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hyperparameter optimization for Capsule Network Based Modified Hybrid Rice optimization Algorithm

Intelligent Automation & Soft Computing 2023年第8期37卷 2019-2035页

作者： Zhiwei Ye Ziqian Fang Zhina Song Haigang Sui Chunyan Yan Wen Zhou Mingwei Wang School of Computer Science and Technology Hubei University of TechnologyWuhan430068China State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhan430079China

hyperparameters play a vital impact in the performance of most machine learning *** is a challenge for traditional methods to con-figure hyperparameters of the capsule network to obtain high-performance *** swarm intelligence or evolutionary computation algorithms have been effectively employed to seek optimal hyperparameters as a com-binatorial optimization ***,these algorithms are prone to get trapped in the local optimal solution as random search strategies are *** inspiration for the hybrid rice optimization(HRO)algorithm is from the breeding technology of three-line hybrid rice in China,which has the advantages of easy implementation,less parameters and fast *** the paper,genetic search is combined with the hybrid rice optimization algorithm(GHRO)and employed to obtain the optimal hyperparameter of the capsule network automatically,that is,a probability search technique and a hybridization strategy belong with the primary *** benchmark functions are used to evaluate the performance of ***,the MNIST,Chest X-Ray(pneumonia),and Chest X-Ray(COVID-19&pneumonia)datasets are also utilized to evaluate the capsule network learnt by *** experimental results show that GHRO is an effective method for optimizing the hyperparameters of the capsule network,which is able to boost the performance of the capsule network on image classification.

关键词： hyperparameter optimization hybrid rice optimization algorithm genetic algorithm capsule network image classification

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hyperparameter optimization for cardiovascular disease data-driven prognostic system

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Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 218-244页

作者： Jayson Saputra Cindy Lawrencya Jecky Mitra Saini Suharjito Suharjito Industrial Engineering Department BINUS Graduate Program-Master of Industrial EngineeringBina Nusantara UniversityJakarta 11480Indonesia

Prediction and diagnosis of cardiovascular diseases(CVDs)based,among other things,on medical examinations and patient symptoms are the biggest challenges in *** 17.9 million people die from CVDs annually,accounting for 31%of all deaths *** a timely prognosis and thorough consideration of the patient’s medical history and lifestyle,it is possible to predict CVDs and take preventive measures to eliminate or control this life-threatening *** this study,we used various patient datasets from a major hospital in the United States as prognostic factors for *** data was obtained by monitoring a total of 918 patients whose criteria for adults were 28-77 years *** this study,we present a data mining modeling approach to analyze the performance,classification accuracy and number of clusters on Cardiovascular Disease Prognostic datasets in unsupervised machine learning(ML)using the Orange data mining *** techniques are then used to classify the model parameters,such as k-nearest neighbors,support vector machine,random forest,artificial neural network(ANN),naïve bayes,logistic regression,stochastic gradient descent(SGD),and *** determine the number of clusters,various unsupervised ML clustering methods were used,such as k-means,hierarchical,and density-based spatial clustering of applications with noise *** results showed that the best model performance analysis and classification accuracy were SGD and ANN,both of which had a high score of 0.900 on Cardiovascular Disease Prognostic *** on the results of most clustering methods,such as k-means and hierarchical clustering,Cardiovascular Disease Prognostic datasets can be divided into two *** prognostic accuracy of CVD depends on the accuracy of the proposed model in determining the diagnostic *** more accurate the model,the better it can predict which patients are at risk for CVD.

关键词： Cardiovascular disease Data-driven analytics Data mining hyperparameter optimization Orange data mining software Prognostic system Unsupervised machine learning

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hyperparameter optimization of apple leaf dataset for the disease recognition based on the YOLOv8

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JOURNAL OF AGRICULTURE AND FOOD RESEARCH 2025年 21卷

作者： Lee, Yong-Suk Patil, Maheshkumar Prakash Kim, Jeong Gyu Seo, Yong Bae Ahn, Dong-Hyun Kim, Gun-Do Pukyong Natl Univ Inst Food Sci Dept Food Sci & Technol Busan 48513 South Korea Pukyong Natl Univ Ind Univ Cooperat Fdn Busan 48513 South Korea Pukyong Natl Univ Dept Microbiol Busan 48513 South Korea

