Journal of Artificial Intelligence and Data Mining最新文献_第4页

ParsNER-Social: A Corpus for Named Entity Recognition in Persian Social Media Texts ParsNER-Social:波斯语社交媒体文本中命名实体识别的语料库

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-03-08 DOI: 10.22044/JADM.2020.9949.2143

Majid Asgari-Bidhendi, Behrooz Janfada, O. Talab, B. Minaei-Bidgoli

{"title":"ParsNER-Social: A Corpus for Named Entity Recognition in Persian Social Media Texts","authors":"Majid Asgari-Bidhendi, Behrooz Janfada, O. Talab, B. Minaei-Bidgoli","doi":"10.22044/JADM.2020.9949.2143","DOIUrl":"https://doi.org/10.22044/JADM.2020.9949.2143","url":null,"abstract":"Named Entity Recognition (NER) is one of the essential prerequisites for many natural language processing tasks. All public corpora for Persian named entity recognition, such as ParsNERCorp and ArmanPersoNERCorpus, are based on the Bijankhan corpus, which is originated from the Hamshahri newspaper in 2004. Correspondingly, most of the published named entity recognition models in Persian are specially tuned for the news data and are not flexible enough to be applied in different text categories, such as social media texts. This study introduces ParsNER-Social, a corpus for training named entity recognition models in the Persian language built from social media sources. This corpus consists of 205,373 tokens and their NER tags, crawled from social media contents, including 10 Telegram channels in 10 different categories. Furthermore, three supervised methods are introduced and trained based on the ParsNER-Social corpus: Two conditional random field models as baseline models and one state-of-the-art deep learning model with six different configurations are evaluated on the proposed dataset. The experiments show that the Mono-Lingual Persian models based on Bidirectional Encoder Representations from Transformers (MLBERT) outperform the other approaches on the ParsNER-Social corpus. Among different Configurations of MLBERT models, the ParsBERT+BERT-TokenClass model obtained an F1-score of 89.65%.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46451939","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

Online Recommender System Considering Changes in User's Preference 考虑用户偏好变化的在线推荐系统

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-03-06 DOI: 10.22044/JADM.2020.9518.2085

J. Hamidzadeh, M. Moradi

引用次数: 2

Feature Selection based on Particle Swarm Optimization and Mutual Information 基于粒子群优化和互信息的特征选择

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-02-17 DOI: 10.22044/JADM.2020.8857.2020

Z. Shojaee, S. A. S. Fazeli, E. Abbasi, F. Adibnia

{"title":"Feature Selection based on Particle Swarm Optimization and Mutual Information","authors":"Z. Shojaee, S. A. S. Fazeli, E. Abbasi, F. Adibnia","doi":"10.22044/JADM.2020.8857.2020","DOIUrl":"https://doi.org/10.22044/JADM.2020.8857.2020","url":null,"abstract":"Today, feature selection, as a technique to improve the performance of the classification methods, has been widely considered by computer scientists. As the dimensions of a matrix has a huge impact on the performance of processing on it, reducing the number of features by choosing the best subset of all features, will affect the performance of the algorithms. Finding the best subset by comparing all possible subsets, even when n is small, is an intractable process, hence many researches approach to heuristic methods to find a near-optimal solutions. In this paper, we introduce a novel feature selection technique which selects the most informative features and omits the redundant or irrelevant ones. Our method is embedded in PSO (Particle Swarm Optimization). To omit the redundant or irrelevant features, it is necessary to figure out the relationship between different features. There are many correlation functions that can reveal this relationship. In our proposed method, to find this relationship, we use mutual information technique. We evaluate the performance of our method on three classification benchmarks: Glass, Vowel, and Wine. Comparing the results with four state-of-the-art methods, demonstrates its superiority over them.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43777557","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

Facial Expression Recognition based on Image Gradient and Deep Convolutional Neural Network 基于图像梯度和深度卷积神经网络的面部表情识别

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-02-17 DOI: 10.22044/JADM.2021.9898.2121

