2021 7th International Conference on Web Research (ICWR)最新文献_第4页

Persian Opinion Mining:A Networked Analysis Approach 波斯意见挖掘:一种网络分析方法

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443158

Mohammad Heydari, B. Teimourpour

引用次数: 0

Improving Recommender Systems Performances Using User Dimension Expansion by Movies’ Genres and Voting-Based Ensemble Machine Learning Technique 基于电影类型和基于投票的集成机器学习技术的用户维度扩展改进推荐系统的性能

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443146

Arash Oshnoudi, Behzad Soleimani Neysiani, Zahra Aminoroaya, N. Nematbakhsh

{"title":"Improving Recommender Systems Performances Using User Dimension Expansion by Movies’ Genres and Voting-Based Ensemble Machine Learning Technique","authors":"Arash Oshnoudi, Behzad Soleimani Neysiani, Zahra Aminoroaya, N. Nematbakhsh","doi":"10.1109/ICWR51868.2021.9443146","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443146","url":null,"abstract":"The recommender system's performance needs to be improved more than ever by increasing computer systems' usage in various applications. Recommender systems are a valuable tool in e-commerce websites. Their primary purpose is to generate accurate forecasts to access information in less time and energy for end-users. Classification optimizes information retrieval activity in these systems and reduces user search time. Besides, clustering tries to insert the new object in the best similar class, like using the k-nearest neighbor algorithm as a classifier. The proposing approach focuses on modeling categories by averaging rates of movie genres.Moreover, the user clustering will be improved by voting machine learning classifiers on multilayer perceptron (MLP) neural networks and k-nearest neighbors (kNN) algorithms. The experiments performed on the MovieLens dataset show that the proposed method is more successful than other previous methods in predicting user clusters with 93.81% accuracy, 94.45% precision, and 92.81% recall. Also, Davies Bouldin metrics indicates better clustering result using dimension expansion of movies' genres.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126400987","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

Automatic Web-Based Software Structural Testing Using an Adaptive Particle Swarm Optimization Algorithm for Test Data Generation 基于自适应粒子群算法的基于web的软件结构自动测试

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443153

A. Damia, M. Esnaashari, Mohammadreza Parvizimosaed

{"title":"Automatic Web-Based Software Structural Testing Using an Adaptive Particle Swarm Optimization Algorithm for Test Data Generation","authors":"A. Damia, M. Esnaashari, Mohammadreza Parvizimosaed","doi":"10.1109/ICWR51868.2021.9443153","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443153","url":null,"abstract":"The purpose of a software test is to search for a set of test data in a search space to satisfy a specific coverage criterion. Therefore, finding an effective way to automatically generate this data is an important issue in software testing. This is especially crucial for web-based software, where the size of the program is large, and automatic test-case generation is of prominence. In this paper, a novel method of particle swarm optimization algorithm (PSO) for automatic generation of test data is presented, for web-based software. PSO algorithm has several weaknesses. In this algorithm, there is a possibility of particles to be trapped in local optima. Although PSO is quite rapid compared to other evolutionary algorithms, it usually cannot improve the quality of the solution achieved by increasing iterations. One reason is that in this algorithm, particles converge to a specific point between the best general position and the best personal position. Due to this weakness, a change in PSO has been given in this paper. This is an inertial weight change. In general, in this paper, the inertia weight is dynamically calculated in each round of the algorithm according to the fitness of each particle . Experiments have been performed on different programs and the results of experiments have shown that the proposed method (AIWPSO) has better convergence rate than several methods performed by other variants of the PSO.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130716576","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

KnowSum: Knowledge Inclusive Approach for Text Summarization Using Semantic Allignment 使用语义对齐的文本摘要的知识包容方法

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443149

K. N, G. Deepak

{"title":"KnowSum: Knowledge Inclusive Approach for Text Summarization Using Semantic Allignment","authors":"K. N, G. Deepak","doi":"10.1109/ICWR51868.2021.9443149","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443149","url":null,"abstract":"Text summarization plays an important role in delivering compact, most relevant, and efficient text to the user. It is also applied on the field of community question answers. There is a large amount of data on the internet pertaining to each topic. The question needs to be analyzed properly so that optimized, most relevant, and summarized text answer is generated. This paper proposes an ontology-based text summarization technique using Semantic Alignment and information gain along with LSTM and flower pollination algorithm. Here MS Marco Data set is used. From this for classifying question and answers LSTM is used. The top half of the data is only taken. With respect to each domain term from domain ontology feature extraction is done using information scent. Community question answer data such as Yahoo answers and Quora dataset are taken and classified. Both of these are then mapped together based on semantic alignment using flower pollination algorithm. After mapping, the answers are prioritized based on semantic similarity and information gain. Top 5 answers are chosen and summarized. The architecture’s performance is calculated and compared with the baseline approaches and it is clearly observed that the proposed ontology-based text summarization technique is predominant in terms of performance and attained a precision and accuracy of 99.94% and 96.54 % respectively.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132243332","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

COPER: a Query-Adaptable Semantics-based Search Engine for Persian COVID-19 Articles COPER:基于查询适应性语义的波斯语COVID-19文章搜索引擎

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443151

Reza Khanmohammadi, Mitra Sadat Mirshafiee Khoozani, M. Allahyari

引用次数: 1

Sentiment Analysis of Persian Instagram Post: a Multimodal Deep Learning Approach 波斯语Instagram帖子的情感分析:一种多模式深度学习方法

