http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16 2026-05-16T18:02:54Z 2026-05-16T18:02:54Z Yeasir, Z M Touki http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17199 2026-05-16T02:37:17Z 2025-05-14T00:00:00Z

Hospital Management with Online Booking System Yeasir, Z M Touki The proposed project, "Hospital Management with Online Booking System," is a webbased solution designed to simplify the process of searching for and booking appointments with doctors. It addresses the common issue of manually managing and scheduling appointments, which can be time-consuming and inefficient for both patients and medical staff. Through this system, users—primarily patients—can easily view available time slots and select a preferred day and time that aligns with their schedule. This application provides individual login access for both doctors and patients. Patients can browse available doctors, book appointments based on availability, and even cancel bookings if necessary. Doctors, on the other hand, can manage their schedules, approve or reject appointments, and maintain a list of confirmed consultations. This system reduces the burden on hospital staff and allows patients to receive timely care without long waiting times. The system is built using modern web technologies including HTML, CSS, JavaScript, jQuery, Bootstrap, and server-side scripting with PHP and Laravel framework for secure and scalable backend processing. Overall, the system promotes efficient hospital operations, enhances user experience, and ensures smoother communication between doctors and patients through digital automation. Project Report

2025-05-14T00:00:00Z Anu, Aunirudra Dey Chakraborty, Karina http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17198 2026-05-16T02:36:53Z 2025-05-14T00:00:00Z

Explainable AI (XAI) driven Mango leaf disease detection using State-ofthe-art convolutional neural network and Tiny-Net Anu, Aunirudra Dey; Chakraborty, Karina Rising agricultural requirements for intelligent, accessible solutions demonstrate why efficient plant disease detection systems have become vital. A deep learning framework has been developed to automatically detect mango leaf diseases while providing mobile access through an explainable framework solution. Six state-of-the-art convolutional neural network (CNN) models underwent initial evaluation on the MLD24 dataset, with DenseNet101 producing the most precise classification results of 99.06%. After researchers realised that traditional models were unsuitable for practical deployment due to their computational constraints, a custom lightweight model called Tiny-Net was developed. The Tiny-Net model delivered performance like standard models, achieving 99.61% accuracy while using minimal system resources, thus meeting the needs of mobile applications. The Explainable AI (XAI) methods Grad-CAM and LIME and SHAP provided transparent analysis of model decisions to increase system transparency and foster trust. The TensorFlow Lite version of the final model received implementation within a Flutter-based mobile application which delivered real-time offline detection capabilities accessible to farmers in regions with low connectivity. The system proved resistant to failure while demonstrating ease of use through multiple evaluations that proved its real-world utility. Through this research researchers demonstrated progress in automated plant disease detection technologies while using human-centered artificial intelligence deployments to benefit sustainable farming practices. The proposed system proves its ability to enhance farmers' disease diagnosing capabilities through real-time accurate diagnosis while closing the gap between controlled laboratory models and realistic usage conditions. Project Report

2025-05-14T00:00:00Z Alim, shraful http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17197 2026-05-16T02:35:00Z 2025-09-14T00:00:00Z

Vulnerability Detection in Source Code Using AI Alim, shraful This project delves into the critical need for automated source code security checks in today's modern software development process. The software development process in the industry is growing more complex day by day, the challenge of finding security weaknesses inside source code is also becoming hard. My work, "Vulnerability Detection in Source Code Using AI," offers an AI-driven method to detect and guide users for potential security problems in application source code early in the development phase. The main objective is to build an intelligent system that learns from deep learning and then uses its learning to analyse application source code and pinpoint where is the vulnerabilities. I am focusing on issues like vulnerable code injection points, insecure object handling by the code, improperly handling user data, and the other misuse of programming functions. The system learns by studying labeled dataset and from a teacher model of both secure and vulnerable code, allowing it to recognize patterns linked to insecure coding inside of an application. The overall process of my application includes preparing different code samples, extracting key features and labeling, training deep learning models from specialized teacher model, and assessing the system's accuracy and performance. The end goal of my project after successful completion is, to lessen the need for manual application source code review for application developers, educate the users with security best practice and boost the overall security inside application development process. While the current system successfully identifies several common vulnerabilities, there's room for improvement. Future work includes expanding language support and integrating the system into realtime development tools. Thesis Report

2025-09-14T00:00:00Z Sultana, Jarin http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17196 2026-05-16T02:34:40Z 2025-09-18T00:00:00Z

Building Trust with Explainable AI in Lung Disease Detection Using Deep Learning Sultana, Jarin The primary aim of this research is to design an XAI system for lung disease diagnosis that achieves the highest degree of accuracy possible in a more user-friendly manner than current systems. The EfficientB5 Network model is proposed for implementation as the main architecture. It has got the promise to achieve high accuracy rates up to 99.13%, but with more confidence, it could classify three types of lung diagnoses: Benign, Malignant, and Normal. For the augmentation of trust and usability in clinical practice, Grad-CAM visualization techniques based on Explainable AI will be utilized. The methodology highlights the class-specific probabilities and regional contributions as interpretable justification from the patient's or clinician's viewpoint for understanding the prediction outcome. While lung cancer remains the most lethal cancer among all cancers, it had 2.5 million cases worldwide in 2022, with more than 1.8 million deaths just in that year. The datasets used in this research are public and hence no restrictions are exercised on the utilization of the data. Validation has thus far been carried out comparing EfficientNetB5 against another pre-trained models, InceptionV3, ResNet50 and Vision Transformer. In particular, all pre-trained models were subjected to extensive experimentation to demonstrate the need for achieving a combination of explain ability plus technical accuracy ensuring trust in AI systems for a patient-centered healthcare, and thereby pushing naturally to become a new reality with this medical season Thesis Report

2025-09-18T00:00:00Z