Faculty of Science and Information Technology
http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16
2024-03-29T12:51:07ZEntrepreneurs Consultant
http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/11897
Entrepreneurs Consultant
Mamun, Abdulla Al
The "Startup Repair" initiative is an innovative web application that aims to foster
cooperation and support between newbie entrepreneurs and experienced consultants.
Users are granted the ability to conveniently register, locate consultants by their
names, Ids, or areas of expertise, and arrange appointments for the provision of expert
guidance. Tailored dashboards offer functionalities including payment history, profile
management, and appointment monitoring to accommodate the particular
requirements of users, consultants, and administrators. The incorporation of
SSLCommerz guarantees dependable and secure payment transactions, thereby
augmenting the overall satisfaction of users. The primary objective of the platform's
dedication to user-centric design is to streamline the consulting procedure and foster a
dynamic community where expertise and knowledge collide in order to facilitate the
triumph of startup endeavors. Through the utilization of technology and the promotion
of smooth interactions, "Startup Repair" endeavors to serve as a crucial resource for
individuals commencing entrepreneurial endeavors by providing a collaborative
environment conducive to mentorship and advancement within the startup ecosystem.
2024-01-23T00:00:00ZSmart To-Let System
http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/11896
Smart To-Let System
Rony, Md. Rokunuzzaman
The Smart To-Let System is a user-friendly website for property management. It has a login
page, profile page, and a special rent page with categories like Buying House and Sublet. What
makes it unique is the zone-wise service, letting users search for properties in specific areas. The
rent page has two sections - "Visible Rents" for quick property views and "Add New Rents" for
detailed listings. It's designed to help both tenants and landlords easily find or list properties.
With a simple interface and strong technology, this system brings efficiency and transparency to
real estate.
2024-01-23T00:00:00ZDrivers Drowsiness and Mental Health Detection using Deep Learning
http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/11858
Drivers Drowsiness and Mental Health Detection using Deep Learning
Hossain, Md. Faisal
One of the most important body part is the face which holds a lot of information. Any person's
mental state is revealed through their facial expression.The purpose of the study is to develop a
system that can ensure safe driving with great accuracy. The main objectives of this study is to
detect whether the driver is asleep or not, avoid road accidents, eliminate reckless driving, alert
drivers about their mental and emotional situation. For this research, I have collected data from
two different datasets. One is FER-2013 and another one is drowsiness dataset for open eyes and
closed eyes. The images of the emotion dataset contains only the face which are enough cropped
and the drowsiness dataset contains only the eyes. I have used angry, fear, happy, neutral, sad and
yawning for emotion classifications. I have used 4 deep learning models in this research. The 4
four models are Xception, InceptionV3, ResNet50 and VGG19. These neural network models are
used for feature extraction and classification tasks. The model that gives the higher accuracy than
other models is Xception. In both tasks, The Xception outperformed the competition. For eye
detection, it obtained 98.97% accuracy, and for face emotion detection, 99.26% accuracy. It
showed excellent accuracy and metrics when it came to classifying emotions. The model performs
quite well, with an average precision, recall, and F1-score of about 0.99. Overall, Xception
performed exceptionally well across a number of emotion classifications and attained 99%
accuracy on the eye dataset. The weighted and macro averages both confirmed the effectiveness
of the system. This study suggests an improved pretrained model based approach for detecting
driver’s inattention, which will ensure safe driving with great accuracy and improve the drivers
driving efficiency. In future, I will work on adding night vision capabilities, the system will be
able to identify and recognize objects better and adapt to different driving circumstances more
easily. Furthermore, it may be proposed that driving behaviors like speeding, safe driving, and
braking suggest a more advanced system. The combination of these factors can result in an
improved and more advanced system that can identify and mark drowsy drivers, improve drivers'
concentration, and reduce the number of traffic accidents.
2024-01-18T00:00:00ZHenna Artist Appointment Booking Website
http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/11856
Henna Artist Appointment Booking Website
Priyanka, Nishat Nayala
First of all, I am grateful to the Almighty Allah for making me eligible to complete this
project. Then I would like to thank my supervisor Dr. Imran Mahmud, Associate
Professor and Head. I am extremely grateful and indebted to him as he has given me
his expert, sincere and valuable guidance and encouragement.
I would like to thank everyone who helped me in my project by their important
suggestion. Without their passionate participation and input, the project could not be
successfully conducted. I take this occasion to convey my sincere thanks to all faculty
members of the Department of Software Engineering for their help and encouragement.
2024-01-18T00:00:00Z