International Journal of Computational Intelligence Systems最新文献_第6页

Analysis and Evaluation of Feature Selection and Feature Extraction Methods 特征选择和特征提取方法的分析与评价

4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-09-20 DOI: 10.1007/s44196-023-00319-1

Rubén E. Nogales, Marco E. Benalcázar

{"title":"Analysis and Evaluation of Feature Selection and Feature Extraction Methods","authors":"Rubén E. Nogales, Marco E. Benalcázar","doi":"10.1007/s44196-023-00319-1","DOIUrl":"https://doi.org/10.1007/s44196-023-00319-1","url":null,"abstract":"Abstract Hand gestures are widely used in human-to-human and human-to-machine communication. Therefore, hand gesture recognition is a topic of great interest. Hand gesture recognition is closely related to pattern recognition, where overfitting can occur when there are many predictors relative to the size of the training set. Therefore, it is necessary to reduce the dimensionality of the feature vectors through feature selection techniques. In addition, the need for portability in hand gesture recognition systems limits the use of deep learning algorithms. In this sense, a study of feature selection and extraction methods is proposed for the use of traditional machine learning algorithms. The feature selection methods analyzed are: maximum relevance and minimum redundancy (MRMR), Sequential, neighbor component analysis without parameters (NCAsp), neighbor component analysis with parameters (NCAp), Relief-F, and decision tree (DT). We also analyze the behavior of feature selection methods using classification and recognition accuracy and processing time. Feature selection methods were fed through seventeen feature extraction functions, which return a score proportional to its importance. The functions are then ranked according to their scores and fed to machine learning algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT). This work demonstrates that all feature selection methods evaluated on ANN provide better accuracy. In addition, the combination and number of feature extraction functions influence the accuracy and processing time.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136308352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Multilevel Fuzzy Evaluation of Cross-Border E-Commerce Profitability Model 跨境电子商务盈利模式的多层次模糊评价

4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-09-20 DOI: 10.1007/s44196-023-00332-4

Qijun Zou

引用次数: 0

Self-Supervised Learning for Industrial Image Anomaly Detection by Simulating Anomalous Samples 基于模拟异常样本的工业图像异常检测自监督学习

4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-09-20 DOI: 10.1007/s44196-023-00328-0

Mingjing Pei, Ningzhong Liu, Bing Zhao, Han Sun

引用次数: 0

Prediction of Aircraft Arrival Runway Occupancy Time Based on Machine Learning 基于机器学习的飞机到达跑道占用时间预测

4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-09-18 DOI: 10.1007/s44196-023-00333-3

Haoran Gao, Yubing Xie, Changjiang Yuan, Xin He, Tiantian Niu

引用次数: 0

Heap-Based Optimizer Algorithm with Chaotic Search for Nonlinear Programming Problem Global Solution 基于混沌搜索的堆优化算法求解非线性规划问题

4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-09-14 DOI: 10.1007/s44196-023-00327-1

Rizk M. Rizk-Allah, Islam M. Eldesoky, Ekram A. Aboali, Sarah M. Nasr

{"title":"Heap-Based Optimizer Algorithm with Chaotic Search for Nonlinear Programming Problem Global Solution","authors":"Rizk M. Rizk-Allah, Islam M. Eldesoky, Ekram A. Aboali, Sarah M. Nasr","doi":"10.1007/s44196-023-00327-1","DOIUrl":"https://doi.org/10.1007/s44196-023-00327-1","url":null,"abstract":"Abstract In this paper, a heap-based optimizer algorithm with chaotic search has been presented for the global solution of nonlinear programming problems. Heap-based optimizer (HBO) is a modern human social behavior-influenced algorithm that has been presented as an effective method to solve nonlinear programming problems. One of the difficulties that faces HBO is that it falls into locally optimal solutions and does not reach the global solution. To recompense the disadvantages of such modern algorithm, we integrate a heap-based optimizer with a chaotic search to reach the global optimization for nonlinear programming problems. The proposed algorithm displays the advantages of both modern techniques. The robustness of the proposed algorithm is inspected on a wide scale of different 42 problems including unimodal, multi-modal test problems, and CEC-C06 2019 benchmark problems. The comprehensive results have shown that the proposed algorithm effectively deals with nonlinear programming problems compared with 11 highly cited algorithms in addressing the tasks of optimization. As well as the rapid performance of the proposed algorithm in treating nonlinear programming problems has been proved as the proposed algorithm has taken less time to find the global solution.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134911740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PolySeg Plus: Polyp Segmentation Using Deep Learning with Cost Effective Active Learning PolySeg Plus:使用具有成本效益的主动学习的深度学习的多边形分割

4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-09-14 DOI: 10.1007/s44196-023-00330-6

Abdelrahman I. Saad, Fahima A. Maghraby, Osama Badawy

{"title":"PolySeg Plus: Polyp Segmentation Using Deep Learning with Cost Effective Active Learning","authors":"Abdelrahman I. Saad, Fahima A. Maghraby, Osama Badawy","doi":"10.1007/s44196-023-00330-6","DOIUrl":"https://doi.org/10.1007/s44196-023-00330-6","url":null,"abstract":"Abstract A deep convolution neural network image segmentation model based on a cost-effective active learning mechanism is proposed and named PolySeg Plus. It is intended to address polyp segmentation with a lack of labeled data and a high false-positive rate of polyp discovery. In addition to applying active learning, which assisted in labeling more image samples, a comprehensive polyp dataset formed of five benchmark datasets was generated to increase the number of images. To enhance the captured image features, the locally shared feature method is used, which utilizes the power of employing neighboring features together with one another to improve the quality of image features and overcome the drawbacks of the Conditional Random Features method. Medical image segmentation was performed using ResUNet++, ResUNet, UNet++, and UNet models. Gaussian noise was removed from the images using a gaussian filter, and the images were then augmented before being fed into the models. In addition to optimizing model performance through hyperparameter tuning, grid search is used to select the optimum parameters to maximize model performance. The results demonstrated a significant improvement and applicability of the proposed method in polyp segmentation when compared to state-of-the-art methods on the datasets CVC-ClinicDB, CVC-ColonDB, ETIS Larib Polyp DB, KVASIR-SEG, and Kvasir-Sessile, with Dice coefficients of 0.9558, 0.8947, 0.7547, 0.9476, and 0.6023, respectively. Not only did the suggested method improve the dice coefficients on the individual datasets, but it also produced better results on the comprehensive dataset, which will contribute to the development of computer-aided diagnosis systems.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134912354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved Golden Jackal Optimization Algorithm Using Opposition-Based Learning for Global Optimization and Engineering Problems 面向全局优化和工程问题的基于对立学习的改进金豺优化算法

4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-09-12 DOI: 10.1007/s44196-023-00320-8

Sarada Mohapatra, Prabhujit Mohapatra

引用次数: 1

Research on Financial Risk Evaluation and Control of Tourism Enterprises Based on Improved GA Algorithm 基于改进遗传算法的旅游企业财务风险评价与控制研究

IF 2.9 4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-09-08 DOI: 10.1007/s44196-023-00317-3

Ping Chen

引用次数: 0

Research on Data Analysis of Efficient Innovation and Entrepreneurship Practice Teaching Based on LightGBM Classification Algorithm 基于LightGBM分类算法的高效创新创业实践教学数据分析研究