Samina Amin¹, Muhammad Ali Zeb¹, Hani Alshahrani^2,*, Mohammed Hamdi², Mohammad Alsulami², Asadullah Shaikh³

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1167-1202, 2024, DOI:10.32604/cmes.2023.043921

Abstract Social media (SM) based surveillance systems, combined with machine learning (ML) and deep learning (DL) techniques, have shown potential for early detection of epidemic outbreaks. This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance. Since, every year, a large amount of data related to epidemic outbreaks, particularly Twitter data is generated by SM. This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM, along with the ML and DL techniques that have been configured for the… More >

Uzma Afzal¹, Tariq Mahmood², Ali Mustafa Qamar^3,*, Ayaz H. Khan^4,5

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6251-6267, 2023, DOI:10.32604/cmc.2023.037653

Abstract A complex Laboratory Developed Test (LDT) is a clinical test developed within a single laboratory. It is typically configured from many feature constraints from clinical repositories, which are part of the existing Laboratory Information Management System (LIMS). Although these clinical repositories are automated, support for managing patient information with test results of an LDT is also integrated within the existing LIMS. Still, the support to configure LDTs design needs to be made available even in standard LIMS packages. The manual configuration of LDTs is a complex process and can generate configuration inconsistencies because many constraints between features can remain unsatisfied.… More >

Nabila Mansouri^1,2,*, Khalid Sultan³, Aakash Ahmad⁴, Ibrahim Alseadoon⁴, Adal Alkhalil⁴

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1247-1264, 2022, DOI:10.32604/iasc.2022.025046

Abstract The outbreak of novel Coronavirus COVID-19, an infectious disease caused by the SARS-CoV-2 virus, has caused an unprecedented medical, economic, and social emergency that requires data-driven intelligence and decision support systems to counter the subsequent pandemic. Data-driven models and intelligent systems can assist medical researchers and practitioners to identify symptoms of COVID-19 infection. Several solutions based on medical image processing have been proposed for this purpose. However, the most shortcoming of hand craft image processing systems is the lower provided performances. Hence, for the first time, the proposed solution uses a deep learning model that is applied to Computed Tomography… More >