Int. J. Comput. Intell. Appl.最新文献_第8页

Supervised Retinal Vessel Segmentation Based Average Filter and Iterative Self Organizing Data Analysis Technique 基于平均滤波和迭代自组织数据分析的监督视网膜血管分割技术

Int. J. Comput. Intell. Appl. Pub Date : 2020-10-24 DOI: 10.1142/s1469026821500036

Erwin, Heranti Reza Damayanti

{"title":"Supervised Retinal Vessel Segmentation Based Average Filter and Iterative Self Organizing Data Analysis Technique","authors":"Erwin, Heranti Reza Damayanti","doi":"10.1142/s1469026821500036","DOIUrl":"https://doi.org/10.1142/s1469026821500036","url":null,"abstract":"Retinal fundus is the inner surface of the eye associated with the lens. The identi¯cation of disease \u0000needs some parts of retinal fundus, such as blood vessel. Blood vessels are part of circulation system \u0000which functions to supply blood to retina area. This research proposed a method for segmentation of \u0000blood vessel in retinal image with Average Filter and Iterative SelfOrganizing Data Analysis \u0000(ISODATA) Technique. The ¯rst step with the input image changed to Gamma Correction, increasing \u0000contrast with Contrast Limited Adaptive Histogram Equalization (CLAHE), the ¯ltering process with \u0000Average Filter. The segmentation is used for ISODATA. Region of Interest was applied to take the \u0000center of a vessel object and remove the background. In the ¯nal stage, the process of noise reduction \u0000and removal of small pixel values with Median Filter and Closing Morphology. Datasets used in this \u0000research were DRIVE and STARE. The average result was obtained for STARE dataset with an \u0000accuracy of 94.41%, Sensitivity of 55.57%, Speci¯cation of 98.31%, F1 Score of 64.81% while for \u0000the DRIVE dataset with accuracy of 94.78%, Sensitivity of 43.46%, Speci¯cation of 99.81%, and F1 \u0000Score of 59.39%.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125981129","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: 4

Prediction of Mechanical Properties of Seamless Steel Tubes Using Artificial Neural Networks 基于人工神经网络的无缝钢管力学性能预测

Int. J. Comput. Intell. Appl. Pub Date : 2020-10-15 DOI: 10.1142/s1469026820500285

Ramon Santos Correa, Patricia Teixeira Sampaio, R. Braga, Victor Alberto Lambertucci, G. M. Almeida, A. Braga

{"title":"Prediction of Mechanical Properties of Seamless Steel Tubes Using Artificial Neural Networks","authors":"Ramon Santos Correa, Patricia Teixeira Sampaio, R. Braga, Victor Alberto Lambertucci, G. M. Almeida, A. Braga","doi":"10.1142/s1469026820500285","DOIUrl":"https://doi.org/10.1142/s1469026820500285","url":null,"abstract":"A bottleneck of laboratory analysis in process industries including steelmaking plants is the low sampling rate. Inference models using only variables measured online have then been used to made such information available in advance. This study develops predictive models for key mechanical properties of seamless steel tubes, by strength, ultimate tensile strength and hardness. A plant in Brazil was used as the case study. The sample sizes of some steel tube families given namely, yield a particular property are discrepant and sometimes very small. To overcome this sample imbalance and lack of representativeness, committees of predictive neural network models based on bagging predictors, a type of ensemble method, were adopted. As a result, all steel families for all properties have been satisfactorily described showing the correlations between targets and model estimates close to 99%. These results were compared to multiple linear regression, support vector machine and a simpler neural network. Such information available in advance favors corrective actions before complete tube production mitigating rework costs in general.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116040841","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

Prediction of Highly Volatile Cryptocurrency Prices Using Social Media 利用社交媒体预测高度波动的加密货币价格

Int. J. Comput. Intell. Appl. Pub Date : 2020-10-14 DOI: 10.1142/s146902682050025x

Mason McCoy, S. Rahimi

引用次数: 6

Gradient Descent Optimization Algorithms for Decoding SCMA Signals SCMA信号解码的梯度下降优化算法

Int. J. Comput. Intell. Appl. Pub Date : 2020-10-08 DOI: 10.1142/s1469026821500024

S. Vidal-Beltrán, J. López-Bonilla, F. Martínez-Piñón, Jesús Yalja-Montiel

引用次数: 3

One-Class Fault Detection Using Multi-Layer Elm-Based Auto-Encoder 基于多层榆树自编码器的一类故障检测

Int. J. Comput. Intell. Appl. Pub Date : 2020-10-07 DOI: 10.1142/s1469026821500012

