M
Muhammad Nazir
Researcher at University of Dammam
Publications - 106
Citations - 2038
Muhammad Nazir is an academic researcher from University of Dammam. The author has contributed to research in topics: Medicine & Support vector machine. The author has an hindex of 16, co-authored 95 publications receiving 1194 citations. Previous affiliations of Muhammad Nazir include Pir Mehr Ali Shah Arid Agriculture University & National University of Computer and Emerging Sciences.
Papers
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Journal Article
Prevalence of periodontal disease, its association with systemic diseases and prevention.
TL;DR: It is hoped that medical, dental practitioners, and other health-care professionals will get familiar with perio-systemic link and risk factors, and need to refer to the specialized dental or periodontal care.
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Global Prevalence of Periodontal Disease and Lack of Its Surveillance.
Muhammad Nazir,Asim Al-Ansari,Khalifa S. Al-Khalifa,Muhanad Alhareky,Balgis Gaffar,Khalid Almas +5 more
TL;DR: It is found that the distribution of periodontal disease increases with age, and periodontitis was the most common in older persons and in population from high-income countries.
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The mesioangular third molar--to extract or not to extract? Analysis of 776 consecutive third molars.
TL;DR: Distal caries in lower second molars related to a mesioangular third molar is a common finding in oral and maxillofacial patients in secondary care, especially if the third molars is fully or partially erupted.
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The burden of Diabetes, Its Oral Complications and Their Prevention and Management.
TL;DR: Dental professionals should be part of the multidisciplinary team that helps individuals with diabetes and improve glycemic control among diabetic patients, as well as prevent oral complications of DM.
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Lungs nodule detection framework from computed tomography images using support vector machine.
Sajid Ali Khan,Sajid Ali Khan,Muhammad Nazir,Muhammad Attique Khan,Tanzila Saba,Kashif Javed,Amjad Rehman,Tallha Akram,Muhammad Awais +8 more
TL;DR: A novel classification framework for lungs nodule classification with less false positive rates (FPRs), high accuracy, sensitivity rate, less computationally expensive and uses a small set of features while preserving edge and texture information is proposed.