Department of Information and Communication Technology (ICT)

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A Time-Nonlocal Optimization Approach for Image classification
(Comilla University, 1-Feb-2025) Das, Hridoy Chandra; Hafsa, Akter
Image classification remains a fundamental problem in artificial intelligence, with applications
An Unified Quantum Classical Model For Noisy Label Medical Image Binary Classification.
(Comilla University, 1-Feb-2025) Bhuiyan, Taki Jakera; Fahim , Jahid Karim
The presence of noisy labels in medical imaging datasets can severely impact diagnostic
"RNN's LSTM Based Deep Learning Model for Stock Price Prediction"
(Comilla University, 1-Feb-2025) Mozumder, Minhajul Islam
One of the most significant activities in the world of finance is stock trading. The goal of stock market prediction is to forecast the future value of stocks as well as other financial instruments traded on stock exchanges. Most stock brokers base their stock predictions on technical, fundamental, or time series analysis. Python is the computer language used for machine learning techniques for stock market forecasts. In this research, we propose a Deep Learning approach that can be taught using publicly available stock data to learn from experience and then apply that information to make accurate predictions. A crucial area of research is stock price forecasting because of how profitable it can be for people, businesses, and governments. The method and novel applications are examined in this paper. This study examines novel applications and techniques for forecasting a particular corporation's consistent closing price. Customers can purchase or sell assets of the companies that are regularly scheduled there. A difficult task is predicting changes in stock market prices. In this study, stock price predictions are made utilizing deep learning models like the Long Short- Term Memory (LSTM), a development of the recurrent neural network. For the purpose of prediction, the two-year datasets from 2021/02/23 to 2022/07/20 are used. This study examines novel applications and techniques for forecasting a particular corporation's consistent closing price. Customers can purchase or sell assets of the companies that are regularly scheduled there. A difficult task is predicting changes in stock market prices. In this study, stock price predictions are made utilizing deep learning models like the Long Short- Term Memory (LSTM), a development of the recurrent neural network. For prediction, the two-year datasets from 2021/02/23 to 2022/07/20 are used.
Development of a Healthcare Chatbot System
(Comilla University, 1-Feb-2025) Rahman, Md. Tareq
In order to live a better life, healthcare is crucial. Technology has never played a more importan
Fine-Tuning Large Language Models to generate websites in specific tech stack
(Comilla University, 1-Feb-2025) Md Najmul Islam Nayeem
The rapid advancement of pre-trained Large Language Models (LLMs) has unlocked significant potential in automating domain-specific tasks. Despite their widespread adoption, fine-tuning LLMs for specialized applications remains an underexplored area. This study examines strategies for fine-tuning LLMs, emphasizing methodologies for adapting foundational models to specific domains such as landing page generation. Key considerations include dataset curation, preprocessing, model architecture selection, and efficient fine-tuning techniques like Low-Rank Adaptation (LoRA).
Comparative Analysis of Fruits Disease Detection using Different types of CNN Architecture
(Comilla University, 1-Feb-2025) Islam, Md. Tohidul; Alam, Kursed
Fruit diseases faces significant challenges to agricultural productivity, leading to substantial economic losses globally. Early and accurate detection of these diseases is crucial for effective management and prevention. In recent years, Deep Learning, particularly Convolutional Neural Networks (CNNs), has emerged as a powerful tool for image classification and disease detection in agriculture.
A Deep Learning Model for Classifying Bangladeshi Food and Estimating Calories
(Comilla University, 1-Feb-2025) Nayeem, Md Najmul Islam
Accurate food calorie estimation is essential for managing dietary intake, particularly for
5G Technology and Future in Bangladesh
(Comilla University, 1-Feb-2025) Hasan, Md. Faysal
BRAIN STROKE PREDICTION USING MACHINE LEARNING
(Comilla University, 1-Feb-2025) Sarkar, Md. Mahmudur Rahman; Sarkar , Proma
The present document is a project carried out in the field of engineering to create a system
ENHANCED NETWORK SECURITY SYSTEM USING FIRWALLS
(Comilla University, 1-Feb-2025) Sadik, Abdulla Al
The Internet and computer networks are exposed to an adding number
Hateful Text Detection Using Comparative Analysis of Machine Learning Algorithm
(Comilla University, 1-Feb-2025) Shanto, Tanvir Hossen
Social media platforms' explosive expansion has increased online interactions and made it easier to share information and communicate with others. But this accessibility has also made cruel and abusive speech easier to propagate, endangering both people and communities. For internet platforms, hate speech—offensive language directed at people or groups because of traits like race, ethnicity, gender, religion, or nationality—presents a serious problem. Conventional moderation methods are unable to keep up with the sheer volume and dynamic nature of hate speech.
