NSU IR :: Browsing by Author "Dr. Mohammad Monirujjaman Khan"

Browsing by Author "Dr. Mohammad Monirujjaman Khan"

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Open Access
A Website and Mobile App Based Intensive Care Unit System
(North-south University, 2018-04-13) Halima Khanom Jolly; Sazzad Hassan Shawon; Junaid Ibna Jafor; Dr. Mohammad Monirujjaman Khan; 1330121042; 1230733042; 1321108042
This report presents the design and the implementation of a website and mobile application based Intensive Care Unit (ICU) system in Bangladesh. In this system any patient and their relatives can find an ICU/CCU/NICU for them with proper information. There is a lack of hospitals and proper ICU facilities in our country. The main purpose of this project is to provide better opportunity for those sufferer patients who can’t find an ICU in their critical time which may sometimes cause a painful death. The system has been designed in such a manner that will help a user as well as a doctor of both rural and urban areas. This system also allows hospitals to share their ICU’s information with the users. We developed this system to help the people of our country who are badly suffering from not having a good ICU system in an easy and affordable way. The system will be accessible from our website and android app via the internet. The report includes content designing, website development and android mobile application development.
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.
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
Open Access
"Analyzing the Influence of Social Media on Students' Academic Performance Using Machine Learning and Deep Learning Approaches "
(North South University, 2023) Nida Shahid; Rita Rahman; Farhana Ahmed; Dr. Mohammad Monirujjaman Khan; 1911981642; 1911212642; 1813289630
Analyzing the Influence of Social Media on Students' Academic Performance Using Machine Learning and Deep Learning Approaches : Social media (SM) are online media technologies that allow people to share and exchange information, ideologies, preferences, and other forms of expression. Since the 1990s, social media platforms have been utilized as an effective method of communication. People can more readily engage with one another and share ideas, information, and opinions because of the proliferation of social media platforms. The worldwide community is more connected than it has ever been before because of the rise of social media. Even in Bangladesh, social media users are increasing rapidly. Studies have shown that students' academic performance tends to worsen when they spend more time on social media. The reason for this is that they choose to engage in conversation with their friends on social networking sites rather than read a book during their downtime. Their social lives and mental health are also negatively impacted, in addition to their academic performance. On the other hand, utilizing social media does come with a few advantages to consider as well. Currently, social media is responsible for the creation of a great deal of both negative and positive aspects. Students are placing a greater emphasis on their use of social media than they are on their ability to read and write, which is leading to a significant loss of both time and academic performance. Consequently, the purpose of this study is to investigate the impact of students' use of social media on their academic performance by applying machine learning (ML) and deep learning (DL) algorithms. As there have already been many studies done in this field, our study focused on university students in Bangladesh. This study used a unique set of data. In this study, we develop this model by utilizing both traditional ML and DL techniques. These algorithms are referred to as Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Gaussian Naïve Bayes (GNB), and Mini Batch Gradient Descent (MBGD). Accuracy is used as the evaluation metric of our models. Among them, Mini Batch Gradient Descent achieved the highest accuracy rate of 99.25% and Random Forest achieved an accuracy rate of 96.43%. This study, on the whole, came up with satisfactory results, successfully forecasting with an excellent level of accuracy.
Open Access
Deep Learning Technique To Understand And Analyze Deep Fakes
(North-south University, 2020-09-30) Nayan Dhar; Riaz Rahman Rafi; MD.Shirajul Islam Shadhin; Dr. Mohammad Monirujjaman Khan; 1610410042; 1620590042; 162701042
Deep fakes are the end result of virtual deception to create convincing motion pictures to mislead the viewer. To accomplish this, high-intensity mastering algorithms based entirely on autoencoders or GANs are used, which can be easily accessible and correct year after year, resulting in fake motion pictures that are difficult to distinguish from real ones."Seeing is believing" is now not actual, and this has far-accomplishing implications for many aspects of our lives. Deepfakes are getting easier and easier to create as the generation advances. In truth, some of it could be carried out with an app in the palm of your hand. Deepfakes are tough to spot. Deepfakes have grown hard to detect with the naked eye.Deep learning-based video modification tools have grown more widely available in recent years. People can simply learn how to create deep fake videos with victims and target images with little to no effort. This poses a significant social issue for everyone whose images are publicly accessible on the Internet, particularly on social media platforms. According to a recent Google survey conducted from December 2018 to December 2020, the number of online deepfake movies increases every day. In December 2020, there were 85,084 videos online, compared to 7,964 videos in December 2018. As a result, it is rapidly growing. There are several methods to detect deep fakes. The objective of this paper is to expose deep fakes with deep learning techniques. Inception-ResNet-v2 was used to detect deep fakes, which is a deep learning technique. The detection has been done with the use of 3 datasets, which have been taken from Kaggle and GitHub. Deepfake was detected using Python 3, Google Colab, and Keras as the frameworks. We have found 98% accuracy by using Inception-ResNet-v2 with the datasets.Deep learning algorithms have advanced to the point 6 where it's now feasible to create splendid-practical pictures and movies, called "deep fakes." Those deepfakes have the capacity to attain a massive target market and have negative effects on our society. In spite of the fact that a variety of efforts have gone into detecting deep fakes, their performance pales in comparison to ours.In this paper, we endorse the use of deep learning to find a residual network architecture for deepfake detection in an adaptable way. This, inception-resnet-v2, is one of the best methods for detecting deepfakes using deep mastering. In comparison to advanced techniques, our proposed approach is significantly less expensive.rds competitive prediction accuracy based totally on our studied search space