Apple leaf diseases have a major influence on apple productivity and quality, demanding a precise and efficient recognition system. Using the YOLOv8 family of object detection models, we created a disease recognition model for the apple leaf dataset in this study. The developed model was fine-tuned extensively by hyperparameter optimization to identify the best variant for practical deployment. Firstly, fine-tuning with different YOLOv8 series was conducted on an apple leaf dataset including various types of images. Among them, the YOLOv8s demonstrated the best balance with a fitness of 0.97171, a precision of 0.97082, a recall of 0.96837, a mAP@0.5 of 0.98016, and an image processing speed of 1.58 ms. Further hyperparameter optimization was conducted using the One-Factor-At-a-Time (OFAT) and Random Search (RS) methods. In this case, the optimal settings determined as per the OFAT method were a batch size of 48, a learning rate of 0.01, a weight decay of 0.0005, a momentum of 0.963, and 200 epochs. These settings were adopted as the baseline for RS. RS then searched for 50 additional configurations;the best configuration, C34 (batch size of 48, learning rate of 0.0137, momentum of 0.9433, and weight decay of 0.0009), achieved a fitness score of 0.97688, a precision of 0.97797, a recall of 0.97295, and a mAP@0.5 of 0.98257. The correlation analysis showed that learning rate and momentum significantly impacted the performance of the models. Overall, the C34 model demonstrates high accuracy, rapid processing speed, and robustness suitable for training real-time, large-scale apple leaf disease recognition.

关键词： Apple leaf disease YOLOv8 hyperparameter optimization One-factor-at-a-time Random search

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hyperparameter optimization for deep neural network models: a comprehensive study on methods and techniques

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INNOVATIONS IN SYSTEMS AND SOFTWARE ENGINEERING 2023年 1-12页

作者： Roy, Sunita Mehera, Ranjan Pal, Rajat Kumar Bandyopadhyay, Samir Kumar Univ Calcutta Dept Comp Sci & Engn JD-2Sect 3Salt Lake Kolkata 700106 West Bengal India Anodot Inc 20130 Lakeview Ctr PlSuite 400 Ashburn VA 20147 USA Bhawanipur Educ Soc Coll 5Elgin RdSreepallyBhowanipore Kolkata 700020 West Bengal India

Advancements in computing and storage technologies have significantly contributed to the adoption of deep learning (DL)-based models among machine learning experts. Although a generic model can be used in the search for a near-optimal solution in any problem domain, what makes these DL models context-sensitive is the combination of the training data and the hyperparameters. Due to the lack of inherent explainability of DL models, the hyperparameter optimization (HPO) or tuning specific to each model is a combination of art, science, and experience. In this article, we have explored various existing methods or ways to identify the optimal set of values for the hyperparameters specific to the DL models along with the techniques to realize those methods in real-life situations. The article also includes a detailed comparative study among various state-of-the-art HPO techniques using the Keras Tuner tuning toolkit and highlights the observations describing how the model performance can be improved by applying various HPO techniques.

关键词： Automated machine learning Brain tumour segmentation Deep learning Encoder-decoder hyperparameter optimization SA-Net

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hyperparameter optimization for interfacial bond strength prediction between fiber-reinforced polymer and concrete

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STRUCTURES 2023年 51卷 573-601页

作者： Kong, Qingzhao He, Chang Liao, Lijia Xu, Jia Yuan, Cheng Tongji Univ Dept Disaster Mitigat Struct Shanghai Peoples R China

This study proposed an intelligent method based on hyperparameter optimization to predict the strength of the interfacial bond between fiber-reinforced polymer (FRP) and concrete. Because ordinary machine learning models extract features manually, this requires additional time and causes errors in parameter selection. Highprecision machine learning model selection and automatic hyperparameter optimization can help overcome these limitations. Comparing eight different machine learning models (i.e., LReg, KNN, LR, MLP, BRR, SVR, LightGBM, and CBR), CatBoost was selected as the primary model for the hyperparameter optimization. CatBoost showed the best performance with the R-2 of 0.9394 and MAPE of 1.21%. According to the prediction results, the hyperparameter optimization reduced the dispersion degree by 90 %. In general, the machine learning model works better than the existing models in terms of the coefficient of determination, root mean square error, and coefficient of variation. Furthermore, the model enhanced by the hyperparameter optimization was better than the selected CatBoost model, which indicates that hyperparameter optimization is a reliable approach to improve the accuracy of the model.

