Hybrid procurement model for the construction of library literature and information resource procurement by Chuanyu Zhang, Changsheng Wang

“…The results show that using genetic algorithm to optimize support vector machine can effectively improve the prediction speed and prediction efficiency of the model. …”
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Recurrent Neural Network Optimized by Grasshopper for Accurate Audio Data-Based Diagnosis of Parkinson's Disease by Saif Wali Ali Alsudani, Ghassan Khudair Saud

“… Proposed here is a speech-based diagnostic framework for detecting Parkinson's disease that utilizes a Long Short-Term Memory neural network and the Grasshopper Optimization Algorithm. The framework aims to improve the detection of PD while ensuring accurate and efficient classification of speech-based signals. …”
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A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic Optimization Algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and... by Priya Mathur, Hammad Shaikh, Farhan Sheth, Dheeraj Kumar, Amit Kumar Gupta

“…In particular, autoencoder-based augmentation combined with hyperparameter optimization consistently improved predictive accuracy across all models. …”
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Few-shot hotel industry site selection prediction method based on meta learning algorithms and transportation accessibility by Na Li, Huaishi Wu

“…Abstract Exploring the rationality of hotel location selection is of significant importance for optimizing urban spatial structure and improving tourism service levels. …”
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Experimental acoustic study of small horizontal axis wind turbines based on computational fluid dynamics and artificial intelligence approaches by M. Sadeghi malekabadi, A.R. Davari

“…However, the extensive numerical computations required for accurate evaluation often hinder the implementation of multi-objective optimization strategies. This paper introduces an innovative approach to address this issue, leveraging a combination of neural network-based reduced order modeling and a multi-objective genetic algorithm. …”
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Short-Term Power Load Forecasting Using Adaptive Mode Decomposition and Improved Least Squares Support Vector Machine by Wenjie Guo, Jie Liu, Jun Ma, Zheng Lan

“…Different frequency features are effectively extracted by using the proposed combination kernel structure, which can achieve the balance of learning capacity and generalization capacity for each unique load component. Further, an optimized genetic algorithm is deployed to optimize model parameters in ILSSVM by integrating the adaptive genetic algorithm and simulated annealing to improve load forecasting accuracy. …”
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Predicting equilibrium scour depth around non-circular bridge piers with shallow foundations using hybrid explainable machine learning methods by Nasrin Eini, Saeid Janizadeh, Sayed M. Bateni, Changhyun Jun, Essam Heggy, Marek Kirs

“…This study combines two metaheuristic optimization techniques—Siberian tiger optimization (STO) and brown-bear optimization algorithms (BOA)—with artificial neural networks (ANNs) to enhance deq prediction accuracy for both round- and sharp-nosed piers using both field and laboratory data. …”
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Integration of multi agent reinforcement learning with golden jackal optimization for predicting average localization error in wireless sensor networks by K. Lakshmi Prabha, Hanan Abdullah Mengash, Hamed Alqahtani, Randa Allafi

“…The GJO algorithm fine-tunes the hyperparameters of MARL to improve generalization across different WSN configurations. …”
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Designing an explainable bio-inspired model for suspended sediment load estimation: eXtreme Gradient Boosting coupled with Marine Predators Algorithm by Roozbeh Moazenzadeh, Okan Mert Katipoğlu, Ahmadreza Shateri, Hamid Nasiri, Mohammed Abdallah

“…The superiority of the proposed model (XGB-MPA) compared to two other hybrid models, including XGB-PSO (Particle Swarm Optimization) and XGB-GWO (Grey Wolf Optimization) was also investigated. …”
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Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods by Babak Pourakbari, Setareh Mamishi, Sepideh Keshavarz Valian, Shima Mahmoudi, Reihaneh Hosseinpour Sadeghi, Mohammad Reza Abdolsalehi, Mahmoud Khodabandeh, Mohammad Farahmand

