International journal of hybrid intelligent systems最新文献_第4页

Hierarchical genetic optimization of convolutional neural models for diabetic retinopathy classification 糖尿病视网膜病变分类卷积神经模型的层次遗传优化

International journal of hybrid intelligent systems Pub Date : 2022-04-21 DOI: 10.3233/his-220004

Rodrigo Cordero-Martínez, D. Sánchez, P. Melin

{"title":"Hierarchical genetic optimization of convolutional neural models for diabetic retinopathy classification","authors":"Rodrigo Cordero-Martínez, D. Sánchez, P. Melin","doi":"10.3233/his-220004","DOIUrl":"https://doi.org/10.3233/his-220004","url":null,"abstract":"Diabetic retinopathy (DR) is one of the worse conditions caused by diabetes mellitus (DM). DR can leave the patient completely blind because it may have no symptoms in its initial stages. Expert physicians have been developing technologies for early detection and classification of DR to prevent the increasing number of patients. Some authors have used convolutional neural networks for this purpose. Pre-processing methods for database are important to increase the accuracy detection of CNN, and the use for an optimization algorithm can further increase that accuracy. In this work, four pre-processing methods are presented to compare them and select the best one. Then the use of a hierarchical genetic algorithm (HGA) with the pre-processing method is done with the intention of increasing the classification accuracy of a new CNN model. Using the HGA increases the accuracies obtained by the pre-processing methods and outperforms the results obtained by other authors. In the binary study case (detection of DR) a 0.9781 in the highest accuracy was achieved, a 0.9650 in mean accuracy and 0.007665 in standard deviation. In the multi-class study case (classification of DR) a 0.7762 in the highest accuracy, 0.7596 in mean accuracy and 0.009948 in standard deviation.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"38 1","pages":"97-109"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76055440","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

Machine learning-based authorship attribution using token n-grams and other time tested features 基于机器学习的作者归属，使用令牌n-gram和其他经过时间考验的特征

International journal of hybrid intelligent systems Pub Date : 2022-04-21 DOI: 10.3233/his-220005

S. Gupta, Swarupa Das, Jyotish Ranjan Mallik

{"title":"Machine learning-based authorship attribution using token n-grams and other time tested features","authors":"S. Gupta, Swarupa Das, Jyotish Ranjan Mallik","doi":"10.3233/his-220005","DOIUrl":"https://doi.org/10.3233/his-220005","url":null,"abstract":"Authorship Attribution is a process to determine and/or identify the author of a given text document. The relevance of this research area comes to the fore when two or more writers claim to be the prospective authors of an unidentified or anonymous text document or are unwilling to accept any authorship. This research work aims to utilize various Machine Learning techniques in order to solve the problem of author identification. In the proposed approach, a number of textual features such as Token n-grams, Stylometric features, bag-of-words and TF-IDF have been extracted. Experimentation has been performed on three datasets viz. Spooky Author Identification dataset, Reuter_50_50 dataset and Manual dataset with 3 different train-test split ratios viz. 80-20, 70-30 and 66.67-33.33. Models have been built and tested with supervised learning algorithms such as Naive Bayes, Support Vector Machine, K-Nearest Neighbor, Decision Tree and Random Forest. The proposed system yields promising results. For the Spooky dataset, the best accuracy score obtained is 84.14% with bag-of-words using Naïve Bayes classifier. The best accuracy score of 86.2% is computed for the Reuter_50_50 dataset with 2100 most frequent words when the classifier used is Support Vector Machine. For the Manual dataset, the best score of 96.67% is obtained using the Naïve Bayes Classification Model with both 5-fold and 10-fold cross validation when both syntactic features and 600 most frequent unigrams are used in combination.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"53 1","pages":"37-51"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75998567","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

Primal-dual algorithms for the Capacitated Single Allocation p-Hub Location Problem 有能力单分配p-Hub定位问题的原始对偶算法

International journal of hybrid intelligent systems Pub Date : 2022-04-21 DOI: 10.3233/his-220003

