Appl. Comput. Intell. Soft Comput.最新文献

Reliable Breast Cancer Diagnosis with Deep Learning: DCGAN-Driven Mammogram Synthesis and Validity Assessment 利用深度学习进行可靠的乳腺癌诊断：DCGAN 驱动的乳房 X 线照片合成与有效性评估

Appl. Comput. Intell. Soft Comput. Pub Date : 2024-02-19 DOI: 10.1155/2024/1122109

Dilawar Shah, Mohammad Asmat Ullah Khan, Mohammad Abrar

{"title":"Reliable Breast Cancer Diagnosis with Deep Learning: DCGAN-Driven Mammogram Synthesis and Validity Assessment","authors":"Dilawar Shah, Mohammad Asmat Ullah Khan, Mohammad Abrar","doi":"10.1155/2024/1122109","DOIUrl":"https://doi.org/10.1155/2024/1122109","url":null,"abstract":"Breast cancer imaging is paramount to quickly detecting and accurately evaluating the disease. The scarcity of annotated mammogram data presents a significant obstacle when building deep learning models that can produce reliable outcomes. This paper proposes a novel approach that utilizes deep convolutional generative adversarial networks (DCGANs) to effectively tackle the issue of limited data availability. The main goal is to produce synthetic mammograms that accurately reproduce the intrinsic patterns observed in real data, enhancing the current dataset. The proposed synthesis method is supported by thorough experimentation, demonstrating its ability to reproduce diverse viewpoints of the breast accurately. A mean similarity assessment with a standard deviation was performed to evaluate the credibility of the synthesized images and establish the clinical significance of the data obtained. A thorough evaluation of the uniformity within each class was conducted, and any deviations from each class’s mean values were measured. Including outlier removal using a specified threshold is a crucial process element. This procedure improves the accuracy level of each image cluster and strengthens the synthetic dataset’s general dependability. The visualization of the class clustering results highlights the alignment between the produced images and the inherent distribution of the data. After removing outliers, distinct and consistent clusters of homogeneous data points were observed. The proposed similarity assessment demonstrates noteworthy effectiveness, eliminating redundant and dissimilar images from all classes. Specifically, there are 505 instances in the normal class, 495 instances in the benign class, and 490 instances in the malignant class out of 600 synthetic mammograms for each class. To check the further validity of the proposed model, human experts visually inspected and validated synthetic images. This highlights the effectiveness of our methodology in identifying substantial outliers.","PeriodicalId":8218,"journal":{"name":"Appl. Comput. Intell. Soft Comput.","volume":"65 2","pages":"1122109:1-1122109:13"},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140449540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A space-reduction based three-phase approach for large-scale optimization 基于空间缩减的三阶段大规模优化方法

Appl. Comput. Intell. Soft Comput. Pub Date : 2023-09-01 DOI: 10.2139/ssrn.4327138

Haiyan Liu, Yuan Cheng, Siyan Xue, Shouheng Tuo

引用次数: 0

Q-learning-based hyper-heuristic evolutionary algorithm for the distributed assembly blocking flowshop scheduling problem 基于q学习的分布式装配阻塞流车间调度问题的超启发式进化算法

Appl. Comput. Intell. Soft Comput. Pub Date : 2023-08-01 DOI: 10.2139/ssrn.4327145

Zi-Qi Zhang, B. Qian, Rong Hu, Jianxin Yang

引用次数: 0

Biparty multiobjective optimal power flow: The problem definition and an evolutionary approach 双方多目标最优潮流:问题定义与演化方法

Appl. Comput. Intell. Soft Comput. Pub Date : 2023-08-01 DOI: 10.2139/ssrn.4381246

Yatong Chang, Wenjian Luo, Xin Lin, Zhen Song, C. Coello

引用次数: 0

Geometric Degree Reduction of Wang-Ball Curves 王球曲线的几何降度

Appl. Comput. Intell. Soft Comput. Pub Date : 2023-07-25 DOI: 10.1155/2023/5483111

