https://ar.iub.edu.bd/handle/11348/624 By CSE Department 2026-06-27T12:05:19Z https://ar.iub.edu.bd/handle/11348/1263 BookPlace: A Modular & Scalable Multi-Vendor eCommerce System with Advanced Order, Inventory & Analytics Management Sayam, Muntakim Kadir On contemporary e-commerce platforms, the discovery of products, management of the cart, payment, and delivery are usually handled, whereas on professional home-service platforms, the focus is on technician booking, scheduling, and job completion. Customers who purchase appliances or products that consume services extensively typically require simultaneous workflows. A customer can buy an air conditioner, washing machine, smart gadget, or home appliance online, but must call a separate service provider, make a phone call, or use a third-party platform to book an installation, repair, or maintenance. This break creates conflicts with clients, ambiguity among suppliers, reduced visibility among technicians and issues with after-sales responsibility. pdf with plagiarism status 2026-06-01T00:00:00Z https://ar.iub.edu.bd/handle/11348/1254 Deep Learning on Dental RGB and OPG Images for Public Health and Clinical Decision Support Smaron, J. M. Sadik-Ul Islam; Alam, Mustaqueem; Islam, Mohammed Tashfiq The rapidly increasing rates of unrecognized dental issues, along with the ongoing barriers to timely dental health monitoring, have raised the need for the creation of Machine Learning (ML) based dental systems that are accurate and can be used by both patients and dental professionals. In an effort to bridge the circuit, a combined Deep Learning (DL) architecture analysis is implemented, which takes advantage of two imaging sources that are very different yet complementary: Intraoral Red-Green-Blue (RGB) images for the monitoring of dental health in general purposes, and panoramic Orthopantomogram (OPG) radiographs for the support of clinical decisions. The approach is composed of two specifically designed frameworks that include an RGB-based classification system that also includes the detection of early cases of dental caries, which is for teeth health monitoring for public use, and an OPG-based system for conceiving and supporting dentists in radiograph interpreting as a supporting tool in case of complex patterns in OPG radiographs. 2025-12-01T00:00:00Z https://ar.iub.edu.bd/handle/11348/1253 BILI: A Domain-Specific Reception Assistant Robot for Bilingual and Regional Language Interaction in Bangladesh Chowdhury, Md. Hana Sultan; Aiman, Umme This project addresses digital inequality and linguistic exclusion in Bangladeshi academic environments by developing a Bilingual University Assistant Robot (BILI) and a supporting speech dataset named BRADS. Although Bangla is one of the most widely spoken languages globally, current digital assistants primarily support only standard Bangla, marginalizing millions of regional dialect speakers in educational institutions. To bridge this gap, we introduce BRADS—a curated audio dataset containing 2,439 recordings of 298 frequently used university-related words, including 233 regional and 65 standard terms, collected from 85 native speakers across all eight divisions of Bangladesh. BILI leverages this dataset to enable real-time dialect recognition using a CNN–BiLSTM hybrid model. The robot features natural language processing, MFCCbased speech feature extraction, and a dialect-tuned text-to-speech (TTS) module, allowing it to interact fluently in both Bangla (standard and regional dialects) and English. Optimized for low-latency edge deployment on the NVIDIA Jetson Xavier NX, BILI offers responsive, multilingual support in real time. It assists students and visitors with educational queries, navigation across campus buildings using Dijkstra’s algorithm, and interactive voice-visual responses through a smart interface. Field deployment at Independent University, Bangladesh (IUB) showed over 1,900 successful interactions in Bangla, English, and regional dialects, achieving high precision and user engagement. Future developments include expanding BRADS to support conversational and emotional speech and integrating region-specific voice synthesis for even more personalized experiences. BILI demonstrates a scalable solution for inclusive, voice-enabled university services in low-resource and linguistically diverse academic settings. Dissertation submitted in partial fulfillment for the degree of Bachelor of Science in Computer Science & Engineering 2025-08-20T00:00:00Z https://ar.iub.edu.bd/handle/11348/1251 Semi-Supervised Approach: ClusterFormer for Semantic Segmentation in Remote Sensing Applications Siddiqui, Choudhury Ben Yamin Applications of remote sensing have been transformed by deep learning, particularly in semantic segmentation for mapping land use and land cover (LULC). Conventional supervised algorithms, on the other hand, spend a lot of money and time since they require a large amount of labeled data. For semantic segmentation in remote sensing applications, this paper investigates a semi-supervised learning paradigm that makes use of ClusterFormer, a transformer-based architecture intended for effective clustering and attention mechanisms. The suggested method attempts to improve segmentation accuracy while lowering reliance on ground-truth labels by merging a sizable corpus of unlabeled satellite data with a small number of annotated samples. Benchmark LULC datasets and custom high-resolution satellite images are used for experiments, while F1-score and Intersection over Union (IoU) are used as assessment measures. Results demonstrate that the semi-supervised ClusterFormer performs similarly to fully supervised baselines under limited-label conditions, highlighting its potential for scalable remote sensing analysis. A Dissertation submitted in partial fulfillment for the Degree of Bachelor of Science in Computer Science and Engineering 2025-08-01T00:00:00Z