http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/54 Sat, 27 Jun 2026 11:47:52 GMT 2026-06-27T11:47:52Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17379 An End-To-End Efficient License Plate Detection and Recognition System using Deep Learning Bristi, Nushrat Jahan This research presents an enhanced license plate recognition system for real-time detection and recognition in transportation and security applications. YOLO object detection algorithms (YOLOv8s, YOLOv8x, YOLOv11s) enable accurate license plate localization, while EasyOCR ensures reliable alphanumeric identification in challenging situations, including low light and complex backgrounds. Testing on diverse datasets demonstrated high accuracy, with YOLOv11 and data augmentation achieving a peak F1 score of 98%. The system also addresses Bengali character recognition challenges, offering a foundation for region-specific improvements. These outcomes validate the system's effectiveness for law enforcement, traffic management and security. Thesis report Mon, 13 Jan 2025 00:00:00 GMT http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17379 2025-01-13T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17246 A Framework for Human Skin Disease Classification Using Convolutional Neural Network Hera, Mst. Dilruba Yeasmin One of the most dangerous types of cancer is skin cancer, it becomes a significant health hazard when not treated and detected on time. Skin cancer may spread to other parts of the body and complicate treatment if it is not detected in its early stages. Mainly it is the result of abnormal skin cell growth, usually the cells are stimulated by the sun for a long time. The early detection of skin tumors is a basic but highly complicated and expensive process due to the complexity of the diagnostic methods implicated. The identification of skin cancer by the location and cells involved augments the necessity of a very precise classifier for a successful diagnosis. Where the use of CNN in the recognition and classification of skin cancer, especially in skin lesion classification has been proposed to solve this issue. The utilized diagnosing method includes the utilization of image processing algorithms and deep learning models to increase accuracy and efficiency. Methods like image augmentation are then used for adding more rows to the dataset are used to scale up the dataset. This way, the model understands the diverse cases encountered. In addition, transfer learning is useful for increasing the classification accuracy by using pre-trained models for improved performance. As one of deep learning's deep architectures, CNNs serves as a key player in the extraction of features and in the classification of skin problems like psoriasis. This technique has been impressively productive for it gets a hit rate of 75%, thus revealing future prospects in the medical field. Project Report Mon, 20 Jan 2025 00:00:00 GMT http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17246 2025-01-20T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16818 Economic Transformation through Mobile Payment Advancements and Innovative Solutions Abdilahi, Abdiqadir Muse A new age of economic revolution has begun with the development of mobile payment technologies, which have completely changed the way that financial transactions and commercial exchanges take place. The broad effects of new developments in mobile payments and creative solutions on economic systems are examined in this thesis. Due to their ease of use, quickness, and wide accessibility, mobile payments have upended established financial processes, promoting financial inclusion and boosting the economy in a variety of industries. Mobile payment systems now have much improved security, dependability, and user confidence because to the incorporation of the digital currency and artificial intelligence and biometric identification. These technology developments have lowered transaction costs and expedited financial transactions, which has increased consumer spending and supported the expansion of small and medium-sized businesses (SMEs). This thesis offers a thorough examination of the financial gains resulting from improvements in mobile payments. It looks at case examples from different parts of the world to show how new technologies are changing both local and global economy. Through their potential to facilitate smooth financial transactions and enhance market efficiency, mobile payments have emerged as a powerful force for economic growth and stability. The study emphasizes how important mobile payment networks are to promoting equitable economic development, improving financial stability, and enabling effective market dynamics. As these technologies develop further, maximizing their economic potential and tackling the issues of financial exclusion will depend heavily on how strategically they are used. Thesis Sun, 14 Jul 2024 00:00:00 GMT http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16818 2024-07-14T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/14556 Improved Explainable Educational Data Mining System for Enhancing Programming Skills Mohamud, Mohamed Abdulle Forecasting student academic performance benefits from the extremely effective method known as educational data mining (EDM), which also helps to find important links within educational data. Evaluating and improving students' programming competency has been the main emphasis of many recent studies. Still, there are chances for constant development in this field. In this work, we provide an improved and understandable Educational Data Mining (EDM) approach for spotting and improving students' programming capacity. This proposed EDM system seeks to investigate a very effective feature engineering approach, a suitable classification technique, and the use of Explainable Artificial Intelligence (XAI) tools for model explanation. We do ablation study to find the best feature engineering method. The categorizing process decides students' current programming state. Six basic Machine Learning (ML) algorithms—decision tree, Support Vector Machine, Random Forest (RF), artificial neural network, Naive Bayes Classifier, k-Nearest Neighbor, and Ensemble method—are the main subjects of this module. Many criteria—including accuracy, precision, recall, f1-score, ROC curve, McNamar test, and others—are used to assess the performance of these algorithms. The experimental results show that among the many models, the Random Forest (RF) and the Stacking-SRDA ensemble technique can classify the students with more accuracy than others. To improve the interpretability of the model, we have finally used XAI technologies like Eli5, SHAPASH, and Local Interpretable Model Agnostic Explanations Thesis Thu, 18 Jul 2024 00:00:00 GMT http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/14556 2024-07-18T00:00:00Z