Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

S He, LG Leanse, Y Feng - Advanced Drug Delivery Reviews, 2021 - Elsevier
In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms
that resist conventional antibiotic treatment has steadily increased. Thus, it is now …

A review of the machine learning algorithms for COVID-19 case analysis

S Tiwari, P Chanak, SK Singh - IEEE Transactions on Artificial …, 2022 - ieeexplore.ieee.org
The purpose of this article is to see how machine learning (ML) algorithms and applications
are used in the COVID-19 inquiry and for other purposes. The available traditional methods …

Hybrid model for precise hepatitis-C classification using improved random forest and SVM method

UK Lilhore, P Manoharan, JK Sandhu, S Simaiya… - Scientific Reports, 2023 - nature.com
Abstract Hepatitis C Virus (HCV) is a viral infection that causes liver inflammation. Annually,
approximately 3.4 million cases of HCV are reported worldwide. A diagnosis of HCV in …

Hepatitis C Virus prediction based on machine learning framework: a real-world case study in Egypt

H Mamdouh Farghaly, MY Shams… - … and Information Systems, 2023 - Springer
Prediction and classification of diseases are essential in medical science, as it attempts to
immune the spread of the disease and discover the infected regions from the early stages …

Intelligent system for COVID-19 prognosis: A state-of-the-art survey

J Nayak, B Naik, P Dinesh, K Vakula, BK Rao… - Applied …, 2021 - Springer
This 21st century is notable for experiencing so many disturbances at economic, social,
cultural, and political levels in the entire world. The outbreak of novel corona virus 2019 …

[HTML][HTML] Optimizing HCV disease prediction in Egypt: The hyOPTGB framework

AM Elshewey, MY Shams, SM Tawfeek, AH Alharbi… - Diagnostics, 2023 - mdpi.com
The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of the
highest rates of HCV in the world. The high prevalence is linked to several factors, including …

DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins

AA Jamali, R Ferdousi, S Razzaghi, J Li, R Safdari… - Drug discovery today, 2016 - Elsevier
Highlights•Develo** a novel machine learning tool for prediction of potential druggable
proteins.•Remarkable high performance of employed models in prediction.•Introducing new …

Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques

K Karami, M Akbari, MT Moradi, B Soleymani… - PloS one, 2021 - journals.plos.org
This paper identifies prognosis factors for survival in patients with acute myeloid leukemia
(AML) using machine learning techniques. We have integrated machine learning with …

Hierarchical pattern recognition in milking parameters predicts mastitis prevalence

E Ebrahimie, F Ebrahimi, M Ebrahimi… - … and electronics in …, 2018 - Elsevier
The aim of this study was to develop a predictive model for mastitis incidence, independent
from Somatic Cell Count (SCC), to provide an alternative, simple, and cost-effective …