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| 1 | Interaction of the hepatitis C virus (HCV) core with cellular genes in the development of HCV-induced steatosis显示文摘 | Mahwish Khan Shah Jahan Saba Khaliq Bushra Ijaz Waqar Ahmad Baila Samreen Sajida Hassan | 2010 | Archives of Virology2010，，11： | 1 |
| 2 | 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 |
| 3 | Interaction of the hepatitis C virus (HCV) core with cellular genes in the development of HCV-induced steatosis显示文摘 | Mahwish Khan Shah Jahan Saba Khaliq Bushra Ijaz Waqar Ahmad Baila Samreen Sajida Hassan | 2010 | Archives of Virology2010，，11： | 1 |
| 4 | Hepatitis C virus entry: Role of host and viral factors显示文摘 | Baila Samreen Saba Khaliq Usman Ali Ashfaq Mahwish Khan Nadeem Afzal Muhammad Aiman Shahzad Sabeen Riaz Shah Jahan | 2012 | Infection Genetics and Evolution2012，，8： | 1 |
| 5 | Interaction of the hepatitis C virus (HCV) core with cellular genes in the development of HCV-induced steatosis显示文摘 | Mahwish Khan Shah Jahan Saba Khaliq Bushra Ijaz Waqar Ahmad Baila Samreen Sajida Hassan | 2010 | Archives of Virology2010，，11： | 1 |
| 6 | Homotopy perturbation aided optimization procedure with applications to oscillatory fractional order nonlinear dynamical systems显示文摘This paper presents an approximate solution of nonlinear fractional differential equations(FDEs)that exhibit an oscillatory behavior by using a metaheuristic technique.The solutions of the governing equations are approximated by using homotopy perturbation method(HPM)along with the fractional derivative in the Caputo sense.The designed methodology is based on a weighted series of HPM in conjunction with a nature-inspired algorithm.The idea is instantly fascinated by the researchers on the consequent implementation of nature-inspired learning algorithms such as a Cuckoo search algorithm(CSA).The usage of CSA has accelerated the minimized search path of error to the convergent values of the solution.The validity and accuracy of the proposed technique are ascertained by calculating the approximate solution and the error norms which ensure the convergence of the approximation that can be further increased.The critical analysis is also provided by the numerical simulation of two different test models.Discussion of key points has been determined by the tabulation of numerical values and graphs.Comparative study of the results with known numerical technique is also performed. | Najeeb Alam Khan Tooba Hameed Samreen Ahmed | 2019 | International Journal of Modeling, Simulation, and Scientific Computing2019，10，4： | 0 |
| 7 | Rapid metabolic fingerprinting with the aid of chemometric models to identify authenticity of natural medicines: Turmeric, Ocimum, and Withania somnifera study显示文摘Herbal medicines are popular natural medicines that have been used for decades.The use of alternative medicines continues to expand rapidly across the world.The World Health Organization suggests that quality assessment of natural medicines is essential for any therapeutic or health care applications,as their therapeutic potential varies between different geographic origins,plant species,and varieties.Classification of herbal medicines based on a limited number of secondary metabolites is not an ideal approach.Their quality should be considered based on a complete metabolic profile,as their pharmacological activity is not due to a few specific secondary metabolites but rather a larger group of bioactive compounds.A holistic and integrative approach using rapid and nondestructive analytical strategies for the screening of herbal medicines is required for robust characterization.In this study,a rapid and effective quality assessment system for geographical traceability,species,and variety-specific authenticity of the widely used natural medicines turmeric,Ocimum,and Withania somnifera was investigated using Fourier transform near-infrared(FT-NIR)spectroscopy-based metabolic fingerprinting.Four different geographical origins of turmeric,five different Ocimum species,and three different varieties of roots and leaves of Withania somnifera were studied with the aid of machine learning approaches.Extremely good discrimination(R^(2)>0.98,Q^(2)>0.97,and accuracy=1.0)with sensitivity and specificity of 100%was achieved using this metabolic fingerprinting strategy.Our study demonstrated that FT-NIR-based rapid metabolic fingerprinting can be used as a robust analytical method to authenticate several important medicinal herbs. | Samreen Khan Abhishek Kumar Rai Anjali Singh Saudan Singh Basant Kumar Dubey Raj Kishori Lal Arvind Singh Negi Nicholas Birse Prabodh Kumar Trivedi Christopher T.Elliott Ratnasekhar Ch | 2023 | Journal of Pharmaceutical Analysis2023，13，9： | 0 |
| 8 | 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 |
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