Telmo Matos

{"title":"Primal-dual algorithms for the Capacitated Single Allocation p-Hub Location Problem","authors":"Telmo Matos","doi":"10.3233/his-220003","DOIUrl":"https://doi.org/10.3233/his-220003","url":null,"abstract":"The Hub Location Problems (HLP) have gathered great interest due to the complexity and to the many applications in industry such as aviation, public transportation, telecommunications, among others. The HLP have many variants regarding allocation (single or multiple) and capacity (uncapacitated or capacitated). This paper presents a variant of the HLP, encompassing single allocation with capacity constraints. The Capacitated Single Allocation p-Hub Location Problem (CSApHLP) objective consists on determine the set of p hubs in a network that minimizes the total cost of allocating all the non-hub nodes to the p hubs. In this work, it is proposed a sophisticated RAMP approach (PD-RAMP) to improve the results obtained previously by the simple version (Dual-RAMP). Thus, a parallel implementation is conducted to assess the effectiveness of a parallel RAMP model applied to the CSApHLP. The first algorithm, the sequential PD-RAMP, incorporates Dual-RAMP with a Scatter Search procedure to create a Primal-Dual RAMP approach. The second algorithm, the parallel PD-RAMP, also take advantage of the dual and primal, parallelizing the primal side of the problem and interconnecting both sides as it is expected in the RAMP sequential algorithm. The quality of the results carried out on a standard testbed shows that the PD-RAMP approach managed to improve the state-of-the-art algorithms for the CSApHLP.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"122 1","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87322565","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

Analysis and performance optimization of LoRa network using the CE & SC hybrid approach 基于CE & SC混合方法的LoRa网络分析与性能优化

International journal of hybrid intelligent systems Pub Date : 2022-04-21 DOI: 10.3233/his-220007

Abdellah Amzil, Abdessamad Bellouch, Ahmed Boujnoui, Mohamed Hanini, Abdellah Zaaloul

引用次数: 0

CrossViT Wide Residual Squeeze-and-Excitation Network for Alzheimer's disease classification with self attention ProGAN data augmentation 基于自关注ProGAN数据增强的跨svit宽剩余挤压和激励网络用于阿尔茨海默病分类

International journal of hybrid intelligent systems Pub Date : 2022-04-11 DOI: 10.3233/his-220002

Rahma Kadri, Bassem Bouaziz, M. Tmar, F. Gargouri

{"title":"CrossViT Wide Residual Squeeze-and-Excitation Network for Alzheimer's disease classification with self attention ProGAN data augmentation","authors":"Rahma Kadri, Bassem Bouaziz, M. Tmar, F. Gargouri","doi":"10.3233/his-220002","DOIUrl":"https://doi.org/10.3233/his-220002","url":null,"abstract":"Efficient and accurate early prediction of Alzheimer's disease (AD) based on the neuroimaging data has attracted interest from many researchers to prevent its progression. Deep learning networks have demonstrated an optimal ability to analyse large-scale multimodal neuroimaging for AD classification. The most widely used architecture of deep learning is the Convolution neural networks (CNN) that have shown great potential in AD detection. However CNN does not capture long range dependencies within the input image and does not ensure a good global feature extraction. Furthermore, increasing the receptive field of CNN by increasing the kernels sizes can cause a feature granularity loss. Another limitation is that CNN lacks a weighing mechanism of image features; the network doesn’t focus on the relevant features within the image. Recently,vision transformer have shown an outstanding performance over the CNN and overcomes its main limitations. The vision transformer relies on the self-attention layers. The main drawbacks of this new technique is that it requires a huge amount of training data. In this paper, we combined the main strengths of these two architectures for AD classification. We proposed a new method based on the combination of the Cross ViT and Wide Residual Squeeze-and-Excitation Network. We acquired MRI data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Open Access Series of Imaging Studies (OASIS). We also proposed a new data augmentation based on the self attention progressive generative adversarial neural network to overcome the limitation of the data. Our proposed method achieved 99% classification accuracy and outperforms CNN models.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"24 1","pages":"163-177"},"PeriodicalIF":0.0,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84410445","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

Feature matching for 3D AR: Review from handcrafted methods to deep learning 3D AR的特征匹配:从手工方法到深度学习的回顾

International journal of hybrid intelligent systems Pub Date : 2022-04-11 DOI: 10.3233/his-220001

