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| 1 | Endosonographic features predictive of malignancy in mediastinal lymph nodes in patients with lung cancer显示文摘 | Kanwar R. Gill Marwan S. Ghabril Laith H. Jamil Muhammad K. Hasan Rebecca B. McNeil Timothy A. Woodward Massimo Raimondo Brenda J. Hoffman Robert H. Hawes Joseph Romagnuolo Michael B. Wallace | 2010 | Gastrointestinal Endoscopy2010，，2： | 1 |
| 2 | Solar-assisted post-combustion carbon capture feasibility 显示文摘 | MARWAN M MUHAMMAD T A RAJAB K | 2012 | Applied Energy2012，92，： | 1 |
| 3 | Comparison of Probe-Based Confocal Laser Endomicroscopy With Virtual Chromoendoscopy for Classification of Colon Polyps显示文摘 | Anna M. Buchner Muhammad W. Shahid Michael G. Heckman Murli Krishna Marwan Ghabril Muhammad Hasan Julia E. Crook Victoria Gomez Massimo Raimondo Timothy Woodward Herbert C. Wolfsen Michael B. Wallace | 2010 | Gastroenterology2010，，3： | 1 |
| 4 | High-Definition Colonoscopy Detects Colorectal Polyps at a Higher Rate Than Standard White-Light Colonoscopy显示文摘 | Anna M. Buchner Muhammad W. Shahid Michael G. Heckman Rebecca B. McNeil Patrick Cleveland Kanwar R. Gill Anthony Schore Marwan Ghabril Massimo Raimondo Seth A. Gross Michael B. Wallace | 2010 | Clinical Gastroenterology and Hepatology2010，，4： | 1 |
| 5 | Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model显示文摘Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning repository.The research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for CKD.Furthermore,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance problems.To ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning models.The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for detectingCKD.This in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD. | Nazik Alturki Abdulaziz Altamimi Muhammad Umer Oumaima Saidani Amal Alshardan Shtwai Alsubai Marwan Omar Imran Ashraf | 2024 | Computer Modeling in Engineering & Sciences2024，139，6： | 0 |
| 6 | Novel mutations in PDE6A and CDHR1 cause retinitis pigmentosa in Pakistani families显示文摘AIM:To investigate the genetic basis of autosomal recessive retinitis pigmentosa(arRP)in two consanguineous/endogamous Pakistani families.METHODS:Whole exome sequencing(WES)was performed on genomic DNA samples of patients with arRP to identify disease causing mutations.Sanger sequencing was performed to confirm familial segregation of identified mutations,and potential pathogenicity was determined by predictions of the mutations’functions.RESULTS:A novel homozygous frameshift mutation[NM_000440.2:c.1054delG,p.(Gln352Argfs*4);Chr5:g.149286886del(GRCh37)]in the PDE6A gene in an endogamous family and a novel homozygous splice site mutation[NM_033100.3:c.1168-1G>A,Chr10:g.85968484G>A(GRCh37)]in the CDHR1 gene in a consanguineous family were identified.The PDE6A variant p.(Gln352Argfs*4)was predicted to be deleterious or pathogenic,whilst the CDHR1 variant c.1168-1G>A was predicted to result in potential alteration of splicing.CONCLUSION:This study expands the spectrum of genetic variants for arRP in Pakistani families. | Muhammad Dawood Siying Lin Taj Ud Din Irfan Ullah Shah Niamat Khan Abid Jan Muhammad Marwan Komal Sultan Maha Nowshid Raheel Tahir Asif Naveed Ahmed Muhammad Yasin Emma LBaple Andrew HCrosby Shamim Saleha | 2021 | International Journal of Ophthalmology(English edition)2021，14，12： | 0 |
| 7 | Feasibility-Guided Constraint-Handling Techniques for Engineering Optimization Problems显示文摘The particle swarm optimization(PSO)algorithm is an established nature-inspired population-based meta-heuristic that replicates the synchronizing movements of birds and sh.PSO is essentially an unconstrained algorithm and requires constraint handling techniques(CHTs)to solve constrained optimization problems(COPs).For this purpose,we integrate two CHTs,the superiority of feasibility(SF)and the violation constraint-handling(VCH),with a PSO.These CHTs distinguish feasible solutions from infeasible ones.Moreover,in SF,the selection of infeasible solutions is based on their degree of constraint violations,whereas in VCH,the number of constraint violations by an infeasible solution is of more importance.Therefore,a PSO is adapted for constrained optimization,yielding two constrained variants,denoted SF-PSO and VCH-PSO.Both SF-PSO and VCH-PSO are evaluated with respect to ve engineering problems:the Himmelblau’s nonlinear optimization,the welded beam design,the spring design,the pressure vessel design,and the three-bar truss design.The simulation results show that both algorithms are consistent in terms of their solutions to these problems,including their different available versions.Comparison of the SF-PSO and the VCHPSO with other existing algorithms on the tested problems shows that the proposed algorithms have lower computational cost in terms of the number of function evaluations used.We also report our disagreement with some unjust comparisons made by other researchers regarding the tested problems and their different variants. | Muhammad Asif Jan Yasir Mahmood Hidayat Ullah Khan Wali Khan Mashwani Muhammad Irfan Uddin Marwan Mahmoud Rashida Adeeb Khanum Ikramullah Noor Mast | 2021 | Computers, Materials & Continua2021，，6： | 0 |