M. R. Fallahzadeh, F. Farokhi, A. Harimi, R. Sabbaghi‐Nadooshan

{"title":"Facial Expression Recognition based on Image Gradient and Deep Convolutional Neural Network","authors":"M. R. Fallahzadeh, F. Farokhi, A. Harimi, R. Sabbaghi‐Nadooshan","doi":"10.22044/JADM.2021.9898.2121","DOIUrl":"https://doi.org/10.22044/JADM.2021.9898.2121","url":null,"abstract":"Facial Expression Recognition (FER) is one of the basic ways of interacting with machines and has been getting more attention in recent years. In this paper, a novel FER system based on a deep convolutional neural network (DCNN) is presented. Motivated by the powerful ability of DCNN to learn features and image classification, the goal of this research is to design a compatible and discriminative input for pre-trained AlexNet-DCNN. The proposed method consists of 4 steps: first, extracting three channels of the image including the original gray-level image, in addition to horizontal and vertical gradients of the image similar to the red, green, and blue color channels of an RGB image as the DCNN input. Second, data augmentation including scale, rotation, width shift, height shift, zoom, horizontal flip, and vertical flip of the images are prepared in addition to the original images for training the DCNN. Then, the AlexNet-DCNN model is applied to learn high-level features corresponding to different emotion classes. Finally, transfer learning is implemented on the proposed model and the presented model is fine-tuned on target datasets. The average recognition accuracy of 92.41% and 93.66% were achieved for JAFEE and CK+ datasets, respectively. Experimental results on two benchmark emotional datasets show promising performance of the proposed model that can improve the performance of current FER systems.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44191421","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: 6

Convolutional Neural Network Equipped with Attention Mechanism and Transfer Learning for Enhancing Performance of Sentiment Analysis 采用注意机制和迁移学习的卷积神经网络提高情绪分析的性能

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-02-07 DOI: 10.22044/JADM.2021.9618.2100

H. Sadr, M. Pedram, M. Teshnehlab

{"title":"Convolutional Neural Network Equipped with Attention Mechanism and Transfer Learning for Enhancing Performance of Sentiment Analysis","authors":"H. Sadr, M. Pedram, M. Teshnehlab","doi":"10.22044/JADM.2021.9618.2100","DOIUrl":"https://doi.org/10.22044/JADM.2021.9618.2100","url":null,"abstract":"With the rapid development of textual information on the web, sentiment analysis is changing to an essential analytic tool rather than an academic endeavor and numerous studies have been carried out in recent years to address this issue. By the emergence of deep learning, deep neural networks have attracted a lot of attention and become mainstream in this field. Despite the remarkable success of deep learning models for sentiment analysis of text, they are in the early steps of development and their potential is yet to be fully explored. Convolutional neural network is one of the deep learning methods that has been surpassed for sentiment analysis but is confronted with some limitations. Firstly, convolutional neural network requires a large number of training data. Secondly, it assumes that all words in a sentence have an equal contribution to the polarity of a sentence. To fill these lacunas, a convolutional neural network equipped with the attention mechanism is proposed in this paper which not only takes advantage of the attention mechanism but also utilizes transfer learning to boost the performance of sentiment analysis. According to the empirical results, our proposed model achieved comparable or even better classification accuracy than the state-of-the-art methods.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49497230","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: 9

Automatic Persian Text Emotion Detection using Cognitive Linguistic and Deep Learning 基于认知语言学和深度学习的波斯文本情绪自动检测

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-01-18 DOI: 10.22044/JADM.2020.9992.2136

S. S. Sadeghi, Hassan Khotanlou, M. R. Mahand

{"title":"Automatic Persian Text Emotion Detection using Cognitive Linguistic and Deep Learning","authors":"S. S. Sadeghi, Hassan Khotanlou, M. R. Mahand","doi":"10.22044/JADM.2020.9992.2136","DOIUrl":"https://doi.org/10.22044/JADM.2020.9992.2136","url":null,"abstract":"In the modern age, written sources are rapidly increasing. A growing number of these data are related to the texts containing the feelings and opinions of the users. Thus, reviewing and analyzing of emotional texts have received a particular attention in recent years. A System which is based on combination of cognitive features and deep neural network, Gated Recurrent Unit has been proposed in this paper. Five basic emotions used in this approach are: anger, happiness, sadness, surprise and fear. A total of 23,000 Persian documents by the average length of 24 have been labeled for this research. Emotional constructions, emotional keywords, and emotional POS are the basic cognitive features used in this approach. On the other hand, after preprocessing the texts, words of normalized text have been embedded by Word2Vec technique. Then, a deep learning approach has been done based on this embedded data. Finally, classification algorithms such as Naive Bayes, decision tree, and support vector machines were used to classify emotions based on concatenation of defined cognitive features, and deep learning features. 10-fold cross validation has been used to evaluate the performance of the proposed system. Experimental results show the proposed system achieved the accuracy of 97%. Result of proposed system shows the improvement of several percent’s in comparison by other results achieved GRU and cognitive features in isolation. At the end, studying other statistical features and improving these cognitive features in more details can affect the results.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44019482","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: 7