Asthma Disease Prediction using Efficient Machine Learning Model
(Comilla University, 1-Feb-2025) Ahmed, Kazi Tanvir; Ali, Md Ahad
Over 200 million people worldwide suffer from asthma, a common chronic respiratory condition that claims about 450,000 lives each year. The application of machine learning to aid in decision-making in the healthcare industry is growing. It works especially well for activities like diagnosis, prediction, and clinical insight in the management of asthma. By seeing trends, predicting future asthma episodes, and supporting individualized care regimens, these technologies assist enhance treatment results. By adjusting hyperparameters and implementing effective models, this work aims to enhance the prediction of asthma disease through machine learning algorithms. Many machine learning models, including XGBoost, Random Forest, K-Nearest Neighbors (kNN), Decision Tree, and Logistic Regression, were trained and evaluated using a dataset of 316801 unbalanced samples from Kaggle, enhanced by 285 survey replies. With an accuracy of 0.7012 and superior performance over other models in terms of precision, recall, and F1-score, XGBoost stood out among the rest. Model dependability was increased by applying SMOTETomek to overcome data imbalance. In order to improve classification accuracy, the study also investigates the effects of several feature selection strategies and hyperparameter tweaking. According to the results, using machine learning models that have been tuned can greatly enhance asthma prediction, enabling early diagnosis and treatment. The effort will concentrate on creating a real-time forecasting system for clinical applications, and taking genetic and environmental factors into account. This study advances AI-powered medical solutions for the diagnosis of asthma. According to this study, machine learning (ML) has the potential to be a very effective technique for forecasting asthma flare-ups. Nevertheless, these models' approaches varied widely. Future research should concentrate on enhancing the models' practicability and generalizability in order to promote their use in clinical practice
Image Cartoonization Using Deep Learning
(Comilla University, 1-Feb-2025) Seema, Tanima Sultana
The Cartoonization Project is an innovative image processing system that transforms real-world photographs into cartoon-like representations using advanced deep-learning techniques. The core of this project revolves around a U-Net architecture-based convolutional neural network (CNN), enhanced by residual blocks and guided filters, that processes input images and generates cartoonized outputs. By leveraging TensorFlow, along with various image manipulation techniques such as resizing, upsampling, and applying residual learning, the system ensures high-quality, visually appealing cartoon images. This project employs a client-server architecture, where users can upload images through a web interface (built using Flask) and obtain the cartoonized output. Designed with flexibility in mind, the system can be deployed locally or in cloud environments using services like Google Cloud, ensuring scalability and robustness. Through the integration of state-of-the-art image processing algorithms, the Cartoonization Project serves as a powerful tool for automating the transformation of real images into stylized cartoon representations, with potential applications in entertainment, digital art, and social media content creation.
