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| 1 | Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment Analysis of Twitter Data显示文摘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 words.The dataset was collected in raw form,and was subjected to natural language processing by way of data preprocessing.Response 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 it.To better represent the dataset,we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector space.Clusters that were obtained using a sampling approach were then used for data optimization.Five machine learning classifiers—the simple logistic classifier,the random forest,bagging,naïve Bayes,and the support vector machine—were applied to the optimized dataset.The results show that the simple logistic classifier yielded the highest accuracy of 82.14%for the USD and the PKR exchange rates forecasting. | Samreen Naeem Wali Khan Mashwani Aqib Ali M.Irfan Uddin Marwan Mahmoud Farrukh Jamal Christophe Chesneau | 2021 | Computers, Materials & Continua2021，，6： | 1 |
| 2 | COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach显示文摘The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COVID-19 by using machine learning methods.Our dataset contains data from patients who are prone to the epidemic.It 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 Radiopaedia.For 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 ROIs.Also,12 optimized statistical features are selected via the chi-square feature reduction technique.For 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 dataset.It is observed that the deep learning J4 has promising results(sensitivity and specificity:0.987;accuracy:98.67%)among all the deployed classifiers.As 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. | Aqib Ali Wali Khan Mashwani Samreen Naeem Muhammad Irfan Uddin Wiyada Kumam Poom Kumam Hussam Alrabaiah Farrukh Jamal Christophe Chesneau | 2021 | Computers, Materials & Continua2021，，7： | 0 |
| 3 | Assessment of biosafety implementation in clinical diagnostic laboratories in Pakistan during the COVID-19 pandemic显示文摘Laboratory diagnostic capacity is crucial for an optimal national response to a public health emergency such as the COVID-19 pandemic.Preventing 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 place.In this study,we aimed to evaluate laboratory biosafety and biosecurity in Pakistan during the COVID-19 pandemic.In 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 2020.The questionnaire assessed knowledge and perceptions of LPs,resource availability,and commitment by top management in these laboratories.In 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 facility.Only 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 methods.These 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. | Samreen Sarwar Faheem Shahzad Ayesha Vajeeha Rimsha Munir Amina Yaqoob Aniqa Naeem Mamoona Sattar Sheereen Gull | 2022 | Journal of Biosafety and Biosecurity2022，4，1： | 0 |
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