Theses - Undergraduate
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- ItemOpen AccessDistinguish walking and running(North South University, 2021-12-31) Sangeeta Paul Priya; Dr. Md Shahriar Karim; 1621735042Physical activity is a vital need for our survival. But in the busy world that we live in, it’s not often that easy for us to include exercise within our schedule. But the least we can do is keep track of how much physical activity we are doing throughout the day that can actually leave an impact on our physic. Running impact us differently than just walking. That’s why it is important to keep track of both individually. It becomes even more important especially if we go out to run with the intention of exercising. That’s why our goal is to create a system that can learn the difference between running and walking from data through machine learning and provides the user with a result that contains how much they ran and how much they walked separately. Many systems were developed over the years to distinguish walking and running. This project is about establishing a system that can detect whether someone is running or walking. For this project we went with two different approaches, both of which involved machine learning. Our first approach was to use a numeric dataset and apply them on different machine learning algorithms. Our other approach was to use an image-based dataset to create a CNN model. The end goal for both processes was to interphase the models with an Arduino.
- ItemOpen AccessRECK: IOT- Based Fire Alarming System and Help Service(North South University, 2019-12-31) Mahamudhul Hasan; Md Shahriar Karim; 1513157042
- ItemOpen AccessHealth care chat-bot hospital management system(North South University, 2020-08-30) MD.Harun ur Rashid.; MD. Maruf Hasan; Yeasin Arafat Prantik; Dr. ATIQUR RAHMAN; 1522117642; 1521207642; 1520722042Our main focus is design a unique Healthcare chat-bot Hospital Management System that will improve hospital experience for both patients and the hospital authorities. The whole system will run on internet. The system is written in PHP, java script, j query, HTML and CSS. Users will have the felicity to log in from any place with internet connection. After that they will be able to various tasks that are designed for them. Users are categorized in three groups :( Management, Patient and Doctor).The primary target is to focus on every user who can get our service and get benefited. It can be turned into a paid system only for doctors.Where the doctors can get additional cloud storage on payment. A doctor can have different types of patient and the number of patients also vary from doctor to doctor. A doctor can have various number of patients. We can assume that doctors will need different amount of cloud storage. We can allocate a fixed cloud storage for each doctors. They can ask for extra storage according their demand and they will be charged for their demand. We can make various package and assign various cost. The patients will have some allocated space which they can use to keep their information. As the patient only needs storage for only themselves they can use this as a free user. This will make the system useful and more convenient for everyone. Keeping the goal in mind the system we developed works as a social network where information’s are more close and relevant for every user.This report contains the full details of the system and its functionality in details
- ItemOpen AccessSpeaking System for Mute and Deaf people(North South University, 2021-04-30) Muhtasim Rafid Ahmed; Fareeza Sharara karim Bohota; Atik Mahmud; Dr. ATIQUR RAHMAN; 1530194043; 1631847645; 1711633042Deaf and mute communities are facing big problem for their disability. They are not comfortable with normal people. So, the aim of our project is to eradicate the barrier between disability and normal people in terms of communication. We have made a simple, wearable sensor-based project which is low cost and anyone can easily wear this. Our project is smart hand gloves and portable, and using flex sensor, amplifier, Arduinouno, Arduinolilypad, speaker, LCD screen, SD card. Through our project speaker can talk for mute and blind people and also deaf people can see the LCD screen. Standard ASL hand gesture taking as input database. ASL is standard sign language invented for mute and deaf people so that they are live their life as normal people. They also can work in different sector using our project. We did not use image processing or PIC microcontroller only because of high cost. We tried to make this project in low cost so that everyone can use this
- ItemOpen AccessFake Job Posting Detection Using Machine Learning(North South University, 2022-12-30) Tawfiqul Islam Talukder; S.M Shouvik Islam; Dr. Mohammad Monirujjaman Khan; 1521092042; 1712767642This report presents the design and the implementation of a system that can detect fake jobs using a machine learning method that employs a variety of categorization algorithms. The COVID-19 epidemic situation has transformed the regular livelihoods of mankind in the world. This epidemic has put excessive pressure on the job market. As a consequence of the epidemic, most organizations have halted their recruiting processes, which has raised the rate of unemployment. Online recruiting has suddenly increased the quantity of applicants while also bridging the distance between recruiters and candidates. It indicates that scammers have emerged in the online recruiting market. They provide extremely high pay ranges or any other type of benefit on several online platforms. It's called "Fake Job Postings." Job seekers are applying for those fake jobs. As a result, scammers steal their personal information. Scammers use their personal information for a variety of cybercrimes or sell it on the dark web. This paper's objective is to identify and verify these job advertisements, whether they’re fake or not. To identify these fake job advertisements, Machine Learning Algorithms (MLA) was implemented throughout this study, such as the Random Forest algorithm, and Logistic Regression algorithm. This study trained and tested the dataset and got an accuracy of 98.86 percent in Logistic Regression and 98.54 percent in Random Forest. In the Logistic Regression algorithm, our recommended technique has an accuracy of 98.86 percent, which is a huge improvement over the current methods. The accuracy percentile of both the algorithms used throughout this analysis is substantially in excess of prior studies, showing that the algorithms utilized throughout this analysis are well balanced.