关键词： Fibre reinforced polymer (FRP) Debonding Machine learning hyperparameter optimization

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hyperparameter optimization Using Iterative Decision Tree (IDT)

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IEEE ACCESS 2022年 10卷 106812-106827页

作者： Saum, Narith Sugiura, Satoshi Piantanakulchai, Mongkut Hokkaido Univ Div Engn & Policy Sustainable Environm Sapporo Hokkaido 0600808 Japan Thammasat Univ Sch Civil Engn & Technol Sirindhorn Int Inst Technol Pathum Thani 12121 Thailand

Machine learning and deep learning have gained a lot of attention from researchers because of their promising predictive performance and the availability of extensive high-dimensional data and high-performance computational hardware. However, the performance of these algorithms is susceptible to the choice of hyperparameters, while optimizing these hyperparameters is usually computationally expensive. For this reason, this study proposed a novel sequential search algorithm using decision tree regression as the surrogate function. In each iteration of this algorithm, several new combinations of hyperparameters were selected from a few best-performed leaves of the decision tree, called Iterative Decision Tree (IDT). Our approach could reduce the computational time from repetitive training the surrogate function compared to conventional sequential search algorithms and enable parallel computing. To confirm the effectiveness of the proposed algorithm, it was compared with six popular benchmark optimization algorithms, including Grid Search, Random Search, Bayesian optimization, Random Forest, Tree-Structured Parzen Estimation, and Genetic Algorithm. The comparison was examined by optimizing three benchmark nonconvex functions, and hyperparameter tuning of two machine learning algorithms (Support Vector Machine and Random Forest) and two deep learning models (Autoencoder and Convolutional Neural Networks). As a result, the proposed algorithm achieved competitive performance with high stability in addition to the feature importance metrics.

关键词： optimization Machine learning algorithms Linear programming Training Genetic algorithms Deep learning Search problems Decision tree deep learning feature importance hyperparameter optimization machine learning

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hyperparameter optimization of deep CNN classifier for plant species identification using artificial bee colony algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第7期14卷 8827-8838页

作者： Erkan, Ugur Toktas, Abdurrahim Ustun, Deniz Karamanoglu Mehmetbey Univ Engn Fac Dept Comp Engn TR-70200 Karaman Turkey Karamanoglu Mehmetbey Univ Engn Fac Dept Elect Elect Engn TR-70200 Karaman Turkey Tarsus Univ Engn Fac Dept Comp Engn TR-33400 Tarsus Mersin Turkey

Tailoring a deep convolutional neural network (CNN) for an implementation is a tedious and time-consuming task especially in image identification. In this study, an optimization scheme based on artificial bee colony (ABC) algorithm so-called optimal deep CNN (ODC) classifier for hyperparameter optimization of deep CNN is proposed for plant species identification. It is implemented to a ready-made leaf dataset namely Folio containing #637 images with 32 different plant species. The images are undergone various image preprocessing such as scaling, segmentation and augmentation so as to improve the efficacy of the ODC classifier. Therefore, the dataset is augmented from #637 to #15,288 leaf images whose #12,103 images is allocated for training phase and the remainder for testing the ODC. Moreover, a validation process on 20% of the training dataset is performed along with the training phase in both optimization and classification stages. The accuracy and loss performance of the ODC are examined over the training and validation results. The achieved ODC is verified through the test phase as well as by a comparison with the results in the literature in terms of performance evaluation metrics such as accuracy, sensitivity, specificity and F1-score. In order to further corroborate the proposed scheme, it is even subjected to a benchmark with optimization-based studies such as genetic, particle swarm and firefly algorithms through MNIST digit-image dataset. The ODC identifies the leaf images and digit-images with the best accuracy of 98.99% and 99.21% surpassing the state of the arts. Therefore, the proposed ODC is effective and useful in achieving an optimal CNN thanks to ABC algorithm.

