Int. J. Comput. Intell. Appl.最新文献_第5页

Collaborative Learning to Improve the Non-uniqueness of NMF 协同学习改进NMF的非唯一性

Int. J. Comput. Intell. Appl. Pub Date : 2022-03-01 DOI: 10.1142/s1469026822500018

Kaoutar Benlamine, Younès Bennani, Basarab Matei, Nistor Grozavu, Issam Falih

引用次数: 2

Padeep: A Patched Deep Learning Based Model for Plants Recognition on Small Size Dataset: Chenopodiaceae Case Study Padeep:基于补丁深度学习的小数据集植物识别模型:藜科案例研究

Int. J. Comput. Intell. Appl. Pub Date : 2022-03-01 DOI: 10.1142/s1469026822500055

Ahmad Heidary-Sharifabad, M. S. Zarchi, G. Zarei

{"title":"Padeep: A Patched Deep Learning Based Model for Plants Recognition on Small Size Dataset: Chenopodiaceae Case Study","authors":"Ahmad Heidary-Sharifabad, M. S. Zarchi, G. Zarei","doi":"10.1142/s1469026822500055","DOIUrl":"https://doi.org/10.1142/s1469026822500055","url":null,"abstract":"A large training sample is prerequisite for the successful training of each deep learning model for image classification. Collecting a large dataset is time-consuming and costly, especially for plants. When a large dataset is not available, the challenge is how to use a small or medium size dataset to train a deep model optimally. To overcome this challenge, a novel model is proposed to use the available small size plant dataset efficiently. This model focuses on data augmentation and aims to improve the learning accuracy by oversampling the dataset through representative image patches. To extract the relevant patches, ORB key points are detected in the training images and then image patches are extracted using an innovative algorithm. The extracted ORB image patches are used for dataset augmentation to avoid overfitting during the training phase. The proposed model is implemented using convolutional neural layers, where its structure is based on ResNet architecture. The proposed model is evaluated on a challenging ACHENY dataset. ACHENY is a Chenopodiaceae plant dataset, comprising 27030 images from 30 classes. The experimental results show that the patch-based strategy outperforms the classification accuracy achieved by traditional deep models by 9%.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134245911","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

Optimized Feature Selection in Software Product Lines using Discrete Bat Algorithm 基于离散Bat算法的软件产品线特征选择优化

Int. J. Comput. Intell. Appl. Pub Date : 2022-03-01 DOI: 10.1142/s1469026822500031

Hajar Sadeghi, Shohreh Ajoudanian

{"title":"Optimized Feature Selection in Software Product Lines using Discrete Bat Algorithm","authors":"Hajar Sadeghi, Shohreh Ajoudanian","doi":"10.1142/s1469026822500031","DOIUrl":"https://doi.org/10.1142/s1469026822500031","url":null,"abstract":"Software Product Lines (SPLs) are one of the ways to develop software products by increasing productivity and reducing cost and time in the software development process. In SPLs, each product has many features and it is necessary to consider the optimal and custom features of the products. In fact, selecting key features in SPLs is a challenging process. Feature selection in SPLs is an optimization problem and an NP-Hard problem. One way to select a feature is to use meta-heuristic algorithms modeled on nature, i.e., Bat Algorithm. By modeling bat behavior in prey hunting, a suitable meta-innovative algorithm is considered. This algorithm has important advantages that make it more accurate than conventional methods such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. In this paper, to select software product features, idol binary algorithm and artificial neural network are used to identify important features of software products that reduce production costs and time. The experiments in MATLAB software and datasets related to software production lines show that the rate of reduction of target performance error or feature selection cost in software production lines in the proposed method has decreased by 64.17% with increasing population.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132053069","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: 1

A Fuzzy Strategy to Eliminate Uncertainty in Grading Positive Tuberculosis 消除结核阳性分级不确定性的模糊策略

Int. J. Comput. Intell. Appl. Pub Date : 2022-03-01 DOI: 10.1142/s1469026822500067

R. Samuel, B. R. Kanna

{"title":"A Fuzzy Strategy to Eliminate Uncertainty in Grading Positive Tuberculosis","authors":"R. Samuel, B. R. Kanna","doi":"10.1142/s1469026822500067","DOIUrl":"https://doi.org/10.1142/s1469026822500067","url":null,"abstract":"Sputum smear microscopic examination is an effective, fast, and low-cost technique that is highly specific in areas with a high prevalence of pulmonary tuberculosis. Since manual screening needs trained pathologist in high prevalence zones, the possibility of deploying adequate technicians during the epidemic sessions would be impractical. This condition can cause overburdening and fatigue of working technicians which may tend to reduce the potential efficiency of Tuberculosis (TB) diagnosis. Hence, automation of sputum inspection is the most appropriate aspect in TB outbreak zones to maximize the detection ability. Sputum collection, smear preparing, staining, interpreting smears, and reporting of TB severity are all part of the diagnosis of tuberculosis. This study has analyzed the risk of automating TB severity grading. According to the findings of the analysis, numerous TB-positive cases do not fit into the standard TB severity grade while applying direct rule-driven strategy. The manual investigation, on the other hand, arbitrarily labels the TB grade on those cases. To counter the risk of automation, a fuzzy-based Tuberculosis Severity Level Categorizing Algorithm (TSLCA) is introduced to eliminate uncertainty in classifying the level of TB infection. TSLCA introduces the weight factors, which are dependent on the existence of maximum number of Acid-Fast Bacilli (AFB) per microscopic Field of View (FOV). The fuzzification and defuzzification operations are carried out using the triangular membership function. In addition, the [Formula: see text]-cut approach is used to eliminate the ambiguity in TB severity grading. Several uncertain TB microscopy screening reports are tested using the proposed TSLCA. Based on the experimental results, it is observed that the TB grading by TSLCA is consistent, error-free, significant and fits exactly into the standard criterion. As a result of the proposed TSLCA, the uncertainty of grading is eliminated and the reliability of tuberculosis diagnosis is ensured when adapting automatic diagnosis.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129225226","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