| 8 | An Abstractive Summarization Technique with Variable Length Keywords as per Document Diversity显示文摘Text Summarization is an essential area in text mining,which has procedures for text extraction.In natural language processing,text summarization maps the documents to a representative set of descriptive words.Therefore,the objective of text extraction is to attain reduced expressive contents from the text documents.Text summarization has two main areas such as abstractive,and extractive summarization.Extractive text summarization has further two approaches,in which the first approach applies the sentence score algorithm,and the second approach follows the word embedding principles.All such text extractions have limitations in providing the basic theme of the underlying documents.In this paper,we have employed text summarization by TF-IDF with PageRank keywords,sentence score algorithm,and Word2Vec word embedding.The study compared these forms of the text summarizations with the actual text,by calculating cosine similarities.Furthermore,TF-IDF based PageRank keywords are extracted from the other two extractive summarizations.An intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is performed.This technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each document.This form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized text.It also solves the issue of deciding the number of representative keywords for a specific text document.To evaluate the technique,the study used a sample of more than eighteen hundred text documents.The abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text summarization.The proposed technique provides a stable measure of similarity as compared to existing forms of text summarization. | Muhammad Yahya Saeed Muhammad Awais Muhammad Younas Muhammad Arif Shah Atif Khan M.Irfan Uddin Marwan Mahmoud | 2021 | Computers, Materials & Continua2021，，3： | 0 |
| 9 | Risk and predictors of severity and mortality in patients with type 2 diabetes and COVID-19 in Dubai显示文摘BACKGROUND Globally,patients with diabetes suffer from increased disease severity and mortality due to coronavirus disease 2019(COVID-19).Old age,high body mass index(BMI),comorbidities,and complications of diabetes are recognized as major risk factors for infection severity and mortality.AIM To investigate the risk and predictors of higher severity and mortality among inhospital patients with COVID-19 and type 2 diabetes(T2D)during the first wave of the pandemic in Dubai(March–September 2020).METHODS In this cross-sectional nested case-control study,a total of 1083 patients with COVID-19 were recruited.This study included 890 men and 193 women.Of these,427 had T2D and 656 were non-diabetic.The clinical,radiographic,and laboratory data of the patients with and without T2D were compared.Independent predictors of mortality in COVID-19 non-survivors were identified in patients with and without T2D.RESULTS T2D patients with COVID-19 were older and had higher BMI than those without T2D.They had higher rates of comorbidities such as hypertension,ischemic heart disease,heart failure,and more life-threatening complications.All laboratory parameters of disease severity were significantly higher than in those without T2D.Therefore,these patients had a longer hospital stay and a significantly higher mortality rate.They died from COVID-19 at a rate three times higher than patients without.Most laboratory and radiographic severity indices in non-survivors were high in patients with and without T2D.In the univariate analysis of the predictors of mortality among all COVID-19 non-survivors,significant associations were identified with old age,increased white blood cell count,lymphopenia,and elevated serum troponin levels.In multivariate analysis,only lymphopenia was identified as an independent predictor of mortality among T2D non-survivors.CONCLUSION Patients with COVID-19 and T2D were older with higher BMI,more comorbidities,higher disease severity indices,more severe proinflammatory state with cardiac involvement,and died from COVID-19 at three times the rate of patients without T2D.The identified mortality predictors will help healthcare workers prioritize the management of patients with COVID-19. | Fatheya Alawadi Alaaeldin Bashier Azza Abdulaziz Bin Hussain Nada Al-Hashmi Fawzi Al Tayb Bachet Mohamed Mahmoud Aly Hassanein Marwan Abdelrahim Zidan Rania Soued Amar Hassan Khamis Debasmita Mukhopadhyay Fatima Abdul Aya Osama Fatima Sulaiman Muhammad Hamed Farooqi Riad Abdel Latif Bayoumi | 2023 | World Journal of Diabetes2023，14，8： | 0 |
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