2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)最新文献_第8页

Adverse Drug Reaction Detection Using Data Mining Techniques: A Review Article 使用数据挖掘技术检测药物不良反应:综述文章

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303709

Behnaz Pourebrahim, M. Keyvanpour

{"title":"Adverse Drug Reaction Detection Using Data Mining Techniques: A Review Article","authors":"Behnaz Pourebrahim, M. Keyvanpour","doi":"10.1109/ICCKE50421.2020.9303709","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303709","url":null,"abstract":"Adverse drug reactions (ADRs) are side effects that occur when taking the drug in natural doses. ADRs are a public health issue because they hospitalize millions of patients worldwide each year. Early detection of ADRs reduces economic costs and prevents fatality.Diagnosis of ADRs usually depended on voluntary reporting or medical information. But in recent years, the data sent by the user on social media has become a significant source for detecting ADR. Twitter is a social media where people use short messages as a way to communicate. Limit the number of words on Twitter allows users to use words purposefully and focused. The information provided by users about drugs and their adverse reactions on Twitter is an important resource for post-marketing drug monitoring.In recent years, machine learning and data mining methods have been considered in the field of data science for ADR detection. Important challenges in this area are divided into three parts: data pre-processing, extracting meaningful features, and selecting the best model for classification.The aim of this study is to study, review and challenge the methods of ADR diagnosis by data mining on social media, especially Twitter.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121688251","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 Hybrid Algorithm for Evaluating Trust in Online Social Networks 在线社交网络中信任评估的混合算法

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303641

Nina Fatehi, H. Shahhoseini

引用次数: 1

Forecasting stock market trends using support vector regression and perceptually important points 使用支持向量回归和感知重要点预测股票市场趋势

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303667

Mojtaba Azimifar, Babak Nadjar Araabi, Hadi Moradi

引用次数: 1

Evaluating the Effect of Compression Settings in the Classification of Image File Formats 评价压缩设置对图像文件格式分类的影响

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303655

Z. Seyedghorban, M. Teimouri

引用次数: 0

Secure Multi-party Sorting Protocol Based on Distributed Oblivious Transfer 基于分布式无关传输的安全多方排序协议

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303630

Motahareh Dehghan, B. Sadeghiyan

引用次数: 0

Fusion Beacon and Machine Vision Based on Extended Kalman Filter for Indoor Localization 基于扩展卡尔曼滤波的融合信标和机器视觉室内定位

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303644

Nafise Dehghan Salmasi, R. Azmi

引用次数: 1

A convolutional neural network and stacked autoencoders approach for motor imagery based brain-computer interface 基于卷积神经网络和堆叠自编码器的运动图像脑机接口

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303717

Roya Arabshahi, M. Rouhani

引用次数: 1

Persian Text Based Traffic sign Detection with Convolutional Neural Network: A New Dataset 基于波斯语文本的卷积神经网络交通标志检测:一个新的数据集

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303646

Saba Kheirinejad, Noushin Riaihi, R. Azmi

{"title":"Persian Text Based Traffic sign Detection with Convolutional Neural Network: A New Dataset","authors":"Saba Kheirinejad, Noushin Riaihi, R. Azmi","doi":"10.1109/ICCKE50421.2020.9303646","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303646","url":null,"abstract":"Recently, traffic panel detection has attracted both academic and industrial attention. However, there are a few works that studied text based traffic panels. This is because there are many challenges in this kind of traffic panels. To obtain an appropriate accuracy in text recognition in the text based traffic panels, we need to detect the panel. Since there is no public text based traffic panels dataset, we collected a new dataset included the Persian text based traffic panels in the streets of Tehran-Iran for the first time. Our dataset contains two sets of figures. The first set has 9294 pictures and the second set has 3305 pictures. The second dataset is more uniform than the first dataset. Therefore, we exploit the first set as an additional dataset and use the second one as the main dataset. Accordingly, we pretrain the network by the additional dataset and train it by the main dataset. We use the tiny YOLOv3 (You Only Look Once version three) algorithm to pretrain, train, and test the dataset. The algorithm is fast and has low complexity. We use K-fold cross validation method to appraise efficiency of the algorithm. From the results section we could see that Precision is 0.973, Recall is 0.945, and Fmeasure is 0.955.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129492913","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

An Improved Feature Selection Method for Sentiments Analysis in Social Networks 一种改进的社交网络情感分析特征选择方法

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303710

F. Akbarian, F. Z. Boroujeni

{"title":"An Improved Feature Selection Method for Sentiments Analysis in Social Networks","authors":"F. Akbarian, F. Z. Boroujeni","doi":"10.1109/ICCKE50421.2020.9303710","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303710","url":null,"abstract":"The increasing growth of user oriented media and users’ preferences in using social networks for communication, causes these virtual communities to become a valuable source of data. These communities provide users with the possibility of being aware of useful and reliable opinions. Many organizations employ efficient classifiers for determining the polarity of users’ opinions in order to make valid decisions in different business domains. However, most of the existing approaches suffer from low accuracy results due to performing classification task in a high-dimensional feature space. To this end, an efficient feature selection method based on a modified version of firefly algorithm is presented in this article. The main contribution of the proposed method is employing a weighted combination of classification performance measures in constructing a fitness function for the firefly algorithm. The proposed model for the fitness function leads to establishing a trade-off between the performance measures while trying to reduce the number of dimensions. The results obtained from experiments conducted on 11000 tweets show that the proposed method outperforms the existing counterparts in terms of polarity classification performance.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130262627","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

Intelligent Architecture for Car-following Behaviour Observing Lane-changer: Modeling and Control 汽车跟随行为观察的智能体系结构:建模与控制

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303652

Farzam Tajdari, Naeim Ebrahimi Toulkani, Maral Nourimand

{"title":"Intelligent Architecture for Car-following Behaviour Observing Lane-changer: Modeling and Control","authors":"Farzam Tajdari, Naeim Ebrahimi Toulkani, Maral Nourimand","doi":"10.1109/ICCKE50421.2020.9303652","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303652","url":null,"abstract":"During motorway driving behaviour, the car-following behaviour is the most popular among the rest of behaviours e.g., lane-changing and overtaking. However, a few research has been done on the effect of lane change behaviour on car-following behaviour. The effect is a highly complex transient state among the car-following models and makes the car follower exit the previous ones, which are known as conventional models, for a limited time. Accordingly, in this paper, an intelligent model includes anticipation of interaction behaviour regarding the micro-structure of drivers is proposed, when Lane Changer (LC) exits the lane is studied. Continuously, a fuzzy controller is designed based on the criteria of detecting the complex behaviour in the model. Both the model and the controller aim to regulate the Follower Vehicle (FV) acceleration which simulates the behaviour of a real driver. Afterward, its performance is compared with the database of human drivers. The results assert that the model is capable to estimate the behaviour of the real drivers perfectly. Also, the controller provides a safer and smoother drive comparing to a real driver, in addition to less traveling time.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128379406","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: 10