https://ar.iub.edu.bd/handle/11348/8 SETS 2026-09-10T15:56:01Z https://ar.iub.edu.bd/handle/11348/1570 Design and development of a low-cost ammonia monitoring system for water quality assessment Khan, Md Iftie Islam; Islam, Md Rakibol; Sarker, Dipto; Ahmed, Tanvir This thesis presents a low-cost ammonia detection and monitoring system for freshwater fish farming. The system uses an MQ-137 gas sensor, Arduino Uno, heater, water pump, thermocouple, and LCD display to measure ammonia concentration in water samples. The sensor was calibrated using known ammonia concentrations and a fourth-order polynomial model to convert sensor readings into ppm values. The system performed effectively within the 0–8 ppm range and was validated against a commercial ammonia test kit. Despite minor errors caused by sensor warm-up time and vapour leakage, the system offers an affordable and practical solution for aquaculture, environmental monitoring, and educational applications, with potential for future IoT-based remote monitoring. This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science (B. Sc.) in Electrical and Electronic Engineering, 2026. 2026-08-01T00:00:00Z 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. 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. 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. 2026-08-01T00:00:00Z