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

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

IF 2.9 4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-09-07 DOI: 10.1007/s44196-023-00324-4

Binbin Huang, Ciyu Wang

引用次数: 0

A Graph Representation Learning Framework Predicting Potential Multivariate Interactions 预测潜在多元交互的图表示学习框架

IF 2.9 4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-09-05 DOI: 10.1007/s44196-023-00329-z

Yanlin Yang, Zhonglin Ye, Haixing Zhao, Lei Meng

引用次数: 0

Multi-temporal Sequential Recommendation Model Based on the Fused Learning Preferences 基于融合学习偏好的时序推荐模型

IF 2.9 4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-09-01 DOI: 10.1007/s44196-023-00310-w

Jianxia Chen, Liwei Pan, Shi Dong, Tianci Yu, Liang Xiao, Meihan Yao, Shijie Luo

引用次数: 0

E-commerce User Recommendation Algorithm Based on Social Relationship Characteristics and Improved K-Means Algorithm 基于社会关系特征和改进K-Means算法的电子商务用户推荐算法

IF 2.9 4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-08-31 DOI: 10.1007/s44196-023-00321-7

X. Shen

引用次数: 0

A Deep Learning-Based Multi-objective Optimization Model for PM2.5 Prediction 基于深度学习的PM2.5预测多目标优化模型

IF 2.9 4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-08-30 DOI: 10.1007/s44196-023-00322-6

Wenkai Xu, Fengchen Fu, Qingqing Zhang, Lei Wang

引用次数: 0