https://ar.iub.edu.bd/handle/11348/20 Thu, 10 Sep 2026 15:55:33 GMT 2026-09-10T15:55:33Z https://ar.iub.edu.bd/handle/11348/1569 Detecting adaptive wash trading behaviors in NFT marketplaces: a neuro-symbolic approach Shatabdy, Shamsun Nahar; Raihan, Rabiul Islam This study proposes a neuro-symbolic framework to detect increasingly sophisticated NFT wash trading that traditional rule-based systems and standard GNNs struggle to identify. The proposed ResidualGATv2 model combines heuristic logic with spatio-temporal graph learning and incorporates transaction timing through edge-level temporal encoding. Tested on 108,588 real NFT transactions, the model achieved 0.99 ROC-AUC, 0.93 PR-AUC, and 0.87 F1-score, while forensic review confirmed 93.6% of identified suspicious wallets as genuine wash traders. The model also demonstrated strong generalization to previously unseen NFT collections, highlighting its potential for effective blockchain fraud detection. This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science (BSc) in Computer Science and Engineering (CSC), 2026. Sat, 01 Aug 2026 00:00:00 GMT https://ar.iub.edu.bd/handle/11348/1569 2026-08-01T00:00:00Z https://ar.iub.edu.bd/handle/11348/1568 A lightweight framework for implementing ISO/IEC 26550 in small and medium-sized software enterprises in Bangladesh Hafiz, Zinia This study explores the challenges faced by Bangladeshi software Small and Medium-sized Enterprises (SMEs) in adopting ISO/IEC 26550 for Software Process Improvement (SPI). Data collected from 12 SMEs revealed uneven implementation across key process areas, with high familiarity in domain requirements (up to 95%) but limited engagement in testing and validation practices. To address these gaps, the study proposes a lightweight PDCA (Plan–Do–Check–Act) framework tailored to the unique resource constraints of SMEs. Unlike traditional models such as CMMI or ISO/IEC 12207, the proposed approach emphasizes simplicity, cost-effectiveness, and role-based adaptability, making it more practical for SMEs in emerging economies. The framework supports scalable and gradual process improvement, enhances quality assurance, and facilitates stronger alignment with ISO/IEC 26550 in resource-limited environments. This thesis is submitted in partial fulfilment of the requirements for the degree of Master of Science (M. SC.) in Software Engineering, 2025. Sun, 14 Dec 2025 00:00:00 GMT https://ar.iub.edu.bd/handle/11348/1568 2025-12-14T00:00:00Z https://ar.iub.edu.bd/handle/11348/1567 Benchmarking CNN architectures using transfer learning for Lipomatous tumor classification: a comprehensive study Haque, Hasibul; Jamil, Fahmida This study evaluates five pre-trained CNN architectures—ResNet50, DenseNet121, EfficientNetB3, InceptionV3, and MobileNetV2—for distinguishing benign lipoma from well-differentiated liposarcoma (WDLPS) using T1-weighted MRI. Using a uniform frozen transfer-learning framework and MRI data from 115 patients in the WORC database, MobileNetV2 achieved the best overall performance, with 96.7% accuracy and 0.997 AUROC. DenseNet121 showed strong WDLPS recall and may be preferable for safety-critical screening. Although EfficientNetB3 achieved the highest WDLPS recall, its high false-positive rate and poor overall performance made it unsuitable under the tested configuration. The findings support MobileNetV2 as an effective, lightweight architecture for automated MRI-based lipoma and WDLPS classification. This thesis is submitted in partial fulfilment of the requirements for the degree of Degree of Bachelors of Computer Science and Engineering, 2026. Sat, 01 Aug 2026 00:00:00 GMT https://ar.iub.edu.bd/handle/11348/1567 2026-08-01T00:00:00Z https://ar.iub.edu.bd/handle/11348/1553 Automated segmentation of wisdom teeth (m3): an analytical study of model performance & metric variability Roy, Avijit; Roy, Souhardya; Talha, Tokey Tamim Segmentation of wisdom teeth (M3) from panoramic dental radiographs plays a critical role in dental diagnosis, treatment planning, and surgical decision-making. However, manual segmentation is time-consuming, relies heavily on expert knowledge, and is prone to inter-observer variability. Although deep learning has shown strong performance across the medical imaging domain, relatively little research has specifically addressed wisdom tooth segmentation using deep learning-based models. This study addresses that gap by conducting a comparative analysis of four semantic segmentation models: U-Net, U-Net++, DeepLabV3, and DeepLabV3+. A custom dataset of 700 panoramic dental radiographs was prepared, with pixel-level segmentation masks manually created for six semantic classes: general teeth and the four wisdom teeth defined by the FDI tooth numbering system. All models were trained and evaluated under identical conditions, including the same preprocessing, augmentation, and training setup, using a ResNet34 encoder within the PyTorch framework. Model performance was assessed using Accuracy, Precision, Recall, F1-score, Dice Similarity Coefficient (DSC), and Intersection over Union (IoU). In addition, the study examined the effects of manual versus SAM-assisted annotation and compared two dataset preparation strategies: Split → Augmentation and Augmentation → Split. Test results show that DeepLabV3 achieved the best overall performance, with the highest Dice Score (0.7530), IoU (0.6872), Precision (0.7056), and Accuracy (0.9918), while U-Net achieved the highest Recall (0.9671). Manually annotated masks consistently produced more accurate segmentation results than SAM-assisted annotations. Furthermore, applying data augmentation before splitting the dataset was found to introduce data leakage, yielding artificially inflated performance, whereas splitting the dataset before augmentation produced more reliable and realistic results. These findings suggest that the accuracy of wisdom tooth segmentation depends not only on the choice of deep learning architecture but also on annotation quality and dataset preparation strategy. This work underscores the value of a manually annotated wisdom tooth dataset, provides a comparative benchmark across four segmentation models, and offers direction for future research in automated dental image analysis. This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science (BSc) in Computer Science and Engineering (CSC), 2026. Sat, 22 Aug 2026 00:00:00 GMT https://ar.iub.edu.bd/handle/11348/1553 2026-08-22T00:00:00Z