http://dspace.uiu.ac.bd/handle/52243/3 2026-04-13T14:44:36Z http://dspace.uiu.ac.bd/handle/52243/3468 Securing E-Voting in Bangladesh: A Blockchain Based System Jahan, Nusrat The electronic voting process brings a technological advancement to ballot casting. It blends tradition with technology. The system presents digital capabilities which guarantee both improved security and efficiency in operations. Previous e-voting implementations frequently experience problems at present. Democratic systems receive criticism because of their virtual weaknesses. The system permits security breaches while displaying minimal transparency while weakening vote privacy. This research investigates the development of blockchain-enabled e-voting (BEV) as a solution for resolving the existing problems within voting systems. The project development targets improvements in election security together with privacy systems and enhanced reliability features. Our system deploys blockchain essential attributes which include decentralization, transparency and immutability. Research determines how BEV functions as an election technology in Bangladesh. The report takes a confident stance to compare Bangladesh’s digital voting system with the digital voting pioneers of Estonia and Switzerland. The research proves blockchain uses security protocols to preventVote alteration and duplication events. Blockchain operates as an effective system to prevent accidental modifications in addition to unauthorized alterations. The system helps authorize voter identification. At the same time we fully recognize the critical need for actual field tests regarding this matter. Bangladesh needs to address its infrastructure challenges along with raising standards of voter privacy in its one-of-a-kind geographic setting. The research establishes that blockchain technology establishes active and transparent digital election procedures which provide security. The importance of future examination becomes crucial to solve both technical problems and regulatory requirements with blockchain technology. These findings are meant to serve as practical guidance for Bangladeshi policy makers together with other stakeholders. The researchers strive to enhance blockchain election functions and develop secure trusted electronic voting infrastructure. 2026-01-24T00:00:00Z http://dspace.uiu.ac.bd/handle/52243/3408 A Secure Spectrum Aware MAC layer protocol for Cognitive Radio Wireless Sensor Network. Hossain, Md Shamiun Persistent Secure Spectrum Aware MAC layer, scalability and load balancing are important requirements for numerous ad-hoc sensor network. Secure Clustering sensor nodes is an effective technique for achieving these goals. In this work, we present a secure, spectrum-aware cross-layer MAC protocol designed for Cognitive Radio Ad Hoc Networks (CRAHN), where cluster formation is defined maximum edge biclique problem, enhance the security of the cluster formation process and ensure both a stable number of common channels and robustness to varying spectrum availability. The clustering process concludes in O(1) of iterations, independent of the network’s structure or scale. By carefully choosing the secondary clustering parameter, the process can significantly reduce the hassle of re-clustering. This thoughtful selection ensures that the workload is evenly spread across the cluster heads, preventing any one cluster from becoming overloaded, and keeping the network running smoothly and efficiently. Dynamic approach maintains a stable network structure despite node mobility. These protocols focus on secure and efficient spectrum sharing among nodes to enhance network performance. It also achieves fairly uniform cluster head distribution across the network. The cluster-based architecture is designed to be highly flexible, allowing it to build and adjust itself as needed. This dynamic nature is supported by two key operations: Node-Move-In and Node-Move-Out. These operations enable the network to easily integrate new nodes and remove existing ones, ensuring that the system can adapt to changes and maintain optimal performance without manual intervention. A pseudocode analysis focused on enhancing the security of the cluster formation process is also applied in two topology management operations. Overall, the time complexity for the Node-Move-In and Node-Move-Out algorithms is O(n), where n represents the number of members in the cluster. This means that the algorithms handle a number of operations proportional to the number of nodes involved, making them efficient and scalable as the network grows. 2026-03-31T00:00:00Z http://dspace.uiu.ac.bd/handle/52243/3393 A Hybrid Approach to Bangla Regional Text Classification Using BERT Ensemble and Region-Specific Lexical Oversampling Sultana, Babe Regional text analysis reflects the lived realities of diverse communities by capturing the linguistic richness and diversity present in various dialects. It bridges the gap between everyday regional usage and standardized language forms, thereby enhancing the inclusivity of language technologies. In this paper, we focus on five regional dialects in Bangladesh, namely Chittagong, Sylhet, Noakhali, Barishal, and Rangpur, using a dataset of 4,218 text samples. The dataset is validated by five regional experts and categorized into three tiers based on an assigned agreement criterion. Tier 1 represents a strictly filtered, high-confidence subset and is used primarily for evaluation. A set of region-specific special words, which belong exclusively to their respective regions and are validated by domain experts, is introduced. These words are used in a linguistically informed oversampling technique to balance the dataset in both experiments. In the first experiment, we demonstrate the effectiveness of the tiered dataset structure, where Tier 2 and Tier 3 (mediumand low-confidence subsets) are used for training, and Tier 1 (high-quality subset) is used for testing. In this setting, BanglaBERT achieves the best individual performance with 67.45% accuracy and a weighted F1-score of 67.62%. In the second experiment, we focus exclusively on the Tier 1 dataset, applying a wide range of machine learning and deep learning models to assess their effectiveness. The key contribution is a heterogeneous deep ensemble technique that combines three BERT models, BanglaBERT, BUETBERT, and DistilBERT, achieving an accuracy of 85.17% and a weighted F1-score of 84.84% on the Tier 1 dataset. CSE 2026-01-12T00:00:00Z http://dspace.uiu.ac.bd/handle/52243/3392 An Explainable Ensemble Convolutional Neural Network for Early Lung Cancer Prediction with Web Application Noor, Kazi Rifah Lung Cancer is one of the deadliest forms of cancer significantly contributing to the rising mortality rates globally. The high mortality rate associated with lung cancer can largely be attributed to its late detection, as symptoms often do not appear until the disease has reached advanced stages. Early detection of anomalies in medical imaging, particularly at the initial stages, is crucial for advancing both quantitative image analysis and patient care. In this context, our research introduces a fully automated web application to predict lung cancer early on CT scan images. This application leverages the power of a weighted average ensemble-based deep learning framework that combines multiple neural network architectures to enhance the reliability of automated classification. The work highlights the need for interpretability in clinical decision support systems, going beyond classification. Our proposed approach is organized into three independent phases. First, we performed image augmentation as part of the preprocessing stage analyzing the IQ-OTH lung cancer dataset which implies that the model is trained on diverse and enriched input data. The system incorporates ResNet50, VGG16, and a custom CNN, all of which provide complementary feature-learning capabilities that improve overall predictive dependability. The ensemble model clearly outperformed the individual networks, achieving an accuracy of 92.28% on the IQ-OTH lung cancer dataset. The visual indications and regions that most influence our model’s decisions are highlighted using Explainable Artificial Intelligence techniques, particularly LIME and SHAP. By making the model’s inner workings more understandable, these explanations hope to boost medical practitioners’ confidence. Finally, the ensemble model was integrated into a user-friendly web application allowing users to upload CT scan images, which are then analyzed to classify the images into normal, benign, or malignant categories. Furthermore, our web application gives confidence levels for each prediction increasing the credibility of its results. Our system provides medical practitioners with a smooth and effective tool by integrating the precision of an ensemble model with the flexibility of a web application. CSE UIU 2026-01-12T00:00:00Z