http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/4 Wed, 15 Apr 2026 16:49:37 GMT 2026-04-15T16:49:37Z http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/7311 Effective use of data transformation Methods for machine learning applications Saifur Rahman, Dr. Mohammad; Sana, Joydeb Kumar; 1018054003; 006.31/SAN/2025 Data transformation (DT) plays a vital role in the data preprocessing phase of machine learning (ML) model training. Several DT methods are being used in ML applications. However, all the DT methods are not suitable for the same ML application. This issue has been ignored in the research community. In this thesis, we have investigated and analyzed the effectiveness, suit- ability, and applicability of DT methods in ML applications covering multiple domains along different dimensions. Focusing on the mentioned issue, we have come up with a novel DT approach which not only improves ML prediction performance in various application domains but also preserves data privacy. In the process, this research spanned several ML applications. At first, we developed customer churn prediction models in the telecommunication industry (TCI), where we investigated the impact of several DT methods. Our findings revealed that DT methods, particularly Weight-of-Evidence (WOE), significantly improved churn prediction ac- curacy. However, despite its effectiveness, we identified certain limitations of WOE. To address these issues, we developed a modified version of WOE and introduced it as adaptive Weight-of- Evidence (aWOE). The proposed method was evaluated across multiple application domains, demonstrating improvements in prediction performance, data privacy preservation, and model interpretability. These findings were validated using three publicly available datasets from three different domains and seven classification algorithms. Since aWOE is able to boost prediction performance in various domains, we employed it to improve the prediction performance in Loan Eligibility Prediction (LEP), along with other DT methods. Extensive experiments were conducted on seven publicly available datasets using eleven different classifiers. The experimental results indicate that the aWOE based LEP models achieve improved prediction performance while preserving data privacy. Furthermore, SHAP analysis revealed that aWOE prioritizes features that are more closely aligned with practical loan eligibility criteria. Following the impressive success of the proposed aWOE method in the aforementioned pre- diction tasks, this thesis turned its attention to data privacy. We propose a privacy-preserving customer churn prediction (PPCCP) framework in the cloud environment for the telecommu- nications industry (TCI). The proposed approach is a combination of Generative Adversarial Networks (GANs) and adaptive Weight-of-Evidence (aWOE). Synthetic data is generated from GANs, and aWOE is applied on the synthetic data before feeding the data to the classifica- tion algorithms. Our experiments were carried out using eight different ML classifiers on three publicly accessible datasets. The experimental results, supported by statistical tests and compar- isons with previous studies, demonstrate that the proposed GANs-aWOE framework enhances prediction performance while effectively preserving data privacy. Next, we shift our focus to the healthcare sector. We propose a distributed patient similarity computation (DPSC) for clinical decision support, leveraging aWOE in conjunction with static and time series data. Dynamic Time Warping (DTW) is employed for time series similarity, while Spark-based distributed processing is utilized to meet real-time computational demands. SHAP analysis further reveals that, when using the aWOE method, patient medical records contribute more significantly to prediction performance than demographic attributes. Overall, this thesis has developed and validated a generic framework for multiple prediction problems across several domains. The proposed methodologies, techniques, results, observa- tions, and insightful discussions are believed to have advanced the knowledge base and the current state-of-the-art. Tue, 13 May 2025 00:00:00 GMT http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/7311 2025-05-13T00:00:00Z http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/7310 An efficient CNN-based regression model for noise prediction in color images Miah, Dr. Md Jarez; Nasima Islam Bithi; 0421312032; 006.42/NAS/2025 The presence of noise in digital images remains a critical barrier to high-fidelity visual representation, compromising both perceptual quality and the accuracy of downstream image analysis. Among various noise types, Gaussian and Poisson noise stemming from electronic sensor imperfections and photon-counting variability, respectively pose significant challenges. Accurate estimation of these noise levels is essential for improving computer vision tasks such as denoising, super-resolution, segmentation, and object detection. However, existing approaches often struggle under high-noise conditions, mixed-noise scenarios, and color image contexts. To address these limitations, we introduce two deep regression models: NoiseNet and NoiseNetV2, designed with noise-specific feature extraction and attention mechanisms to enhance performance across diverse noise types and datasets. Evaluated on datasets including Flickr30k, COCO, CelebA, and DIV2K, the proposed NoiseNet model achieved state-of-the-art results. For Gaussian noise, it recorded the Mean Absolute Error (MAE) of 0.0038, a Root Mean Squared Error (RMSE) of 