2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)最新文献_第4页

Image Colorization Using a Deep Transfer Learning 使用深度迁移学习的图像着色

2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238737

Leila Kiani, Masoudnia Saeed, H. Nezamabadi-pour

{"title":"Image Colorization Using a Deep Transfer Learning","authors":"Leila Kiani, Masoudnia Saeed, H. Nezamabadi-pour","doi":"10.1109/CFIS49607.2020.9238737","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238737","url":null,"abstract":"Over the past decade, the automatic image coloring has been of particular interest in applications such as repairing damaged or old images. One of the problems with the auto-coloring is the ability to predict multiple color results for gray image pixels. In the presence of noise, the problem becomes more complicated. Recently, some researchers employ conventional neural networks (CNN) to the problem of image colorization. Usually, the output of the last layer of CNN is used as a feature representation. However, the information contained in this layer may be too coarse spatially to allow exact localization. Conversely, earlier layers may be precise in localization but will not capture semantics. In this article, we use a concept called hypercolumns to achieve the best in both cases and develop a fully automatic image coloring system. Our approach exploits recent advances in deep neural networks and uses the semantic representation to provide an accurate color prediction. Since many elements of the scene naturally appear by the color distribution, we train our model in such a way to predict the color texture of each pixel. The DIV2K dataset has been used for training, and the obtained results are compared with other methods based on PSNR, which are promising.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117265574","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

Classification of Pulmonary Images By Using Generative Adversarial Networks 基于生成对抗网络的肺部图像分类

2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238755

Yasamin Kowsari, Seyed Javad Mahdavi Chabok, M. Moattar

{"title":"Classification of Pulmonary Images By Using Generative Adversarial Networks","authors":"Yasamin Kowsari, Seyed Javad Mahdavi Chabok, M. Moattar","doi":"10.1109/CFIS49607.2020.9238755","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238755","url":null,"abstract":"Using deep learning networks has made developments in the computer vision field. Due to the growth in interstitial lung diseases (ILDs), the necessity of using modern computer-aided diagnosis (CAD) is increased. Regarding the importance of this issue, many kinds of research have been done on this subject. However, the extreme similarity between lung nodules and the complexity of detecting nodules characteristics is a severe challenge to design a system that can classify lung diseases with high accuracy. This study is done in 3 steps. First, the Convolutional Neural Network (CNN) is used due to the effectiveness and higher accuracy it can provide in comparison to the previous methods. This proposed network consists of four convolutional layers with 2×2 kernels and LeakyReLU, four average pooling layers, and three fully-connected layers. The last layer has five outputs equivalent to the considered classes: healthy, ground-glass opacity (GGO), micromodels, consolidation, reticulation. The main challenge in the field of medical images is the lack of labeled samples. So in the second step, Generative Adversarial Network (GAN) is used to generate data and increase the accuracy of the convolutional network structure by creating non-realistic but useful data in network learning. GAN structure is based on two neural networks. The generator, generates new data instances, while the discriminator, evaluates them for authenticity. The CNN which is used in the structure of discriminator can carefully detect the similarity of produced data and lung nodule classes. To train the CNN, in the third step, Interstitial Lung Diseases (ILDs) dataset (containing 3527 images) and 3200 GAN produced images is used. In the test phase for making an evaluation, real images that are extracted from the dataset are used. The accuracy of categorizing five types of lung nodules in the designed system is 88 %, which is 5 percent more than previous studies.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114354013","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

A Review of Algorithms, Datasets, and Criteria in Word Sense Disambiguation With a View to its Use in Islamic Texts 对伊斯兰文本中词义消歧的算法、数据集和标准的回顾

2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238679

Yasamin Vasheghani Farahani, Behrooz Janfada, B. M. Bidgoli

引用次数: 0

Triangular fuzzy bilevel linear programming problem 三角模糊双层线性规划问题

2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238756

Niloofar Davoudi, Farhad Hamidi, H. M. Nehi

引用次数: 0

Attention-based Convolutional Neural Network for Answer Selection using BERT 基于BERT的基于注意力的卷积神经网络答案选择

2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238669

H. Khorashadizadeh, R. Monsefi, Shima Foolad

{"title":"Attention-based Convolutional Neural Network for Answer Selection using BERT","authors":"H. Khorashadizadeh, R. Monsefi, Shima Foolad","doi":"10.1109/CFIS49607.2020.9238669","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238669","url":null,"abstract":"Question answering is at the heart of natural language processing and is composed of two sections: Reading Comprehension and Answer Selection. Prior to deep learning, all natural language processing solutions including Question Answering were based on statistical methods and researchers generated set of features based on text input. Answer Selection is a fundamental task in Question Answering, also a tough one because of the complicated semantic relations between questions and answers. Attention is a mechanism that has revolutionized deep learning community. Leveraging pretrained language models have made a breakthrough in most natural language processing tasks. Bert is one of the top pretrained deep language models that has achieved state-of-the-art on an extensive area of nlp tasks. In this paper we utilize an attention-based convolutional neural network. First, we employ BERT, a state-of-the-art pre-trained contextual as the embedding layer. Second, we enhance the model by adding some more attentive features. We evaluate the performance of our model on WikiQA dataset. Our experiments show that our model is superior to many other answer-selection models.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114665038","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: 2

An under-sampling technique for imbalanced data classification based on DBSCAN algorithm 基于DBSCAN算法的欠采样不平衡数据分类技术

2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238718

Behzad Mirzaei, Bahareh Nikpour, H. Nezamabadi-pour

{"title":"An under-sampling technique for imbalanced data classification based on DBSCAN algorithm","authors":"Behzad Mirzaei, Bahareh Nikpour, H. Nezamabadi-pour","doi":"10.1109/CFIS49607.2020.9238718","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238718","url":null,"abstract":"In the classification problem, the classification accuracy will be influenced by the training data significantly. However, data sets distribution in real-world applications, is mostly imbalanced. Imbalanced data sets mean that most of the samples are in one class named the majority class, whereas the other class named the minority class has little samples. In these situations, most of the classifiers confront the problem, because they designed to classify samples that are distributed between classes equally. Therefore, selecting a suitable training set is an essential step in the domain of imbalanced data classification. In this paper, a novel and effective under-sampling technique is presented to select the suitable samples of majority class using the well-known DBSCAN algorithm. According to this algorithm, the most appropriate samples from the majority class are selected, and other majority class samples will be removed to balance the training set. Experimental results over fifteen imbalanced data sets demonstrate the supremacy of the proposed method compared with six other preprocessing methods.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"441 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129411604","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

Monocular Reconstruction of Conformal Surfaces through Optimization 基于优化的保形面单目重建

2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238703

Seyed Ali Hosseini Shamoushaki, Mohammad Talebi, Amineh Mazandarani, S. Hosseini

引用次数: 0

A Computer-Aided Design Approach for Improving the Performance of Double-Tail Comparators 提高双尾比较器性能的计算机辅助设计方法

2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238712

Sadegh Mohammadi-Esfahrood, Mostafa Najafzadeh Ashrafi, S. Zahiri

引用次数: 0

AFDM : an Analytical Framework of Dialogue Manager AFDM:对话管理的分析框架

2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238732

M. Keyvanpour, Mehrnoush Barani Shirzad, Haniyeh Rashidghalam

引用次数: 0

True-False Sets 对错题集

2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238701

R. Borzooei, M. M. Takallo, Y. Jun

引用次数: 1