Bio-inspired Computing Paradigm for Periodic Noise Reduction in Digital Images 数字图像周期性降噪的生物启发计算范式

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-01-01 DOI: 10.22044/JADM.2020.9358.2071

N. Alibabaie, A. Latif

引用次数: 0

An Image Restoration Architecture using Abstract Features and Generative Models 基于抽象特征和生成模型的图像恢复体系结构

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-01-01 DOI: 10.22044/JADM.2020.9691.2101

A. Fakhari, K. Kiani

{"title":"An Image Restoration Architecture using Abstract Features and Generative Models","authors":"A. Fakhari, K. Kiani","doi":"10.22044/JADM.2020.9691.2101","DOIUrl":"https://doi.org/10.22044/JADM.2020.9691.2101","url":null,"abstract":"Image restoration and its different variations are important topics in low-level image processing. One of the main challenges in image restoration is dependency of current methods to the corruption characteristics. In this paper, we have proposed an image restoration architecture that enables us to address different types of corruption, regardless of type, amount and location. The main intuition behind our approach is restoring original images from abstracted perceptual features. Using an encoder-decoder architecture, image restoration can be defined as an image transformation task. Abstraction of perceptual features is done in the encoder part of the model and determines the sampling point within original images' Probability Density Function (PDF). The PDF of original images is learned in the decoder section by using a Generative Adversarial Network (GAN) that receives the sampling point from the encoder part. Concretely, sampling from the learned PDF restores original image from its corrupted version. Pretrained network extracts perceptual features and Restricted Boltzmann Machine (RBM) makes the abstraction over them in the encoder section. By developing a new algorithm for training the RBM, the features of the corrupted images have been refined. In the decoder, the Generator network restores original images from abstracted perceptual features while Discriminator determines how good the restoration result is. The proposed approach has been compared with both traditional approaches like BM3D and with modern deep models like IRCNN and NCSR. We have also considered three different categories of corruption including denoising, inpainting and deblurring. Experimental results confirm performance of the model.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":"9 1","pages":"129-139"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68374957","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 Distributed Sailfish Optimizer Based on Multi-Agent Systems for Solving Non-Convex and Scalable Optimization Problems Implemented on GPU 基于多智能体系统的分布式旗鱼优化器在GPU上实现的非凸可伸缩优化问题

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-01-01 DOI: 10.22044/JADM.2020.9389.2075

S. Shadravan, H. Naji, V. Khatibi

{"title":"A Distributed Sailfish Optimizer Based on Multi-Agent Systems for Solving Non-Convex and Scalable Optimization Problems Implemented on GPU","authors":"S. Shadravan, H. Naji, V. Khatibi","doi":"10.22044/JADM.2020.9389.2075","DOIUrl":"https://doi.org/10.22044/JADM.2020.9389.2075","url":null,"abstract":"The SailFish Optimizer (SFO) is a metaheuristic algorithm inspired by a group of hunting sailfish that alternates their attacks on group of prey. The SFO algorithm takes advantage of using a simple method for providing the dynamic balance between exploration and exploitation phases, creating the swarm diversity, avoiding local optima, and guaranteeing high convergence speed. Nowadays, multi agent systems and metaheuristic algorithms can provide high performance solutions for solving combinatorial optimization problems. These methods provide a prominent approach to reduce the execution time and improve of the solution quality. In this paper, we elaborate a multi agent based and distributed method for sailfish optimizer (DSFO), which improves the execution time and speedup of the algorithm while maintaining the results of optimization in high quality. The Graphics Processing Units (GPUs) using Compute Uniﬁed Device Architecture (CUDA) are used for the massive computation requirements in this approach. In depth of the study, we present the implementation details and performance observations of DSFO algorithm. Also, a comparative study of distributed and sequential SFO is performed on a set of standard benchmark optimization functions. Moreover, the execution time of distributed SFO is compared with other parallel algorithms to show the speed of the proposed algorithm for solving unconstrained optimization problems. The final results indicate that the proposed method is executed about maximum 14 times faster than other parallel algorithms and shows the ability of DSFO for solving non-separable, non-convex and scalable optimization problems.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":"9 1","pages":"59-71"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68374898","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

GroupRank: Ranking Online Social Groups Based on User Membership Records grou恶作剧:基于用户成员记录对在线社交组进行排名

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-01-01 DOI: 10.22044/JADM.2020.8337.1973

Ali Hashemi, M. Z. Chahooki

引用次数: 0