Publications by authors named "Shamsul Masum"
Forest ecosystems face increasing wildfire threats, demanding prompt and precise detection methods to ensure efficient fire control. However, real-time forest fire data accessibility and timeliness require improvement. Our study addresses the challenge through the introduction of the Unmanned Aerial Vehicles (UAVs) based forest fire database (UAVs-FFDB), characterized by a dual composition.
View Article and Find Full Text PDFObjective: To compare long-term outcomes between laparoscopic and robotic total mesorectal excisions (TMEs) for rectal cancer in a tertiary center.
Background: Laparoscopic rectal cancer surgery has comparable long-term outcomes to the open approach, with several advantages in short-term outcomes. However, it has significant technical limitations, which the robotic approach aims to overcome.
COVID-19, caused by SARS-CoV-2, has been declared as a global pandemic by WHO. Early diagnosis of COVID-19 patients may reduce the impact of coronavirus using modern computational methods like deep learning. Various deep learning models based on CT and chest X-ray images are studied and compared in this study as an alternative solution to reverse transcription-polymerase chain reactions.
View Article and Find Full Text PDFArticle Synopsis
- * Significant predictors for patient outcomes included factors like age, surgical approach, and complications, with various AI models achieving accuracies over 80% in predicting length of stay, readmission, and mortality.
- * Different models, including support vector regression and BI-LSTM, effectively predicted outcomes like length of stay and readmission, with high accuracy—showing the potential of combining multiple variables for improved predictions in patient care.