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443026

Aria Naseri Karimvand, R. Chegeni, Mohammad Ehsan Basiri, Shahla Nemati

{"title":"Sentiment Analysis of Persian Instagram Post: a Multimodal Deep Learning Approach","authors":"Aria Naseri Karimvand, R. Chegeni, Mohammad Ehsan Basiri, Shahla Nemati","doi":"10.1109/ICWR51868.2021.9443026","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443026","url":null,"abstract":"Instagram is a popular social media that has a wide range of active users from ordinary people to artists and official users. Instagram posts are widely used by users to share text, image or video. Many users use text to describe or complement the images they share. To analyze the sentiment of such posts, both the content of the text and the image should be considered at the same time. This requires modelling of the relationship between the text and image modalities. To address this problem, we propose a multimodal deep learning method. The proposed method utilizes a bi-directional gated recurrent unit (bi-GRU) for processing text comments and a 2-dimensional convolutional neural network (2CNN) for analyzing images. In order to assess the performance of the proposed model, we introduce a new dataset of Instagram posts, MPerInst, containing 512 pairs of images and their corresponding comments written in the Persian language. Implementation results shows that employing both text and image modalities improves polarity detection accuracy and F1-scrore by 23% and 0.24 compared to using only image and text modalities, respectively. Moreover, the proposed model outperforms 11 similar deep fusion models by 11% and 0.1 in terms of accuracy and F1-score. Both the dataset and the codes of our proposed model are publicly available for probable future use.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130664576","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

The Impact of Active Learning Algorithm on a Cross-lingual model in a Persian Sentiment Task 主动学习算法对波斯语情感任务跨语言模型的影响

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443156

Monire Shirghasemi, M. Bokaei, M. Bijankhan

{"title":"The Impact of Active Learning Algorithm on a Cross-lingual model in a Persian Sentiment Task","authors":"Monire Shirghasemi, M. Bokaei, M. Bijankhan","doi":"10.1109/ICWR51868.2021.9443156","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443156","url":null,"abstract":"One of the most challenging problems that we may face in natural language processing tasks is the lack of annotated training datasets. In this paper our goal is to consider the impact of Active Learning algorithm on a cross-lingual model in sentiment analysis task on Persian language which is known as a low-resource language. Cross-lingual model trains a model by using a rich-resource language like English as a source language and apply it to a low-resource language, in this way the dependency to training datasets is decreased. Also using Active Learning strategy helps us to improve the functionality of our model by selecting most representative samples. Since labeling data is expensive and time consuming, by selecting the machine desirable data we can reduce the amount of labeled data required for our tasks. To do this we can select data which classifier is the least confident about them. When they are chosen, a user is asked to labeled them. There are lots of methods and factors to choose the appropriate data for Active Learning strategy. In the end these methods help our classifier to gain more knowledge about samples and work more properly.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127042366","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 Analysis of Iranian University Library Websites from Standpoint Five Effective Factors on Google SEO : Iranian University Library Websites and Google SEO 从谷歌搜索引擎优化的五个影响因素:伊朗大学图书馆网站与谷歌搜索引擎优化

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443112

Maryam Tavosi, N. Naghshineh

{"title":"An Analysis of Iranian University Library Websites from Standpoint Five Effective Factors on Google SEO : Iranian University Library Websites and Google SEO","authors":"Maryam Tavosi, N. Naghshineh","doi":"10.1109/ICWR51868.2021.9443112","DOIUrl":"https://doi.org/10.1109/ICWR51868.2021.9443112","url":null,"abstract":"The main purpose of this study was, analysis of library websites of Iranian governmental universities affiliated with the ministry of science from Standpoint, five effective factors on Google Search Engine Optimization (SEO). First, the five factors that impact Google SEO, by the Google Help Center and some previously published articles, Identified. Then 42 library websites of governmental universities affiliated with the ministry of science exception for Payam Noor and Farhangian located in Tehran and other metropolitan cities of Iran with descriptive survey methods were comparison analyses. Data collection tools were analytical databases W3C, Similarweb, Ahrefs. The data analysis tool was \"LibreOfficeCalc\" software. All stages of the evaluation of library websites from standpoints, SEO factors, approved by eight Google SEO experts. In the four factors impacting Google SEO, the top 11 websites of the research community (42 websites) were identified. Also, Just, 33% of the research community, used \"Digital Security Certificate\" or HTTPS Protocol for their university library websites (fifth effective factor). It’s worthy, the university library managers in Iran, have high pay attention to improve Google SEO and effective factors on this. Previous studies have noted the role of search engine optimization in libraries or academic libraries, but accurate measurement of five effective factors on Google SEO, in Iranian university library websites, is the innovation of this study.","PeriodicalId":377597,"journal":{"name":"2021 7th International Conference on Web Research (ICWR)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132431955","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 Controller Placement for Software Defined Wide Area Networks 软件定义广域网的优化控制器布局

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443024

Esmaeil Amiri, Emad Alizadeh, Mohammad Hossein Rezvani

引用次数: 4

ParSQuAD: Machine Translated SQuAD dataset for Persian Question Answering ParSQuAD:波斯语问答的机器翻译SQuAD数据集

2021 7th International Conference on Web Research (ICWR) Pub Date : 2021-05-19 DOI: 10.1109/ICWR51868.2021.9443126

Negin Abadani, Jamshid Mozafari, A. Fatemi, Mohammd Ali Nematbakhsh, A. Kazemi

引用次数: 12