The Augmentation Data of Retina Image for Blood Vessel Segmentation Using U-Net Convolutional Neural Network Method 基于U-Net卷积神经网络的视网膜图像增强数据血管分割

Int. J. Comput. Intell. Appl. Pub Date : 2022-03-01 DOI: 10.1142/s1469026822500043

Asri Safmi, Anita Desiani, B. Suprihatin

引用次数: 4

Adaptive Optimization-Enabled Neural Networks to Handle the Imbalance Churn Data in Churn Prediction 基于自适应优化的神经网络处理客户流失预测中的不平衡数据

Int. J. Comput. Intell. Appl. Pub Date : 2021-12-15 DOI: 10.1142/s1469026821500255

Bharathi Garimella, G. Prasad, M. K. Prasad

{"title":"Adaptive Optimization-Enabled Neural Networks to Handle the Imbalance Churn Data in Churn Prediction","authors":"Bharathi Garimella, G. Prasad, M. K. Prasad","doi":"10.1142/s1469026821500255","DOIUrl":"https://doi.org/10.1142/s1469026821500255","url":null,"abstract":"The churn prediction based on telecom data has been paid great attention because of the increasing the number telecom providers, but due to inconsistent data, sparsity, and hugeness, the churn prediction becomes complicated and challenging. Hence, an effective and optimal prediction of churns mechanism, named adaptive firefly-spider optimization (adaptive FSO) algorithm, is proposed in this research to predict the churns using the telecom data. The proposed churn prediction method uses telecom data, which is the trending domain of research in predicting the churns; hence, the classification accuracy is increased. However, the proposed adaptive FSO algorithm is designed by integrating the spider monkey optimization (SMO), firefly optimization algorithm (FA), and the adaptive concept. The input data is initially given to the master node of the spark framework. The feature selection is carried out using Kendall’s correlation to select the appropriate features for further processing. Then, the selected unique features are given to the master node to perform churn prediction. Here, the churn prediction is made using a deep convolutional neural network (DCNN), which is trained by the proposed adaptive FSO algorithm. Moreover, the developed model obtained better performance using the metrics, like dice coefficient, accuracy, and Jaccard coefficient by varying the training data percentage and selected features. Thus, the proposed adaptive FSO-based DCNN showed improved results with a dice coefficient of 99.76%, accuracy of 98.65%, Jaccard coefficient of 99.52%.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128208863","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

Real-Time Human Action Recognition Using Deep Learning Architecture 使用深度学习架构的实时人体动作识别

Int. J. Comput. Intell. Appl. Pub Date : 2021-11-17 DOI: 10.1142/s1469026821500267

S. Kahlouche, M. Belhocine, Abdallah Menouar

引用次数: 1

Quadratic Convex Reformulation for Solving Task Assignment Problem with Continuous Hopfield Network 求解连续Hopfield网络任务分配问题的二次凸重构

Int. J. Comput. Intell. Appl. Pub Date : 2021-11-11 DOI: 10.1142/s1469026821500243

Youssef Hami, Chakir Loqman

引用次数: 0

Optimal Sizing and Location of Distributed Generators for Power Flow Analysis in Smart Grid Using IAS-MVPA Strategy 基于IAS-MVPA策略的智能电网潮流分析中分布式发电机的优化尺寸和位置

Int. J. Comput. Intell. Appl. Pub Date : 2021-11-06 DOI: 10.1142/s1469026821500279

Kumar Cherukupalli, N. VijayaAnand

{"title":"Optimal Sizing and Location of Distributed Generators for Power Flow Analysis in Smart Grid Using IAS-MVPA Strategy","authors":"Kumar Cherukupalli, N. VijayaAnand","doi":"10.1142/s1469026821500279","DOIUrl":"https://doi.org/10.1142/s1469026821500279","url":null,"abstract":"In this paper, the optimal distribution generation (DG) size and location for power flow analysis at the smart grid by hybrid method are proposed. The proposed hybrid method is the Interactive Autodidactic School (IAS) and the Most Valuable Player Algorithm (MVPA) and commonly named as IAS-MVPA method. The main aim of this work is to reduce line loss and total harmonic distortion (THD), similarly, to recover the voltage profile of system through the optimal location and size of the distributed generators and optimal rearrangement of network. Here, IAS-MVPA method is utilized as a rectification tool to get the maximum DG size and the maximal reconfiguration of network at environmental load variation. In case of failure, the IAS method is utilized for maximizing the DG location. The IAS chooses the line of maximal power loss as optimal location to place the DG based on the objective function. The fault violates the equality and inequality restrictions of the safe limit system. From the control parameters, the low voltage drift is improved using the MVPA method. The low-voltage deviation has been exploited for obtaining the maximum capacity of the DG. After that, the maximum capacity is used at maximum location that improves the power flow of the system. The proposed system is performed on MATLAB/Simulink platform, and the effectiveness is assessed by comparing it with various existing processes such as generic algorithm (GA), Cuttle fish algorithm (CFA), adaptive grasshopper optimization algorithm (AGOA) and artificial neural network (ANN).","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127534615","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

Optimization of Interval Type-2 Intuitionistic Fuzzy Logic System for Prediction Problems 区间2型直觉模糊逻辑系统预测问题的优化

Int. J. Comput. Intell. Appl. Pub Date : 2021-11-03 DOI: 10.1142/s146902682150022x

Imo J. Eyoh, J. Eyoh, U. Umoh, R. Kalawsky

引用次数: 1