关键词： Image processing Image identification Deep convolution neural network hyperparameter optimization Artificial bee colony algorithm

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hyperparameter optimization of a Parallelized LSTM for Time Series Prediction

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VIETNAM JOURNAL OF COMPUTER SCIENCE 2023年第3期10卷 303-328页

作者： Ozturk, Muhammed Maruf Suleyman Demirel Univ Fac Engn Dept Comp Engn Isparta Cunur Turkiye

Long Short-Term Memory (LSTM) Neural Network has great potential to predict sequential data. Time series prediction is one of the most popular experimental subjects of LSTM. To this end, various LSTM algorithms have been developed to predict time series data. However, there are a few works considering the hyperparameter optimization of LSTM along with parallelization approaches. To address this problem, a parallelized classic LSTM is proposed to predict time series. In the preprocessing phase, it first replaces missing values with zero and then normalizes the time series matrix. The transposed matrix is divided into training and testing parts. Consequently, a core-based parallelism is established, thereby utilizing forking to split prediction into multiple processes. Derivative-free optimization techniques are also analyzed to find out what sort of hyperparameter optimization technique is much more feasible for a parallelized LSTM. Further, a state-of-the-art comparison is included in the study. Experimental results show that training loss is optimal when using Nelder-Mead. Employing effort-intensive optimization methods such as genetic algorithms results in a remarkable CPU time reduction in parallelized designs according to the results. Last, the proposed algorithm outperformed the comparison methods with respect to the prediction errors.

关键词： Time series prediction hyperparameter optimization derivative-free optimization

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Penalty hyperparameter optimization with diversity measure for nonnegative low-rank approximation

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APPLIED NUMERICAL MATHEMATICS 2025年 208卷 189-204页

作者： Del Buono, Nicoletta Esposito, Flavia Selicato, Laura Zdunek, Rafal Univ Bari Aldo Moro Dept Math Via Orabona 4 I-70125 Bari Italy Natl Res Council IRSA CNR Water Res Inst Vle Francesco Blasio 5 I-70132 Bari Italy Wroclaw Univ Sci & Technol Fac Elect Photon & Microsyst 27 Wybrzeze Wyspianskiego St PL-50370 Wroclaw Poland

Learning tasks are often based on penalized optimization problems in which a sparse solution is desired. This can lead to more interpretative results by identifying a smaller subset of important features or components and reducing the dimensionality of the data representation, as well. In this study, we propose a new method to solve a constrained Frobenius norm-based nonnegative low- rank approximation, and the tuning of the associated penalty hyperparameter, simultaneously. The penalty term added is a particular diversity measure that is more effective for sparseness purposes than other classical norm-based penalties (i.e., 81 or 8 2,1 norms). As it is well known, setting the hyperparameters of an algorithm is not an easy task. Our work drew on developing an optimization method and the corresponding algorithm that simultaneously solves the sparsity-constrained nonnegative approximation problem and optimizes its associated penalty hyperparameters. We test the proposed method by numerical experiments and show its promising results on several synthetic and real datasets.

关键词： hyperparameter optimization Penalty coefficient Sparseness Low-rank approximation

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TransUNet Image 3D Reconstruction with hyperparameter optimization

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2025年第3期32卷 1133-1142页

作者： Wang, Xiaofang Luo, Zhihao Gou, Mingrui Mao, Kerui Zhou, Liang Geely Univ China Chengdu 641423 Sichuan Peoples R China

Ancient architecture is characterized by its complexity and exquisite structure, but most existing images are in 2D format. This study proposes a TransUNet-based 3D reconstruction method with hyperparameter optimization for depth prediction to enhance the effectiveness, accuracy, and efficiency of reconstructing ancient buildings from 2D images. The method employs Restricted Boltzmann Machine (RBM) for depth prediction and an optimized ant colony algorithm for network parameter optimization. Experiments demonstrate that the proposed method achieves an average F1 score of 96.8% in reconstructing ancient buildings, outperforming other algorithms in terms of processing time and efficiency. The results validate the superiority of the proposed algorithm in processing images of ancient architecture, improving measurement accuracy and reducing execution time. This study contributes to the digitization and preservation of cultural heritage.

关键词： hyperparameter optimization neural networks TransUNet 3D Reconstruction

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