Browsing by Author "Dr. Mohammad Monirujjaman Khan"

Now showing 1 - 7 of 7
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    Open Access
    Advanced Virtual Classroom and Automated Grading System
    (North South University, 2023-09-30) Alif Al Razi; Abdul Monim; Dr. Mohammad Monirujjaman Khan; 2011358042; 1712556642
    The Advance Virtual Classroom and Automated Grading System, which aims to revolutionise the traditional education system by integrating virtual classroom functionalities and automated grading, highlights the need for flexible and accessible learning platforms that can enhance student engagement and streamline the grading process. The current status of the system indicates its final stages. It mentions that the system has been designed and implemented with the goal of providing a comprehensive virtual classroom experience and automating the grading process to improve efficiency and accuracy. The methods followed in the development of the system mention the utilisation of advanced technologies, such as web development frameworks and machine learning algorithms, to create a user-friendly virtual classroom environment and implement automated grading mechanisms. The abstract also highlights the integration of secure examination systems to ensure the integrity of assessments. The results acquired through the implementation of the Advance Virtual Classroom and Automated Grading System It states that the system has successfully provided students with a virtual classroom experience that includes features such as live video lectures, interactive discussions, and collaborative tools. Additionally, the system's automated grading component has demonstrated improved accuracy and efficiency in evaluating student assessments. The impact and significance of the results obtained from the system It highlights the potential of the Advance Virtual Classroom and Automated Grading System to revolutionise education by enabling flexible and accessible learning, enhancing studentteacher interaction, and reducing the administrative burden of manual grading. The system's implementation can lead to improved learning outcomes, increased efficiency in grading, and a more engaging educational experience for both students and instructors.
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    Open Access
    An economic and automatic water quality monitoring system in the light of industry 4.0
    (North South University, 2022-04-22) Abrar Zuhaer Tariq; Dr. Mohammad Monirujjaman Khan; 1431152043
    Water quality monitoring is significant for sustainable aquaculture. It helps to reduce the risk of unwanted fish loss due to poor water quality, as a result, farmers can maximize profit. DO, Ammonia, pH, Temperature, Turbidity and TDS are the key parameters and their correct level in water ensures favourable conditions for aquaculture. However, in the aquaculture industry, DO and ammonia measurement with digital systems are costly. Eventually, it is inevitable for the aquaculture farmers to measure the other parameters (pH, Temperature, Turbidity and TDS) by the side of DO, Ammonia. Therefore, we have developed a system in the light of industry 4.0 with the aid of IoT that can monitor automatically the above-mentioned water quality parameters without human involvement and keep the users updated remotely with the help of an android app. The system is expected to achieve more than 98% accuracy in DO tests and nearly 99+% accuracy in ammonia measurement in water. Other parameter readings like temperature, turbidity, and PH have produced a high precision result as modern sensors are used. Our study shows that the developed system can cut down the overall cost (accusation and test) by approximately 85% and it will open a new door of opportunity for aquaculture automation. DO, Ammonia, PH, Temperature, Turbidity and TDS
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    Open Access
    Machine Learning Based Comparative Analysis for Celiac Disease Prediction
    (North South University, 2022) Faija Islam Oishe; Fardin Bin Islam; Dr. Mohammad Monirujjaman Khan; 1821720042; 1721588642
    Celiac disease is a safe-framework condition that mostly affects the small intestine but can also affect the skeleton. Histological analysis of duodenal biopsies obtained through upper digestive endoscopy is used to make the diagnosis. During immunological tests, a blood sample is taken to see if the body has made antibodies. Histology requires endoscopy, which is invasive and takes a long time. In recent years, several algorithms have been developed to process images obtained from capsule endoscopy, a non-invasive endoscopy procedure that yields high-quality, magnified images of the small bowel mucosa. Using these images, a diagnosis can be made quickly. These algorithms make use of neural convolutions (CNN, or convolutional neural networks) as well as artificial intelligence (AI). Additionally, when disease is anticipated, vital information is sent to patients prior to the illness' onset. Using the information withdrawal procedure, previously overlooked data can be removed to eliminate a significant amount of celiac disease-related data. A system that can accurately predict a patient's risk of developing celiac disease is the goal of this study. The method was developed using an open-access dataset on celiac disease prediction. The dataset has numerous significant values, despite its small size. We took a gander at the information and made a couple AI models. The decision tree classifier, the random forest classifier, logistic regression, the Knearest neighbor classifier, and the convolutional neural network were utilized in the prediction process. The degree of improvement in celiac disease may also be helpful. A gluten-free diet is the main treatment for stopping the autoimmune process and improving the villi in the small intestine. The fact that the algorithm uses two modified filters to properly analyze the texture of the intestine wall is novel. For the logistic regression model, it attained an accuracy of 94%; for the random forest, 83%; for the decision tree model, 76%; for the K-nearest neighbor, 81%; and for the convolutional neural network, 99%. It is demonstrated, by means of the appropriate flyers, that the appropriate diagnostic can be obtained through image processing without the need for a complex machine learning algorithm.