Yusuf Fatihu Hamza, M. F. Hamza, A. Rababah, Salisu Ibrahim

{"title":"Geometric Degree Reduction of Wang-Ball Curves","authors":"Yusuf Fatihu Hamza, M. F. Hamza, A. Rababah, Salisu Ibrahim","doi":"10.1155/2023/5483111","DOIUrl":"https://doi.org/10.1155/2023/5483111","url":null,"abstract":"There are substantial methods of degree reduction in the literature. Existing methods share some common limitations, such as lack of geometric continuity, complex computations, and one-degree reduction at a time. In this paper, an approximate geometric multidegree reduction algorithm of Wang–Ball curves is proposed. \u0000 \u0000 \u0000 \u0000 G\u0000 \u0000 \u0000 0\u0000 \u0000 \u0000 \u0000 -, \u0000 \u0000 \u0000 \u0000 G\u0000 \u0000 \u0000 1\u0000 \u0000 \u0000 \u0000 -, and \u0000 \u0000 \u0000 \u0000 G\u0000 \u0000 \u0000 2\u0000 \u0000 \u0000 \u0000 -continuity conditions are applied in the degree reduction process to preserve the boundary control points. The general equation for high-order (G2 and above) multidegree reduction algorithms is nonlinear, and the solutions of these nonlinear systems are quite expensive. In this paper, \u0000 \u0000 \u0000 \u0000 C\u0000 \u0000 \u0000 1\u0000 \u0000 \u0000 \u0000 -continuity conditions are imposed besides the \u0000 \u0000 \u0000 \u0000 G\u0000 \u0000 \u0000 2\u0000 \u0000 \u0000 \u0000 -continuity conditions. While some existing methods only achieve the multidegree reduction by repeating the one-degree reduction method recursively, our proposed method achieves multidegree reduction at once. The distance between the original curve and the degree-reduced curve is measured with the \u0000 \u0000 \u0000 \u0000 L\u0000 \u0000 \u0000 2\u0000 \u0000 \u0000 \u0000 -norm. Numerical example and figures are presented to state the adequacy of the algorithm. The proposed method not only outperforms the existing method of degree reduction of Wang–Ball curves but also guarantees geometric continuity conditions at the boundary points, which is very important in CAD and geometric modeling.","PeriodicalId":8218,"journal":{"name":"Appl. Comput. Intell. Soft Comput.","volume":"17 7 1","pages":"5483111:1-5483111:10"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88628805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A bipolar fuzzy p-competition graph based ARAS technique for prioritizing COVID-19 vaccines 基于双极模糊p竞争图的ARAS技术对COVID-19疫苗进行优先排序

Appl. Comput. Intell. Soft Comput. Pub Date : 2023-07-01 DOI: 10.2139/ssrn.4327160

Deva Nithyanandham, Felix Augustin

引用次数: 2

Graph-based density peak merging for identifying multi-peak clusters 基于图的密度峰合并多峰聚类识别

Appl. Comput. Intell. Soft Comput. Pub Date : 2023-07-01 DOI: 10.2139/ssrn.4394382

Mi-Sung Han, Jong-Seok Lee

引用次数: 0

An efficient evolutionary algorithm for high-speed train rescheduling under a partial station blockage 局部站点阻塞下高速列车调度的一种高效进化算法

Appl. Comput. Intell. Soft Comput. Pub Date : 2023-07-01 DOI: 10.2139/ssrn.4327148

Rongsheng Wang, Qi Zhang, X. Dai, Zhiming Yuan, Tao Zhang, Shuxin Ding, Yaochu Jin

引用次数: 0

An artificial immune differential evolution algorithm for scheduling a distributed heterogeneous flexible flowshop 分布式异构柔性流水车间调度的人工免疫差分进化算法

Appl. Comput. Intell. Soft Comput. Pub Date : 2023-07-01 DOI: 10.2139/ssrn.4344018

H. Xuan, Wenting Li, Bing Li

引用次数: 1

A direction vector-guided multi-objective evolutionary algorithm for variable linkages problems 一种方向矢量导向的变连杆问题多目标进化算法

Appl. Comput. Intell. Soft Comput. Pub Date : 2023-07-01 DOI: 10.2139/ssrn.4327147

Qinghua Gu, Shaopeng Zhang, Qian Wang, Neal N. Xiong

引用次数: 1