Funded by North South University under project ID CTRG-22-RAD-12, this project aims to revolutionize radiotherapy planning through deep learning-driven whole-body auto-contouring. Under the leadership of Dr. Mohammad Monir Uddin, the research develops advanced neural network models to automatically segment organs and tissues in medical imaging data, streamlining the radiotherapy planning process. The system enhances precision in delineating target volumes and critical structures, reducing manual effort and improving treatment accuracy. This initiative seeks to optimize radiotherapy workflows, minimize human error, and enhance patient safety and treatment efficacy.

This project, funded by North South University under project ID CTRG-22-LUNG-08, focuses on developing advanced methods for lung cancer detection and automated report generation from CT scans. Led by Dr. Mohammad Monir Uddin, the initiative employs state-of-the-art deep learning techniques to identify and classify lung nodules with high precision. The system aims to segment suspicious regions in CT images, enabling early detection of lung cancer. Additionally, it generates comprehensive diagnostic reports to assist clinicians in treatment planning. By integrating machine learning with medical imaging, this project seeks to improve diagnostic accuracy, reduce false positives, and enhance patient outcomes in lung cancer management.

This project, funded by North South University under project ID CTRG-21-SEPS-15, aims to advance breast cancer diagnosis through the development of cutting-edge techniques for precision segmentation and comprehensive report generation from mammographic images. Led by Dr. Mohammad Monir Uddin, the project leverages advanced image processing and machine learning methodologies to enhance the accuracy and efficiency of detecting and characterizing breast abnormalities in mammograms. The primary objective is to create a robust system capable of precise segmentation of regions of interest in mammographic images, enabling early and accurate identification of potential cancerous lesions. Additionally, the project focuses on generating detailed diagnostic reports that provide actionable insights for medical professionals, thereby improving clinical decision-making and patient outcomes. By integrating computational intelligence with medical imaging, this initiative seeks to address critical challenges in breast cancer diagnostics, contributing to reduced mortality rates and enhanced healthcare delivery.

This project involved the development of a comprehensive, cloud-based AI-powered learning management platform designed for educational and medical training environments. The system supports multiple user roles, including teachers, students, and administrators, each with dedicated dashboards and role-specific functionalities. The platform was architected to provide all core features of a modern learning management system comparable to CANVAS, with additional advanced AI-driven capabilities.

A centralized cloud library allows users to upload educational materials such as books, lecture notes, presentations, and datasets. Uploaded resources can be managed as public or private, enabling controlled sharing across courses, institutions, or individual users. The library serves as a shared knowledge base that integrates directly with teaching, assessment, and learning workflows.

For teachers, the platform provides AI-assisted content creation and assessment tools. Educators can automatically generate exam questions and quizzes by uploading textbooks, lecture materials, or selecting content from the cloud library. The system supports automated grading and evaluation, significantly reducing manual workload while ensuring consistency and scalability in assessments.

Students are provided with an intelligent exam preparation environment that enables personalized learning. They can generate practice questions, revise course materials, and interact with AI-powered study tools. For medical and image-based learning, the platform includes annotation tools for medical images, with AI-assisted automated segmentation that can be invoked on demand to support deeper understanding and exam preparation.

An integrated large language model (LLM) enhances the experience for all user roles by providing contextual assistance, explanations, content generation, and academic support across the platform. Administrators have access to a centralized dashboard for managing users, courses, content moderation, system performance, and compliance requirements.

Overall, the project delivers a scalable, AI-first learning management system that combines cloud-based content management, automated assessment, intelligent tutoring, and medical image analysis into a unified, production-ready platform.

Over the last few decades, enormous research has been done on STSO, which has mainly emphasized the physical model development rather than the data model generation. However, the data model is crucial for numerical analysis since it reflects all the characteristics of the physical model and is easy to handle for optimization. Due to redundant data, the smooth computation can never be performed on the unoptimized mathematical model. Therefore, ROM techniques can be applicable to turn the unoptimized data model optimized by reducing the useless dimension of the data model. As a result, the physical model generated from the optimized data model becomes smooth to analyze. Dealing with STSO using ROM techniques is a new concept that is excepted to complete flawlessly in this project. The possible significant outcomes of this research are as follows:

• The physical model of the PV panel will be constructed, and data on the solar radiation on the PV panel will be extracted to generate a mathematical model.
• By linearization procedure, the linear state-space formation will be generated based on the input-output relations, which will mainly define the properties of the physical model.
• The generated mathematical models' ROM will be created to truncate the redundant design meshes to boost up the entire simulation procedures.
• Hopefully, the optimized PV panel model will be found in this project to improve the physical efficiency in terms of thermal state and energy storage capacity and minimize the loss of energy due to heat.
• The project will help acquire knowledge on STSO in terms of Control theory, Scientific computing, and Mathematical Algorithms.
• Several scientific papers will be published in renowned journals highlighting the outcomes of this project.

