http://dspace.ewubd.edu:8080/handle/123456789/3303 Sat, 04 Apr 2026 09:07:17 GMT 2026-04-04T09:07:17Z http://dspace.ewubd.edu:8080/handle/123456789/3554 Study on IEEE 802.11ad 60 GHz mmWave Channel Access Scheme and Its Implementation in ns-3 Al-Imran, Md In this paper, realizing the significance of mmWave communication, we perform the research on 5G Wireless Local Area Network (WLAN). Specifically, WLAN in 5G is supposed to be according to the IEEE 802.11ad amendment. Focusing on 60 GHz mmWave communication, there is MAC modified based on the ecosystem of actual IEEE 802.11 standard. In IEEE 802.11ad, communication get interrupted because of various type obstruction. Therefore, there is need for channel in between source and destination which uses techniques to access the medium. So, this study performs the simulation analysis of the channel access scheme performance evaluation in ns-3. This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Information and Communication Engineering of East West University, Dhaka, Bangladesh Tue, 17 Dec 2019 00:00:00 GMT http://dspace.ewubd.edu:8080/handle/123456789/3554 2019-12-17T00:00:00Z http://dspace.ewubd.edu:8080/handle/123456789/3553 An Incremental Approach of Classifying Age Group Using Text Analysis on Blog Data Helalee, Md. Manir Haider; Rahman, Obayedur; Hasan, Md. Tanver Since the dawn of civilization, people are using the writing method to express their thoughts and views. To expose one's feelings it is the best way till now. Social network and many different blogs have a large amount of data, but people don’t provide their personal data such as age and other demographics. Age groups classification from text analysis has become a leading context for scientific and commercial market research in the field of machine learning. Currently, it’s a more prominent research field of English language processing system as there is few researches works regarding text analysis for this language. There are still failures to identify perfect age group because they do not consider the most important parameter which can influence the overall result. The main objective of this research is to develop systems that which word are more frequent in a particular age group. Different machine learning algorithm is used for the classification of the teenager and adult group. Almost 100k sentence was performed to determine which parameter is relevant. Logistic Regression with TF-IDF had the best performance reaching a precision .80 in the validation test. To make the mechanism more efficient and accurate unigram method has been implemented. Several techniques have been integrated for data collection and data processing to make the system more reliable and flexible. Adequate instances and experiments are also provided to describe the methodology for both approaches. This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Information and Communication Engineering of East West University, Dhaka, Bangladesh Tue, 17 Dec 2019 00:00:00 GMT http://dspace.ewubd.edu:8080/handle/123456789/3553 2019-12-17T00:00:00Z http://dspace.ewubd.edu:8080/handle/123456789/3551 Web and Software Security Talaha, Ashikin; Chowdhury, Ayan About 200 million websites are active at present. Billions of people use web applications for transferring information, money and communicating with each other. Web applications are made by humans. So, there may exist many kinds of vulnerabilities. The main reason for the weakness is the lack of choosing the proper programming languages. There are a lot of web application attacks that are existing now such as SQL injection, Buffer overflow, security misconfiguration, cross-site scripting, etc. So, the security issues of web applications are a great concern in presents. Developers are very interested to know about any kind of attack. In this project, we have created a tool to find different types of web application vulnerabilities of particular websites. This ‘D-tect’ tool will check eight dangerous and critical web application attacks. They are WordPress username enumerator, sensitive file detector, sub-domain scanner, port scanner, WordPress scanner, cross-site scripting (XSS), WordPress backup grabber, SQL injection. The tool will show host address, IP address, header information, the vulnerable scopes and server of the web application. There will be also detection of WordPress as it is mentioned that some vulnerabilities may arise due to using WordPress. The tool will check 1904 ports to find out the vulnerable ports. Sub-domains may have vulnerable DNS resolver that may help the attacker to exploit a system. That will be also scanned by the tool. The WordPress backup system will be also analyzed to find whether it is vulnerable or not. So, the tool will check for particular ports and try to inject different types of attacks. Then the corresponding result will be visible. This tool is created by using python and different modules and functions of python. There are different types of modules and functions are used to create the tool. the program can be run till the user wants to stop scanning. This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Information and Communication Engineering of East West University, Dhaka, Bangladesh Mon, 26 Aug 2019 00:00:00 GMT http://dspace.ewubd.edu:8080/handle/123456789/3551 2019-08-26T00:00:00Z http://dspace.ewubd.edu:8080/handle/123456789/3386 Liver Segmentation Using Deep Learning Mondol, Tonmoy; Nahar, Syeda Nurun; Chakroborty, Ankhi Throughout the most recent couple of years, major breakthroughs were achieved in many computer visions tasks, such as image classification and segmentation by using the application of deep learning. The programmed liver division from Computed Tomography (CT) pictures has become a significant territory in clinical research, including radiotherapy, liver volume estimation, and liver transplant medical procedures. This research introduces a framework for the automated segmentation of liver and lesions in images of the CT and Magnetic Resonance Images (MRI) abdomen using Cascaded Fully Convolutional Neural (CFCN) networks for the segmentation of large-scale medical trials and quantitative image analysis. We train and cascade two Fully Convolutional Neural (FCN) networks for the combination of liver segmentation and lesions. We train an FCN as a first step to segment the liver as an input of Region of Interest (ROI) for a second FCN. The second FCN segments only lesions inside phase one's estimated liver ROIs. CFCN models have been trained on a 100-volume abdominal CT dataset. Validation tests on additional data sets show that linguistic liver and lesion segmentation based on CFCN achieves Dice scores above 94% for the liver with computation times below 100s per volume. On 38 MRI liver tumor volumes and the public 3DIRCAD dataset, we further experimentally demonstrate the robustness of the proposed method. This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Information and Communication Engineering of East West University, Dhaka, Bangladesh Thu, 26 Dec 2019 00:00:00 GMT http://dspace.ewubd.edu:8080/handle/123456789/3386 2019-12-26T00:00:00Z