检索结果-南通市图书馆

Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment Analysis of Twitter Data

Computers, Materials & Continua 2021年第6期67卷 3451-3461页

作者： samreen naeem Wali Khan Mashwani Aqib Ali M.Irfan Uddin Marwan Mahmoud Farrukh Jamal Christophe Chesneau Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Faculty of Applied Studies King Abdulaziz UniversityJeddah21577Saudi Arabia Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Mathematics Universitéde CaenLMNOCampus IIScience 3Caen14032France

This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter(called tweets).A dataset of the exchange rates between the United States Dollar(USD)and the Pakistani Rupee(PKR)was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related *** dataset was collected in raw form,and was subjected to natural language processing by way of data *** variable labeling was then applied to the standardized dataset,where the response variables were divided into two classes:“1”indicated an increase in the exchange rate and“−1”indicated a decrease in *** better represent the dataset,we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector *** that were obtained using a sampling approach were then used for data *** machine learning classifiers—the simple logistic classifier,the random forest,bagging,naïve Bayes,and the support vector machine—were applied to the optimized *** results show that the simple logistic classifier yielded the highest accuracy of 82.14%for the USD and the PKR exchange rates forecasting.

关键词： Machine learning exchange rate sentiment analysis linear discriminant analysis principal component analysis simple logistic

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

引用

Computers, Materials & Continua 2021年第7期68卷 391-407页

作者： Aqib Ali Wali Khan Mashwani samreen naeem Muhammad Irfan Uddin Wiyada Kumam Poom Kumam Hussam Alrabaiah Farrukh Jamal Christophe Chesneau Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Program in Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiThanyaburi12110Thailand Departments of Mathematics Faculty of ScienceCenter of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research LaboratoryRoom SCL 802 Fixed Point LaboratoryScience Laboratory BuildingKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand Department of Medical Research China Medical University HospitalTaichung40402Taiwan College of Engineering Al Ain UniversityAl Ain64141United Arab Emirates Department of Mathematics Tafila Technical UniversityTafila66110Jordan Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan 11Department of MathematicsUniversitéde CaenLMNOCaen14032France

The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COVID-19 by using machine learning *** dataset contains data from patients who are prone to the *** contains three types of lungs CT images(Normal,Pneumonia,and COVID-19)collected from two different sources;the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur,Pakistan,and the second one is a publicly free available medical imaging database known as *** the preprocessing,a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest(ROIs)and acquire 52 hybrid statistical features for each ***,12 optimized statistical features are selected via the chi-square feature reduction *** the classification,five machine learning classifiers named as deep learning J4,multilayer perceptron,support vector machine,random forest,and naive Bayes are deployed to optimize the hybrid statistical features *** is observed that the deep learning J4 has promising results(sensitivity and specificity:0.987;accuracy:98.67%)among all the deployed *** a complementary study,a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.

关键词： COVID-19 machine learning fuzzy c-mean deep learning J4

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Assessment of biosafety implementation in clinical diagnostic laboratories in Pakistan during the COVID-19 pandemic

引用

Journal of Biosafety and Biosecurity 2022年第1期4卷 43-49页

作者： samreen Sarwar Faheem Shahzad Ayesha Vajeeha Rimsha Munir Amina Yaqoob Aniqa naeem Mamoona Sattar Sheereen Gull Health Security Partners LahorePunjabPakistan Department of Immunology University of Health Sciences LahorePunjabPakistan Centre for Applied Molecular Biology University of the PunjabLahorePunjabPakistan Cancer Biology Lab Institute of Microbiology and Molecular GeneticsUniversity of the Punjaband Molecular GeneticistHormone LabLahorePunjabPakistan Center of Excellence in Molecular Biology University of the PunjabLahorePakistan Food and Meal Science Kristianstad UniversityKristianstadSweden Microbiological Engineering and Industrial Biotechnology Group College of ChemistryChemical Engineering and BiotechnologyDonghua UniversityShanghaiChina School of Biological Sciences University of the PunjabLahorePakistan

Laboratory diagnostic capacity is crucial for an optimal national response to a public health emergency such as the COVID-19 *** laboratory-acquired infections and the loss of critical human resources,especially during a public health emergency,requires laboratories to have a good biorisk management system in *** this study,we aimed to evaluate laboratory biosafety and biosecurity in Pakistan during the COVID-19 *** this cross-sectional study,a self-rated anonymous questionnaire was distributed to laboratory professionals(LPs)working in clinical diagnostic laboratories,including laboratories performing polymerase chain reaction(PCR)-based COVID-19 diagnostic testing in Punjab,Sindh,Khyber Pakhtunkhwa,and Gilgit-Baltistan provinces as well as Islamabad during March 2020 to April *** questionnaire assessed knowledge and perceptions of LPs,resource availability,and commitment by top management in these *** total,58.6%of LPs performing COVID-19 testing reported that their laboratory did not conduct a biorisk assessment before starting COVID-19 testing in their *** 31%of LPs were aware that COVID-19 testing could be performed at a biosafety level 2 laboratory,as per the World Health Organization interim biosafety guidelines.A sufficiently high percentage of LPs did not feel confident in their ability to handle COVID-19 samples(32.8%),spills(43.1%),or other accidents(32.8%).These findings demonstrate the need for effective biosafety program implementation,proper training,and establishing competency assessment *** findings also suggested that identifying and addressing gaps in existing biorisk management systems through sustainable interventions and preparing LPs for surge capacity is crucial to better address public health emergencies.

关键词： Biosafety Laboratory professionals COVID-19 pandemic

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：