Open Access
Development of an IoT Based Gas Wastage Monitoring, Leakage Detecting and Alerting System
(North South University, 2020-12-31) Md. Ibtida Fahim; Nowshin Tabassum; Abrar Ahamed Habibullah; Aritra Sarker; Dr. Mohammad Monirujjaman Khan; 1621749043; 1620732042; 1621549045; 1610779045
Leakage of any sort of gas has become a fear in present times whether it may be domestic house hold, factory, kitchen in any restaurants, canteens etc. In this paper development of an IoT (Internet of Things) based gas wastage monitoring, leakage detecting and alerting system is proposed. This paper elaborates design such an intelligent system that will help save gas and smartly prevent accidents. The system needs to be integrated with the cooker. There are a ultrasonic sensors integrated with the system that will find out if the cooker is being used for cooking purpose or not. If it is found that the cooker is not in use there is automated switching off technique in the system to turn off the supply of gas. The moment gas leakage will probably be recognized, users will be informed via SMS through GSM, so that user can solve the issue as soon as possible. The system will monitor flame and fire through flame sensor. The buzzer starts beeping whenever fire is detected. In addition to these, the system also includes cloud storage feature. With the help of this cloud storage system the use of gas for per day of per user can be monitored. This process will help to detect the misuse of natural gas of per user at the end of the day. The system has been tested and it is able to monitor gas wastage, leakage and send a SMS to the user. The resulting performance indicated its effectiveness toward saving a significant portion of the wasted gas in domestic.
Open Access
Heart Failure risk prediction and visualization using Machine Learning algorithms and Artificial Neural Network.
(North-south University, 2021-01-02) Polin Rahman; Md Iftehad Amjad Chy; Ahmed Rifat; Dr. Mohammad Monirujjaman Khan; 1531377042; 1612547042; 1620746042
Open Access
HOME AUTOMATION SYSTEM USING BRAIN WAVE
(North South University, 2018-11-30) Mahmudur Khan Tanzid; Zobayda Hossan Arshie; Saima Islam; Shahedur Rahman; Dr. Mohammad Monirujjaman Khan; 1410082042; 1410288042; 1420449645; 1321732043
Brain-computer interface (BCI) is a communication pathway between the brain and the external peripheral devices like computers. We propose to use this technology for Home automation. Home automation can be totally revolutionized using BCI. The brain produces various types of waves like alpha (9-13Hz), beta (14-30Hz), theta (4- 8Hz), delta (1-3Hz). Using these waves we can control various home appliances. The entire concept consists of 4 main stages of detection, amplification, processing, output. First detecting the brain signals using an EEG cap or electrodes. These brain signals are very weak hence in the second stage we need to amplify these brain signals to a usable amount and filter these to remove noise. Then thirdly, we will have to convert these signals into digital by using A to D converter and into a type, a computer software or a microcontroller can understand. Fourth, taking this decoded signal and sending these signals wirelessly, by using an RF circuit to a distant switch circuit, which will turn on or off the appliance in the vicinity. Using this technology, the life of people would be further simplified, physical efforts would be considerably reduced and it would also prove as a boon for physically disabled people.
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.
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.
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.
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.
Open Access
Senior Design Project Design and Analysis of a 28GHz 5G Triangular Shaped Wideband Antenna for Wireless Body Centric Network
(North South University, 2023) Tahsin Rubaiyyat Khan; Md.Miftahul Zannat Alif; Amit Raiyan; Dr. Mohammad Monirujjaman Khan; 1921032643; 1921974043; 1921919643
5G stands for fifth-generation mobile network. 20 GB/s is the maximum speed of 5G, which is also more efficient and has ultra-low latency. We have built and simulated triangular-shaped compact 5G wideband microstrip antennas for body-centric networks (BCN) operating on 28 GHz. A microstrip antenna is made out of a very thin metallic strip placed between a ground plane and a dielectric material. The radiating element and feed lines are etched onto the dielectric material using photoetching. The design of the antenna consists of a triangular radiator patch. This antenna is created and modeled with the aid of computer simulation technology (CST), which is very popular for antenna design. This antenna is designed to operate at 28 GHz. The desired 28 GHz frequency response is achieved by careful parametric modeling. The materials of the triangular antenna of the patch and feedline are copper (annealed), and the substrate and ground are made up of FR-4 (loss-free). The maximum achieved gain at the desired resonance frequency of the triangular antenna in free space is 5.91 dBi, and the total efficiency is 80.24%. On the other hand, the maximum gain in on-body simulation is 8.701 dBi at 4mm body distance, and the highest total efficiency found is 72.57% at 8mm body distance. The value of VSWR in free space is 1.48. In addition, 1.44 is the lowest achieved VSWR value in 4mm and 6mm body distance in on-body simulation. The values we achieved are close enough to the targeted values.
Embargo
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.

Browsing by Author "Dr. Mohammad Monirujjaman Khan"

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