GA BP prediction model for energy consumption of steel rolling reheating furnace by Yi Duan, Guang Chen, Xiangjun Bao, Jing Xu, Lu Zhang, Xiaojing Yang

“…The proposed GA-BP model demonstrates superior predictive capabilities and robustness, offering valuable insights for optimizing process parameters and improving energy efficiency in SRRF operations.…”
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Prediction and Optimization for Multi-Product Marketing Resource Allocation in Cross-Border E-Commerce by Yi Xie, Heng-Qing Ye, Wenbin Zhu

“…We propose a two-stage optimization framework that integrates predictive models with constrained optimization. …”
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SLPDBO-BP: an efficient valuation model for data asset value by Cuiping Zhou, Shaobo Li, Cankun Xie, Panliang Yuan, Zihao Liao

“…Secondly, in an attempt to comprehensively evaluate the optimization performance of SLPDBO, a series of numerical optimization experiments are carried out with 20 test functions and with popular optimization algorithms and dung beetle optimizer (DBO) algorithms with different improvement strategies. …”
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Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry by PNV Srinivasa Rao, PVY Jayasree

“…The validation of the proposed predictive maintenance model optimization with different types of deep learning algorithms shows that our proposed methodology gives an improved accuracy of 98.9336% which is higher than any other models.   …”
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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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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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Dynamic Classification: Leveraging Self-Supervised Classification to Enhance Prediction Performance by Ziyuan Zhong, Junyang Zhou

“…In addition, the algorithm uses subareas boundary to refine predictions results and filter out substandard results without requiring additional models. …”
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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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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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Optimizing space heating efficiency in sustainable building design a multi criteria decision making approach with model predictive control by Zheng Qi, Nan Zhou, Xianwei Feng, Sama Abdolhosseinzadeh

“…The research question explores how advanced control strategies can balance heating costs and thermal comfort efficiently. A novel Model Predictive Control (MPC) framework integrates Long Short-Term Memory (LSTM) neural networks for energy demand prediction and the Ant Nesting Algorithm (ANA) for multi-objective optimization. …”
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Integrated Optimization of Emergency Evacuation Routing for Dam Failure-Induced Flooding: A Coupled Flood–Road Network Modeling Approach by Gaoxiang An, Zhuo Wang, Meixian Qu, Shaohua Hu

“…Based on this model, a flood evacuation route planning method was proposed using Dijkstra’s algorithm. …”
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Improved Splitting-Integrating Methods for Image Geometric Transformations: Error Analysis and Applications by Hung-Tsai Huang, Zi-Cai Li, Yimin Wei, Ching Yee Suen

“…In this paper, we apply the improved techniques to the harmonic, Poisson and blending models, which transform the original shapes of images and patterns into arbitrary target shapes. …”
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EDECO: An Enhanced Educational Competition Optimizer for Numerical Optimization Problems by Wenkai Tang, Shangqing Shi, Zengtong Lu, Mengying Lin, Hao Cheng

“…On the one hand, the estimation of distribution algorithm enhances the global exploration ability and improves the population quality by establishing a probabilistic model based on the dominant individuals provided by EDECO, which solves the problem that the algorithm is unable to search the neighborhood of the optimal solution. …”
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Energy Management of a Semi-Autonomous Truck Using a Blended Multiple Model Controller Based on Particle Swarm Optimization by Mohammad Ghazali, Ishaan Gupta, Kemal Buyukkabasakal, Mohamed Amine Ben Abdallah, Caner Harman, Berfin Kahraman, Ahu Ece Hartavi

“…The approach combines a particle swarm optimization algorithm to determine optimal controller gains and a multiple model controller to adapt these gains dynamically based on real-time vehicle mass. …”
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Geostatistics and artificial intelligence coupling: advanced machine learning neural network regressor for experimental variogram modelling using Bayesian optimization by Saâd Soulaimani, Saâd Soulaimani, Ayoub Soulaimani, Kamal Abdelrahman, Abdelhalim Miftah, Mohammed S. Fnais, Biraj Kanti Mondal

“…The improved reliability of the Bayesian-optimized regressor demonstrates its superiority over traditional, non-optimized regressors, indicating that incorporating Bayesian optimization can significantly advance experimental variogram modelling, thus offering a more accurate and intelligent solution, combining geostatistics and artificial intelligence specifically machine learning for experimental variogram modelling.…”
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Enhancing Last-Mile Logistics: AI-Driven Fleet Optimization, Mixed Reality, and Large Language Model Assistants for Warehouse Operations by Saverio Ieva, Ivano Bilenchi, Filippo Gramegna, Agnese Pinto, Floriano Scioscia, Michele Ruta, Giuseppe Loseto

“…However, existing approaches often treat these aspects in isolation, missing opportunities for optimization and operational efficiency gains through improved information visibility across different roles in the logistics workforce. …”
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A machine learning model with crude estimation of property strategy for performance prediction of perovskite solar cells based on process optimization by Dan Li, Ernie Che Mid, Shafriza Nisha Basah, Xiaochun Liu, Jian Tang, Hongyan Cui, Huilong Su, Qianliang Xiao, Shiyin Gong

“…The model’s evaluation metrics improved by utilizing excess non-stoichiometric components (Ensc) and perovskite additive compounds (Pac) as CEP. …”
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Innovative Business Models Towards Sustainable Energy Development: Assessing Benefits, Risks, and Optimal Approaches of Blockchain Exploitation in the Energy Transition by Aikaterini Papapostolou, Ioanna Andreoulaki, Filippos Anagnostopoulos, Sokratis Divolis, Harris Niavis, Sokratis Vavilis, Vangelis Marinakis

“…Blockchain has the potential to change energy services towards this direction. To optimally exploit blockchain, innovative business models need to be designed, identifying the opportunities emerging from unmet needs, while also considering potential risks so as to take action to overcome them. …”
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Enhancing Compressive Strength Prediction in Recycled Aggregate Concrete through Robust Hybrid Machine Learning Approaches by Samuel Keown, Dylan O’Dwyer

“…To address this issue, robust hybrid machine learning (ML) approaches are employed, particularly emphasizing the Least Square Support Vector Regression (LSSVR) model. This investigation explores the integration of LSSVR with two innovative optimizers, namely the Giant Trevally Optimizer (GTO) and the Dingo Optimization Algorithm (DOA). …”
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Thermal errors in high-speed motorized spindle: An experimental study and INFO-GRU modeling predictions by Zhaolong Li, Kai Zhao, Haonan Sun, Yongqiang Wang, Bangxv Wang, JunMing Du, Haocheng Zhang

“…The novelty of this study lies in two improvements: firstly, the number of temperature measurement points is optimized by combining a clustering algorithm with a correlation coefficient method, reducing the amount of calculation and the risk of data coupling in the prediction; secondly, the GRU model optimized by the INFO algorithm is applied to the field of electric spindles for the first time, effectively analyzing the dynamic relationship between temperature and thermal expansion. …”
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