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    Embargo
    Markerless Location Based Augmented Reality Application For Showcasing Deals
    (North South University, 2019-04-30) Mohammad Sadman Islam; Fyeeza Fyruz; Md. Nahiyan Naser; Gazi Shafayet Hossain; Dr. Mohammad Monirujjaman Khan; 1510151642; 1510152042; 1510718042; 1430364042
    This article describes the design and implementation of an online mobile app that is capable of locating deals and displaying related information on a digital Augmented Reality window through a smartphone’s camera. This application is a two-tier client-server architecture. A deal must firstly be registered on our website by an entity, separate from regular users. DealTeal AR makes use of the camera view to overlay digital information of locations around you, according to the direction from your current location in the real world. When you tap on DealTeal AR, it shows a view through your smartphone's camera. Cards appear showing information or deals pulled from DealTeal's database about businesses - restaurants, hotel, points of interest and more. With advanced augmented reality technologies such as computer vision and object recognition, the deals are overlaid on the real world and becomes interactive. In order to improve the application’s efficiency, a virtual terrain modeling interface with deep learning to improve the building recognition ability was also used.
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    Open Access
    OnDoctor-Telemedicine Service
    (North South University, 2019-08-31) Rezaul Karim; Imtiaz Habib; Homiara Islam Parisa; Dr. Mohammad Monirujjaman Khan; 1410185042; 1420433042; 1420472042
    This is a web based system using Django framework for backend development and for frontend it used html5, css3, bootstrap4, here patient can video call to registered doctor, chat with the doctor, booked appointment, buy medicine online, web application where user can find the primary health care by talking to the doctors online through video call system and also book appointment to the required doctor through this platform. The doctors are specialized and highly professional in their field. Website has also blog site where doctor post different health issue to make awareness among the society.
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    Open Access
    Senior Design Project : Dual Axis Solar Tracking System With Reflector And PV Meter
    (North South University, 2019-12-31) Md. Shihabul Alam; Anisa Binta Kamal; Quazi Nazmus Sakib; Dr. Mohammad Monirujjaman Khan; 1421719043; 1420745043; 1320623043
    The world is using up all the resources to meet the daily demands of energy and it is quite expectable that in the near future we will run out of any naturally occurring ore/mineral/petroleum. As a result, renewable energy solution has achieved a great The world is using up all the resources to meet the daily demands of energy and it is quite expectable that in the near future we will run out of any naturally occurring ore/mineral/petroleum. Demand today to save the natural resources and also to tackle the crisis of energy. Solar energy is rapidly gaining its popularity as an important source of renewable energy. But the efficiency of solar panel is a big factor. While the sun keeps following a parabolic path throughout the day, the panels which are used in our country are generally fixed to a pole or the roof of the house and hence, throughout the day, the efficiency decreases significantly. In this thesis, we have constructed a 2 axis solar tracker which can track the sun throughout the day to obtain the maximum efficiency. This project discuss the design and construction of a prototype for solar tracking system that has a single axis of freedom. Light Dependent Resistors (LDRs) are used for sunlight detection.
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    Unknown
    Stock Market Price Forecasting Service using LSTM
    (North-south University, 2019-04-30) Md. Mahabubul Hasan; Pritom Roy; Sabbir Sarker; Dr. Mohammad Monirujjaman Khan; 1421274042; 1430378042; 1420134042
    In recent years we’ve seen in Bangladesh that the Stock market is not stable at all. In 2011 Stocks continued to tumble amid jitters over banks' liquidity crisis. After starting the day at 5,710, DSEX, the benchmark index of the Dhaka Stock Exchange, plunged to below 5,700 points in less than half an hour. Eventually, it lost 81.92 points to close the day at 5,623.64. People invest a lot in stock market. Many people just lost their hard-earned money in a blink of an eye because of investing at the wrong place. But we’ve seen the vice versa situation from the stock market as well. Stockify comes in play here. Stockify is dedicated to show accurate predicated prices of market shares. 70% accuracy have been achieved via training the algorithm Using LSTM which is an artificial neural network. Deep learning algorithm library TensorFlow have been implemented to show us predicted price using the closing prices of a day. Currently we are providing free service via our website and application. It would be on subscription based when the site will be more enriched in a business level. Thus, we hope to help people to make a profit in stock market and upraise the stock market of Bangladesh.