0.0052, and an R² score of 0.9910, outperforming the best-performing baseline DenseNet121 by margins of ~2.7× lower MAE, ~2.5× lower RMSE, and ~1.05× higher R² score. For Poisson noise, NoiseNet achieved an MAE of 0.0547, RMSE of 0.0763, and R² of 0.9922, representing improvements of ~ 4× lower MAE, ~3.7× lower RMSE, and ~1.1× higher R² score compared to the best-performing baseline ResNet50. The model also outperformed classical techniques like BM3D and Scikit-learn in both low- and high-noise scenarios. Furthermore, NoiseNetV2, an enhanced variant incorporating an attention mechanism, delivered even stronger results under mixed noise conditions. For Gaussian noise, it reached an MAE of 0.0078, R² of 0.9682, and RMSE of 0.0103; for Poisson noise, it obtained an MAE of 0.2759, R² of 0.8106, and RMSE of 0.3763, outperforming both NoiseNet and all other baseline models for mixed noise. These findings confirm the proposed models’ strong generalization and practical utility in fields such as medical imaging, autonomous driving, remote sensing, and surveillance, enabling precise noise estimation for advanced image restoration and real-time vision systems. Mon, 07 Apr 2025 00:00:00 GMT http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/7310 2025-04-07T00:00:00Z http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/7309 Quantifying pathological progression from single-cell transcriptomics data Dr. Mohammad Saifur Rahman; Samin Rahman Khan; 0422052003; 006.31/SAM/2025 The surge in single-cell datasets and reference atlases has enabled the comparison of cell states across conditions, yet a gap persists in quantifying pathological shifts from healthy cell states. To address this gap, we introduce single-cell Pathological Shift Scoring (scPSS), which provides a statistical measure for how much a “query” cell from a diseased sample has shifted away from a reference group of healthy cells. In scPSS, the distance of a cell to its k-th nearest reference cell is considered as its pathological shift score. Euclidean distances in the top n principal component space of the gene expressions are used to measure distances between cells. The distribution of shift scores of the reference cells forms a null model. This allows a p-value to be assigned to each query cell’s shift score, quantifying its statistical significance of being in the reference cell group. This makes our method both simple and statistically rigorous. The key strength scPSS is its applicability in a “semi-supervised” setting, where only healthy reference cells are known and diseased-labeled data are not provided for model training. As existing methods do not support cell-level pathological progression measurement in this setting, we adapt state-of-the-art supervised pathological prediction and contrastive models for benchmarking. Comparative evaluations against these adapted models demonstrate our method’s superiority in accuracy and efficiency. Additionally, we have also shown that the aggregation of cell-level pathological scores from scPSS can be used to predict health conditions at the individual level. The code for scPSS is available at https://github.com/SaminRK/scPSS. Sat, 21 Jun 2025 00:00:00 GMT http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/7309 2025-06-21T00:00:00Z http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/7308 Design and implementation of microwave plasma reactor for the production of silicon-nano-particle from locally available quartzite Chowdhury, Dr. Nadim; Das, Sagar Kumar; 0422062110; 623.813/DAS/2025 Plasma nanotechnology plays a crucial role in the large-scale synthesis of nanoparticles, which are widely used in various modern technological applications. Conventional microwave-based plasma reactors typically incorporate components such as circula- tor and directional couplers to protect the magnetron from reflected microwave power. However, these components significantly increase the overall system cost and complex- ity. In this study, a cost-effective alternative design for a microwave plasma reactor was proposed and analyzed using finite element simulations. The design replaces the conventional circulator with a three-decibel (3dB) waveguide bridge, which passively redirects reflected microwave energy away from the magnetron, thereby mitigating the risk of damage. During the design process, particular emphasis was given on mini- mizing microwave reflections on the magnetron side. This was achieved by optimiz- ing for a low reflection coefficient and a low Voltage Standing Wave Ratio (VSWR), ensuring minimal power loss and enhanced operational stability. An operational mi- crowave plasm reactor was successfully fabricated using locally sourced and affordable components. The functionality of the system was demonstrated by synthesizing fumed silica nanoparticles, which were subsequently characterized using Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS). However, sili- con nanoparticles production from quartz is also possible using the plasma reactor. But due to some complexity this work has been suggested as future prospect for further study and analysis. This alternative reactor design provides a practical and economical solution for microwave plasma generation, eliminating the need for expensive compo- nents while maintaining effective performance. It holds promising potential for a wide range of applications, including nanoparticle synthesis, chemical processing, biomass conversion, and waste treatment. Wed, 28 May 2025 00:00:00 GMT http://lib.buet.ac.bd;localhosthttp://:8080/xmlui/handle/123456789/7308 2025-05-28T00:00:00Z