Academic Assistant :
(Comilla University, 1-Feb-2025) Parves, K. M. Tousif Bin; Islam, Saqiul
The Academic Assistant is an AI-powered robot designed to enhance educational
Quantum Walk-Enhanced Hybrid Routing: Integrating Local and Non-Local Best-Effort Strategies for Robust Quantum Networks
(Comilla University, 1-Feb-2025) Dhruba, Anjan Das; Hasan , Md. Mehedi
network nodes. Unlike classical networks, quantum networks require specialized routing strategie due to the no-cloning theorem, entanglement decay, and limited quantum memory. This paper investigates the performance of different distributed routing algorithms for entanglement distribution in a quantum internet. We analyze five routing algorithms, including modified
A Project Report on COMILLA UNIVERSITY HALL MANAGEMENT SYSTEM
(Comilla University, 1-Feb-2025) Hasan, Md Mahedi; Rahman, Marfater
Comilla University Hall Management System is a software developed for managing colorful conditioning in the residence. There are five resident halls for the scholars of Comilla University. As the number of halls are adding up, the number of scholars abiding in those caravansaries are also adding. As a result, the people managing the halls are under a lot of stress. Software or applications aren’t generally used in this environment. Generally, in Bangladesh perspective no digital systems or applications are used, most of the halls or residences are managed manually. In early days, managing manually was not very tough. But at present manual system is tedious. This specific design addresses the challenges of hall management and circumvents the issues that arise when carried out by hand. When the drawbacks of the current system are recognized, lead us to the design of a motorized system that will work with the existing system and the system that is additionally easily grasped and more GUI acquainted. We can ameliorate the effectiveness of the system and therefore overcome the downsides of the being system. Comilla University Hall Management System targeted for sale operation of the hall for better control and timely response. This eliminates time day and paper deals being marked. The Warden is handed with a better control over the deals like adding the details of new scholar in the hall, modifying the details of the scholars, deleting the scholars, viewing the pupil’s details abiding in the hall, adding or removing rooms, checking available seats. scholars can also check if there's any room available or not, look for better seats. As the main target of this application is to do efficient management by storing scholar’s data, MongoDB is used as database as it offers numerous benefits, especially its ability to manage unstructured data, its straightforward process for horizontal scaling, and its quicker performance in insert and update tasks, which makes it perfect for applications that require rapidly evolving data structures and handle large volumes of data. In the future, we can implement online fee payments, integrate various load balancers, develop a mobile application, and establish a backup database for improved security. This design’s main aphorism is to reduce the effect of wardens and give better service to the scholars. The main thing of this design is to develop a system for the robotization of the hall affiliated information
Smart Vehicle Number Plate Detection System
(Comilla University, 1-Feb-2025) Latif, Md. Abdul
The implementation of number plate recognition systems has significantly improved the city's traffic conditions. These systems provide guidance for creating an efficient intelligent transportation system. With the rapid growth in vehicle numbers, Automatic Number Plate Recognition (ANPR) has become a crucial tool for traffic management. ANPR plays a vital role in traffic and security surveillance, utilizing advanced technology and image processing techniques to automatically identify characters on vehicle license plates.
Medical Image Classification using Hybrid Quantum-Classical Neural Network
(1-Feb-2025) Jalal, Shah; Hossain , Md. Robin
Medical image classification plays a crucial role in healthcare, enabling accurate
Human Gender and Age Detection Through Image Processing Technique
(Comilla University, 1-Feb-2025) Islam, Md. Saiful; Mia, Md. Shahansha
Interest in automatically classifying people's age and gender from face photographs has grown since the introduction of social media. Thus, the age and gender categorization process is an essential step in many applications, including interest group targeting, aging analysis, face verification, and ad targeting. However, the majority of age and gender classification systems still have some issues when used in practical settings.
AI Chatbot For Solving Mathematical Problems Using Large Language Models and Retrieval-Augmented Generation (Rag) With Custom Dataset Integration
(Comilla University, 1-Feb-2025) Ahmed, Junaid; Mahin, Md. Mohiuddin Khan
Mathematical problem-solving is a fundamental skill in education, and recent advancements in artificial intelligence (AI) offer innovative ways to automate this process. This paper presents an AI-powered chatbot developed using Google Vertex AI Agent Builder, designed to solve mathematical problems across various domains. By leveraging Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), the chatbot integrates a custom dataset to enhance its problem-solving capabilities, allowing it to generate accurate and contextually relevant solutions. The RAG technique enables the model to retrieve external information dynamically, improving the chatbot's ability to handle a wide range of mathematical problems. The integration of a custom dataset ensures that the model can effectively tackle specific problem types, making it adaptable to different scenarios. This work demonstrates the potential of AI-driven tools, powered by Google AI Agent Builder, to provide instant, reliable mathematical assistance, offering valuable support for students and educators alike