“…Integrating these predictive models into clinical practice could support early identification of high-risk patients and optimize clinical decision-making.…”
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Program Scheduling With Multi-Skill and External Resource Coordination Consideration Using Improved NSGA-II: Case Study by Heng Zhang, Jingbo Zhou, Huaying Ruan, Yixuan Qin

“…A case study was then developed based on the R program of technology-driven company S. The allocation model was solved using both the classic and improved versions of the algorithm. …”
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A Novel High-Precision Workpiece Self-Positioning Method for Improving the Convergence Ratio of Optical Components in Magnetorheological Finishing by Yiang Zhang, Pengxiang Wang, Chaoliang Guan, Meng Liu, Xiaoqiang Peng, Hao Hu

“…A composite data acquisition method using both a camera and probe is designed, and a stepwise global optimization model is constructed by integrating a synchronous iterative localization algorithm with the Non-dominated Sorting Genetic Algorithm II (NSGA-II). …”
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Optimized CNN-LSTM with hybrid metaheuristic approaches for solar radiation forecasting by İrem Fatma Şener, İhsan Tuğal

“…To improve the accuracy of the model, hyperparameter optimization is applied to the CNN-LSTM model using three metaheuristic algorithms: Particle Swarm Optimization, Grey Wolf Optimization, and Starfish Optimization Algorithm. …”
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A Hybrid Deep Learning-ViT Model and A Meta-Heuristic Feature Selection Algorithm for Efficient Remote Sensing Image Classification by Bilal Ahmed, Syed Rameez Naqvi, Tallha Akram, Lu Peng, Fahdah Almarshad

“…In this study, we introduced XNANet, a self-attention-based CNN network for image classification. Bayesian optimization has been used to initialize the hyperparameters of the proposed model to improve training on the radiographic images. …”
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Research on bearing fault diagnosis based on ISA-VMD and IMSE by Feng Yan

“…This method leverages an improved sparrow search algorithm (ISSA) to optimize the variational mode decomposition (VMD) method and employs an improved multiscale sample entropy (IMSE) for fault feature extraction. …”
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Research on Performance Predictive Model and Parameter Optimization of Pneumatic Drum Seed Metering Device Based on Backpropagation Neural Network by Yilong Pan, Yaxin Yu, Junwei Zhou, Wenbing Qin, Qiang Wang, Yinghao Wang

“…The MOPSO algorithm uses the BPNN predictive model as a fitness function to search for the optimal solution for three types of seeds, and the optimized results were verified through bench experiments. …”
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Improving health-promoting workplaces through interdisciplinary approaches. The example of WISEWORK-C, a cluster of five work and health projects within Horizon-Europe by Deborah De Moortel, Michelle C Turner, Ella Arensman, Alex Binh Vinh Duc Nguyen, Víctor Gonzalez

“…These shifts are giving rise to new forms of work (eg, hybrid work, gig economy jobs) and reshaping management and work organization practices (eg, through algorithmic decision-making or digital monitoring of worker performance). …”
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Transformer Fault Diagnosis Utilizing Feature Extraction and Ensemble Learning Model by Gonglin Xu, Mei Zhang, Wanli Chen, Zhihui Wang

“…The final step involves optimizing the Light Gradient Boosting Machine (LightGBM) model using IAOS algorithm for transformer fault classification; this model is an ensemble learning model. …”
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Design of an efficient multi-objective recognition approach for 8-ball billiards vision system by Jiaying Gao, Qiuyang He, Hong Gao, Zhixin Zhan, Zhe Wu

“…In order to accurately locate and isolate the single ball in a local region, the improved Hough Transform (HT) algorithm and the Least Squares (LS) method are adopted in combination. …”
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A Particle Swarm Optimization-Based Ensemble Broad Learning System for Intelligent Fault Diagnosis in Safety-Critical Energy Systems with High-Dimensional Small Samples by Jiasheng Yan, Yang Sui, Tao Dai

“…In order to reduce the computational cost of the EBLS, which is constrained by the selection of its hyperparameters, the PSO algorithm is employed to optimize the hyperparameters of the EBLS. …”
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