2020 International Conference on Advanced Science and Engineering (ICOASE)最新文献_第2页

Fake News Detection Using Machine Learning and Deep Learning Algorithms 使用机器学习和深度学习算法检测假新闻

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436605

Awf Abdulrahman, M. Baykara

引用次数: 16

Quality Assessment of Reinforcing Steel Used in Residential Building in Duhok City at Kurdistan - Iraq 伊拉克库尔德斯坦杜胡克市住宅建筑用钢筋质量评价

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436571

Hawar Hasan Jasim, Mudhafer H. Selman, S. Mohammed

引用次数: 0

Effective Computational Techniques for Generating Electroencephalogram Data 生成脑电图数据的有效计算技术

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436591

Mahmoud Elsayed, K. Sim, Shing Chiang Tan

引用次数: 1

A systematic review of GIS-based landslide Hazard Mapping on Determinant Factors from International Databases 基于gis的国际数据库决定性因素滑坡危险性制图系统综述

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436611

Sevar Neamat, H. Karimi

{"title":"A systematic review of GIS-based landslide Hazard Mapping on Determinant Factors from International Databases","authors":"Sevar Neamat, H. Karimi","doi":"10.1109/ICOASE51841.2020.9436611","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436611","url":null,"abstract":"The landslide could severely affect infrastructure, irrigation systems, soil fertility, river and streams, and settlements. Both human-made and natural phenomena contribute to landslide hazards, and therefore a comprehensive assessment of landslide susceptibility is essential to its mitigation actions. Nowadays, geographical information systems and remote sensing in combination with modeling techniques have widely been used for assessment and mapping the susceptibility of the landslide. This study contains a review of 20 scientific articles on the spatial-statistical analysis of landslide susceptibility in the last ten years. The papers were reviewed for the locations of the case study, effective parameters used, and the results' validation. The review indicates the case studies were mostly for Asian countries, and this point was obtained that some vulnerable regions had sufficiently been studied. Various causative factors were used for spatial analysis and modeling of landslide susceptibility assessment where slope, lithology, curvature, and distance to rivers were used in most studies. This review concludes that for spatial dimension evaluation of the landslide, it is important to study a comprehensive set of both natural and anthropogenic factors. This review also provides useful information that can help future studies and serve as a resource for understanding the techniques used to manage this important natural hazard.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133493242","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

Queuing Theory Model of Expected Waiting Time for Fast Diagnosis nCovid-19: A Case Study 新型冠状病毒快速诊断预期等待时间排队理论模型研究

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436601

Naji A. Majedkan, B. A. Idrees, Omar M. Ahmed, Lailan M. Haji, Hivi I. Dino

{"title":"Queuing Theory Model of Expected Waiting Time for Fast Diagnosis nCovid-19: A Case Study","authors":"Naji A. Majedkan, B. A. Idrees, Omar M. Ahmed, Lailan M. Haji, Hivi I. Dino","doi":"10.1109/ICOASE51841.2020.9436601","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436601","url":null,"abstract":"This Queuing theory analysis has been used in hospitals and other healthcare settings; its use in this sector is not widespread. Consequently, the queue waiting line is an effective scientific tool in the performance of waiting lines analysis. The main parameters used to measure the performance of the systems are the length of the queue line, utilization of the server, and the delays for arrivals. This study aims to avoid others to expose from epidemics such as (nCovid-19), because of becoming a big global problem today in the world. Also, to know the length for the expected and actual waiting times to diagnosis the arrivals to Duhok city. Collection and execution are done within one week with one activity healthcare team's (HCT) in the main entry Duhok city, port. Data was collected utilize individual conceptions and records from Saturday through to Thursday, which they are the most critical time setting. The main interest in this study was calculating the average waiting time spent after the process to improve arrivals satisfaction using the queuing theory model. The results of this analysis indicate for each citizen checked by the healthcare team may be waiting 32.44 minutes in a queue. Also, was estimate to provide the results of system capabilities with characterization related to the expected waiting time. Finally, Medical staff at the city outlets can estimate; how many arrivals will be waiting in the line and the number of clients that will walk away each day.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129320190","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

Hate Speech Detection Using Genetic Programming 基于遗传编程的仇恨语音检测

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436621

Mona Khalifa A. Aljero, Nazife Dimililer

{"title":"Hate Speech Detection Using Genetic Programming","authors":"Mona Khalifa A. Aljero, Nazife Dimililer","doi":"10.1109/ICOASE51841.2020.9436621","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436621","url":null,"abstract":"There has been a steep increase in the use of social media in our everyday lives in recent years. Along with this, there has been an increase in hate speech disseminated on these platforms, due to the anonymity of the users as well as the ease of use. Social media platforms need to filter and prevent the spread of hate speech to protect their users and society. Due to the high traffic, automatic detection of hate speech is necessary. Hate speech detection is one of the most difficult classification challenges in text mining. Research in this domain focuses on the use of supervised machine learning approaches, such as support vector machine, logistic regression, convolutional neural network, and random forest. Ensemble techniques have also been employed. However, the performance of these approaches has not yet reached an acceptable level. In this paper, we propose the use of the Genetic Programming (GP) approach for binary classification of hate speech on social media platforms. Each individual in the GP framework represents a classifier that is evolved to optimize Fl-score. Experimental results show the effectiveness of our GP approach; the proposed approach outperforms the state-of-the-art using the same dataset HatEval.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116162815","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

Neural Networks Architectures Design, and Applications: A Review 神经网络架构、设计与应用综述

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436582

M. A. Sadeeq, A. Abdulazeez

引用次数: 24

Glove Word Embedding and DBSCAN algorithms for Semantic Document Clustering 语义文档聚类的手套词嵌入和DBSCAN算法

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436540

Shapol M. Mohammed, Karwan Jacksi, Subhi R. M. Zeebaree

引用次数: 14

Enhance the Performance of Independent Component Analysis for Text Classification by Using Particle Swarm Optimization 利用粒子群算法提高文本分类中独立成分分析的性能

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436547

H. Shabat, N. Abbas

{"title":"Enhance the Performance of Independent Component Analysis for Text Classification by Using Particle Swarm Optimization","authors":"H. Shabat, N. Abbas","doi":"10.1109/ICOASE51841.2020.9436547","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436547","url":null,"abstract":"Independent component analysis is a statistical model that is used to separate a multivariate signal into additive components. Independent component analysis has gained much attention in recent years in the neural networks and signals processing fields. Several data mining applications with Independent component analysis have been considered, such as latent variable decomposition, analysis of text document data, detection of hidden signals in satellite imagery, and weather data mining. The conventional Independent component analysis search scheme is based on a gradient algorithm, which requires a predefined learning rate. Therefore, it cannot solve the convergence dilemma. To overwhelm the disadvantage, particle swarm optimization is employed in the ICA algorithm. In statistics, negentropy is used as a measure of distance to normality. The present study used a metaheuristic, particle swarm optimization algorithm that employs negentropy as a fitness function to enhance the performance of independent component analysis for the text classification model as one of the text mining applications. The proposed system was applied to a medical corpus, and two experiments were executed. Results show that the performance of the PSO-ICA algorithm is superior to the FastICA for text classification, where it achieves an overall F -measure of 89% for text classification compared with the FastICA algorithm, which provides 85% of an overall F -measure for text classification.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124447095","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

A Comparative Study Using LZW with Wavelet or DCT for Compressing Color Images LZW与小波和DCT在彩色图像压缩中的比较研究

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436622

Z. Ahmed, Loay E. George

引用次数: 4