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您的检索式：作者名="Sultan Musleh"
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| 1 | Current global research landscape on COVID-19 and cancer: Bibliometric and visualization analysis显示文摘BACKGROUND Cancer is a severe public health issue that seriously jeopardizes global health.In individuals with coronavirus disease 2019(COVID-19),cancer is considered an independent risk factor for severe illness and increased mortality.AIM To identify research hotspots and prospects,we used bibliometrics to examine the global production of COVID-19 literature published in the field of oncology.METHODS Data on publication output were identified based on the Scopus database between January 1,2020,and June 21,2022.This study used VOSviewer to analyze collaboration networks among countries and assess the terms most often used in the titles and abstracts of retrieved publications to determine research hotspots linked to cancer and COVID-19.The Impact Index Per Article for the top 10 high-cited papers collected from Reference Citation Analysis(RCA)are presented.RESULTS A total of 7015 publications were retrieved from the database.The United States published the greatest number of articles(2025;28.87%),followed by Italy(964;13.74%),the United Kingdom(839;11.96%),and China(538;7.67%).The University of Texas MD Anderson Cancer Center(n=205,2.92%)ranked first,followed by the Memorial Sloan-Kettering Cancer Center(n=176,2.51%).The European Journal of Cancer(n=106,1.51%)ranked first,followed by the Frontiers in Oncology(n=104,1.48%),Cancers(n=102,1.45%),and Pediatric Blood and Cancer(n=95;1.35%).The hot topics were stratified into“cancer care management during the COVID-19 pandemic”;and“COVID-19 vaccines in cancer patients”.CONCLUSION This is the first bibliometric analysis to determine the present state and upcoming hot themes related to cancer and COVID-19 and vice versa using VOSviewer during the early stages of the pandemic.The emergence of hot themes related to cancer and COVID-19 may aid researchers in identifying new research areas in this field. | Sa'ed H Zyoud Amer Koni Samah W Al-Jabi Riad Amer Muna Shakhshir Rand Al Subu Husam Salameh Razan Odeh Sultan Musleh Faris Abushamma Adham Abu Taha | 2022 | World Journal of Clinical Oncology2022，13，10： | 0 |
| 2 | Supervised Machine Learning-Based Prediction of COVID-19显示文摘COVID-19 turned out to be an infectious and life-threatening viral disease,and its swift and overwhelming spread has become one of the greatest challenges for the world.As yet,no satisfactory vaccine or medication has been developed that could guarantee its mitigation,though several efforts and trials are underway.Countries around the globe are striving to overcome the COVID-19 spread and while they are finding out ways for early detection and timely treatment.In this regard,healthcare experts,researchers and scientists have delved into the investigation of existing as well as new technologies.The situation demands development of a clinical decision support system to equip the medical staff ways to timely detect this disease.The state-of-the-art research in Artificial intelligence(AI),Machine learning(ML)and cloud computing have encouraged healthcare experts to find effective detection schemes.This study aims to provide a comprehensive review of the role of AI&ML in investigating prediction techniques for the COVID-19.A mathematical model has been formulated to analyze and detect its potential threat.The proposed model is a cloud-based smart detection algorithm using support vector machine(CSDC-SVM)with cross-fold validation testing.The experimental results have achieved an accuracy of 98.4%with 15-fold cross-validation strategy.The comparison with similar state-of-the-art methods reveals that the proposed CSDC-SVM model possesses better accuracy and efficiency. | Atta-ur-Rahman Kiran Sultan Iftikhar Naseer Rizwan Majeed Dhiaa Musleh Mohammed Abdul Salam Gollapalli Sghaier Chabani Nehad Ibrahim Shahan Yamin Siddiqui Muhammad Adnan Khan | 2021 | Computers, Materials & Continua2021，，10： | 0 |
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