Houssam Halmaoui, A. Haqiq

引用次数: 0

Comparison of optimization algorithms based on swarm intelligence applied to convolutional neural networks for face recognition 基于群体智能的优化算法在卷积神经网络人脸识别中的应用比较

International journal of hybrid intelligent systems Pub Date : 2022-01-01 DOI: 10.3233/HIS-220010

P. Melin, D. Sánchez, O. Castillo

引用次数: 1

Towards data warehouse from open data: Case of COVID-19 从开放数据走向数据仓库:以COVID-19为例

International journal of hybrid intelligent systems Pub Date : 2021-11-26 DOI: 10.3233/his-210010

Senda Bouaziz, Ahlem Nabli, F. Gargouri

引用次数: 0

Comparing different metrics on an anisotropic depth completion model 比较各向异性深度完井模型的不同指标

International journal of hybrid intelligent systems Pub Date : 2021-06-25 DOI: 10.3233/his-210006

V. Lazcano, F. Calderero, C. Ballester

{"title":"Comparing different metrics on an anisotropic depth completion model","authors":"V. Lazcano, F. Calderero, C. Ballester","doi":"10.3233/his-210006","DOIUrl":"https://doi.org/10.3233/his-210006","url":null,"abstract":"This paper discussed an anisotropic interpolation model that filling in-depth data in a largely empty region of a depth map. We consider an image with an anisotropic metric gi⁢j that incorporates spatial and photometric data. We propose a numerical implementation of our model based on the “eikonal” operator, which compute the solution of a degenerated partial differential equation (the biased Infinity Laplacian or biased Absolutely Minimizing Lipschitz Extension). This equation’s solution creates exponential cones based on the available data, extending the available depth data and completing the depth map image. Because of this, this operator is better suited to interpolating smooth surfaces. To perform this task, we assume we have at our disposal a reference color image and a depth map. We carried out an experimental comparison of the AMLE and bAMLE using various metrics with square root, absolute value, and quadratic terms. In these experiments, considered color spaces were sRGB, XYZ, CIE-L*⁢a*⁢b*, and CMY. In this document, we also presented a proposal to extend the AMLE and bAMLE to the time domain. Finally, in the parameter estimation of the model, we compared EHO and PSO. The combination of sRGB and square root metric produces the best results, demonstrating that our bAMLE model outperforms the AMLE model and other contemporary models in the KITTI depth completion suite dataset. This type of model, such as AMLE and bAMLE, is simple to implement and represents a low-cost implementation option for similar applications.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"27 1","pages":"87-99"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76612895","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

An enhanced auto adaptive vector evaluated-based metaheuristic for solving real-world problems 一种用于解决现实问题的增强自适应向量评估元启发式算法

International journal of hybrid intelligent systems Pub Date : 2021-06-25 DOI: 10.3233/his-210007

L. F. Costa, O. Cortes, João Pedro Augusto Costa

{"title":"An enhanced auto adaptive vector evaluated-based metaheuristic for solving real-world problems","authors":"L. F. Costa, O. Cortes, João Pedro Augusto Costa","doi":"10.3233/his-210007","DOIUrl":"https://doi.org/10.3233/his-210007","url":null,"abstract":"This article investigates the enhancement of a vector evaluat-ed-based adaptive metaheuristics for solving two multiobjective problems called environmental-economic dispatch and portfolio optimization. The idea is to evolve two populations independently, and exchange information between them, i.e., the first population evolves according to the best individual of the second population and vice-versa. The choice of which algorithm will be executed on each generation is carried out stochastically among three evolutionary algorithms well-known in the literature: PSO, DE, ABC. To assess the results, we used an established metric in multiobjective evolutionary algorithms called hypervolume. Tests solving the referred problem have shown that the new approach reaches the best hypervolumes in power systems comprised of six and forty generators and five different datasets of portfolio optimization. The experiments were performed 31 times, using 250, 500, and 1000 iterations in both problems. Results have also shown that our proposal tends to overcome a variation of a hybrid SPEA2 compared to their cooperative and competitive approaches.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"9 1","pages":"101-112"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81084229","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