Wuke Li, Yin Guangluan, Xiaoxiao Chen

{"title":"One-Class Fault Detection Using Multi-Layer Elm-Based Auto-Encoder","authors":"Wuke Li, Yin Guangluan, Xiaoxiao Chen","doi":"10.1142/s1469026821500012","DOIUrl":"https://doi.org/10.1142/s1469026821500012","url":null,"abstract":"A new approach for one-class fault detection trained only by normal samples has been proposed in this paper. The approach contains multi-anterior-layers for feature extraction and one post-layer for one-class classification. The multi-anterior-layers are based on extreme learning machine-based auto-encoder (ELM-AE). Multi-ELM-AEs are stacked in the front hidden layers to extract abstract features from the raw input. The post-layer is based on the reconstruction error-based ELM-AE (Re-ELM-AE) to act as one-class classifier. As the extension of ELM-AE, the decision threshold and function are given in the Re-ELM-AE, which are utilized to identify whether the test sample is faulty. The efficacy of the presented algorithm is demonstrated on a mathematic example and fault dataset from motor bearing. The method has been compared with shallow learning methods such as one-class support vector machine (OCSVM), the Re-ELM-AE, and one multi-layer neural network named stacked auto-encoder (SAE). The experiment results show that the proposed method outperforms OCSVM and Re-ELM-AE in classification accuracy. Though the classification accuracy of the proposed method and SAE is similar, the training and testing time of the proposed method is much lower than SAE.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114401246","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 Novel Energy-Efficient Clustering Protocol Using Two-Stage Genetic Algorithm for Improving the Lifetime of Wireless Sensor Networks 一种利用两阶段遗传算法提高无线传感器网络生存期的新型节能聚类协议

Int. J. Comput. Intell. Appl. Pub Date : 2020-09-24 DOI: 10.1142/S1469026820500194

A. Mahani, Ebrahim Farahmand, Saeideh Sheikhpour, Nooshin Taheri-Chatrudi

引用次数: 1

Arrhythmias Prediction Using an Hybrid Model Based on Convolutional Neural Network and Nonlinear Regression 基于卷积神经网络和非线性回归的心律失常预测混合模型

Int. J. Comput. Intell. Appl. Pub Date : 2020-09-02 DOI: 10.1142/s1469026820500248

Abdoul-Dalibou Abdou, N. Ngom, Oumar Niang

引用次数: 1

Mine Pressure Prediction Study Based on Fuzzy Cognitive Maps 基于模糊认知图的矿井压力预测研究

Int. J. Comput. Intell. Appl. Pub Date : 2020-09-01 DOI: 10.1142/s1469026820500236

Ye Li, Xiaohu Shi

引用次数: 0

Optimistic variants of single-objective bilevel optimization for evolutionary algorithms 进化算法单目标双层优化的乐观变体

Int. J. Comput. Intell. Appl. Pub Date : 2020-08-19 DOI: 10.1142/S1469026820500200

Anuraganand Sharma

{"title":"Optimistic variants of single-objective bilevel optimization for evolutionary algorithms","authors":"Anuraganand Sharma","doi":"10.1142/S1469026820500200","DOIUrl":"https://doi.org/10.1142/S1469026820500200","url":null,"abstract":"Single-objective bilevel optimization is a specialized form of constraint optimization problems where one of the constraints is an optimization problem itself. These problems are typically non-convex and strongly NP-Hard. Recently, there has been an increased interest from the evolutionary computation community to model bilevel problems due to its applicability in the real-world applications for decision-making problems. In this work, a partial nested evolutionary approach with a local heuristic search has been proposed to solve the benchmark problems and have outstanding results. This approach relies on the concept of intermarriage-crossover in search of feasible regions by exploiting information from the constraints. A new variant has also been proposed to the commonly used convergence approaches, i.e., optimistic and pessimistic. It is called extreme optimistic approach. The experimental results demonstrate the algorithm converges differently to known optimum solutions with the optimistic variants. Optimistic approach also outperforms pessimistic approach. Comparative statistical analysis of our approach with other recently published partial to complete evolutionary approaches demonstrates very competitive results.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127403045","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: 3

A Fuzzy Logic-Based Method to Avert Intrusions in Wireless Sensor Networks Using WSN-DS Dataset 基于模糊逻辑的WSN-DS数据集无线传感器网络入侵防范方法

Int. J. Comput. Intell. Appl. Pub Date : 2020-08-19 DOI: 10.1142/s1469026820500182

Neha Singh, Deepali Virmani, Xiao-zhi Gao

引用次数: 10