Browsing by Author "Choudhury, Pranto Protim"

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Performance analysis of machine learning classi ers for detecting PE malware
(BRAC University, 2019-12) Azmee, ABM.Adnan; Choudhury, Pranto Protim; Alam, Md.Aosaful; Dutta, Orko; Hossain, Muhammad Iqbal
In this modern era of technology, securing and protecting one's data has been a major concern and needs to be focused on. Malware is a program that is designed to cause harm and malware analysis is one of the paramount focused points under the sight of cyber forensic professionals and network administrations. The degree of the harm brought about by malignant programming varies to a great extent. If this happens at home to a random person then that may lead to some loss of irrel- evant or unimportant information but for a corporate network, it can lead to loss of valuable business data. The existing research does focus on some few machine learning algorithms to detect malware and very few of them worked with Portable Executables (PE) les. However, we worked on the PE les and also for real-time computation, a client-server model was developed by using Flask to detect malware or benign. In this paper, we mainly focused on top classi cation algorithms and compare their accuracy to nd out which one is giving the best result according to the dataset and also compare among these algorithms. Top machine learning clas- si cation algorithms were used alongside neural networks such as Arti cial Neural Network, XGBoost, Support Vector Machine, Extra Tree Classi er, etc. The exper- imental result shows that XGBoost achieved the highest accuracy of 98.62 percent when compared with other approaches. Thus, to provide a better solution for this kind of anomalies, we have been interested in researching malware detection and want to contribute to building strong and protective cybersecurity.
Pneumonia Stage Analyzes Through Image Processing
(Institute of Advanced Engineering and Science (IAES), 2024-12-15) Chowdhury, Nishu; Moon, Shatabdi Roy; Choudhury, Pranto Protim
A physical examination and diagnostic imaging techniques including lung biopsies, ultrasounds, and chest X-rays are typically used to make the diagnosis of pneumonia infection, an infectious disease that has the potential to be life-threatening. The objective of this research is to categorize the stages of pneumonia through image processing methods. Before that, an ensemble model for diagnosing pneumonia infections is created utilizing the transfer learning algorithms ResNet50V2 and DenseNet201. The 5,857 images were taken from the PAUL MOONEY dataset for this research. The proposed ensemble averaging model recognizes lung infection appropriately and accurately. By applying a contour detection approach, the left and right chests are separated and the affected pixels from there to analyze the stage of pneumonia. It is very crucial to identify the stage for treatment purposes.
Pneumonia stage analyzes through image processing
(2024-01-15) Chowdhury, Nishu; Choudhury, Pranto Protim; Moon, Shatabdi Roy
physical examination and diagnostic imaging techniques including lung biopsies, ultrasounds, and chest X-rays are typically used to make the diagnosis of pneumonia infection, an infectious disease that has the potential to be life-threatening. The objective of this research is to categorize the stages of pneumonia through image processing methods. Before that, an ensemble model for diagnosing pneumonia infections is created utilizing the transfer learning algorithms ResNet50V2 and DenseNet201. The 5,857 images were taken from the PAUL MOONEY dataset for this research. The proposed ensemble averaging model recognizes lung infection appropriately and accurately. By applying a contour detection approach, the left and right chests are separated and the affected pixels from there to analyze the stage of pneumonia. It is very crucial to identify the stage for treatment purposes.