to select the most significant features. …”
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Intelligent rockburst level prediction model based on swarm intelligence optimization and multi-strategy learner soft voting hybrid ensemble by Qinghong Wang, Tianxing Ma, Shengqi Yang, Fei Yan, Jiang Zhao

“…The data preprocessing method proposed in this study, based on an improved version of the Student t-SNE algorithm, effectively reduced the negative impact of data noise on model performance, enhancing the reliability of predictions. …”
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Financial fraud detection using a hybrid deep belief network and quantum optimization approach by Gui Yu, Zhenlin Luo

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Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach by Sujan Ghimire, Ravinesh C. Deo, Konstantin Hopf, Hangyue Liu, David Casillas-Pérez, Andreas Helwig, Salvin S. Prasad, Jorge Pérez-Aracil, Prabal Datta Barua, Sancho Salcedo-Sanz

“…Explainable Artificial Intelligence (xAI) methods were used to enhance model interpretability and trustworthiness, with optimization via the Optuna algorithm. …”
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Enhancing the prediction of groundwater quality index in semi-arid regions using a novel ANN-based hybrid arctic puffin-hippopotamus optimization model by Moustafa Gamal Snousy, Hussein M. Elshafie, Ashraf R. Abouelmagd, Najmaldin Ezaldin Hassan, Mahmoud E. Abd-Elmaboud, Ali Akbar Mohammadi, Ashraf M.T. Elewa, E. EL-Sayed, Ahmed M. Saqr

“…Study focus: This study presents a novel hybrid arctic puffin–hippopotamus optimization (HPHO) algorithm combined with an artificial neural network (ANN) to improve irrigation water quality index (IWQI) predictions in semi-arid areas. …”
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Predictive analytics of complex healthcare systems using deep learning based disease diagnosis model by Muhammad Kashif Saeed, Alanoud Al Mazroa, Bandar M. Alghamdi, Fouad Shoie Alallah, Abdulrhman Alshareef, Ahmed Mahmud

“…In addition, the convolutional neural network with long short-term memory (CNN-LSTM) approach is used to classify LCC. To optimize the hyperparameter values of the CNN-LSTM approach, the Chaotic Tunicate Swarm Algorithm (CTSA) approach was implemented to improve the accuracy of classifier results. …”
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