Multiple Sclerosis (MS) is a chronic inflammation of the central nervous system that causes the destruction of the protective sheath of nerve cells, resulting in degraded masses known as plaques. This cellular damage to the brain or spinal cord can lead to various side effects such as impaired vision, loss of balance, and learning disorders.

Currently, the diagnosis and progress of MS are determined visually by comparing images of the brain at different periods. This process is time-consuming and challenging for specialists due to the small size of the lesions, the number of lesions, and their spatial distribution, as well as the different severities of lesion progression (white, gray, and black holes). Diagnosis of MS involves examining the clinical symptoms of patients suspected of having MS and using MRI images of the brain. These images show the location and number of lesions in the white matter area of the brain, which are essential for diagnosis and follow-up.

However, accurately diagnosing MS-related lesions can be challenging because various diseases can cause lesions in the brain. The current standard of manually segmenting images to specify the size and location of lesions is time-consuming and less accurate than computer diagnostics, leading to non-identical diagnoses by professionals. Moreover, confuent lesions, which overlap and cannot be easily separated, further complicate manual segmentation.

To address these challenges, researchers have developed a deep learning-based model using the U-Net deep neural network, in which the wavelet pooling layer replaces the max pooling layer. Wavelets allow for localization in scale (i.e., frequency) and space, which can be used to analyze local, spatial transients in the data such as edges or surfaces. This model performs better segmentation of lesions of different sizes by enabling the reduction of image size and the ability to highlight the specificity of MS lesions in MRI images. The use of deep learning in medical image information analysis, such as noise reduction, segmentation, and classification, has had good results in recent years.

Prostate cancer segmentation and detection involve the use of medical imaging techniques and computer algorithms to identify and locate cancerous regions within the prostate gland. The process can help physicians to accurately diagnose and stage prostate cancer, which can help guide treatment decisions.

The main steps involved in prostate cancer segmentation and detection include image pre-processing, image segmentation, feature extraction, and classification. Image pre-processing involves cleaning up the medical images to improve the accuracy of the segmentation and detection algorithms. Image segmentation involves identifying and isolating the regions of interest within the medical images. Feature extraction involves extracting relevant features from these regions, such as texture, shape, and size, which can be used to differentiate between cancerous and non-cancerous tissue. Classification involves using machine learning algorithms to classify the regions of interest as cancerous or non-cancerous.

Several medical imaging techniques are commonly used for prostate cancer segmentation and detection, including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and ultrasound. Deep learning techniques such as convolutional neural networks (CNNs) have also been used to improve the accuracy of prostate cancer detection and segmentation.

Prostate cancer segmentation and detection can help physicians to make informed decisions regarding treatment options, including surgery, radiation therapy, and chemotherapy. It can also help monitor the progression of prostate cancer over time and identify any changes in the cancerous regions that may require additional treatment or monitoring. Overall, prostate cancer segmentation and detection can improve the accuracy of prostate cancer diagnosis and staging, leading to improved patient outcomes.

Over the last few decades, enormous researches have been done on WSO which has mainly emphasized the physical model development rather than the generation of the data model. However, the data model is crucial for numerical analysis since it reflects all the characteristics of the physical model and easy to handle for optimization. Due to the presence of redundant data, the smooth computation can never be performed on the unoptimized mathematical model. Therefore, ROM techniques can be applicable to turn the unoptimized data model optimized by reducing the useless dimension from the data model. As a result, the physical model generated from the optimized data model becomes smooth to analyze and gives an optimal geometric shape easily. Dealing with WSO using ROM techniques is a new concept that is excepted to complete flawlessly in this project.

In this research work, we have developed mathematical algorithms and software for the model reduction of large-scale dynamical systems. The proposed algorithms and software will be used for academic research and industrial applications. To show the efficiency and capability of these algorithms we have applied them to a set of real-world data obtained from a research institute and generated by ourselves. The results obtained in this work were presented at national and international conferences. We also have published several Journal and conference papers using the models and numerical results obtained from this project. Besides, two M.Sc. thesis works have been completed by the funding of this project.

Publication from this research:

1. Md. Tanzim Hossain, Azizul Haque, Kife I. Bin Iqbal, and M. Monir Uddin, Airfoil Generation using GANs and Optimization using Model Order Reduction. (In preparation)
2. Kife I. Bin Iqbal, Md. Tanzim Hossain, and M. Monir Uddin, Generation of the Mathematical Model to Analyze the Dynamical Behavior of the Turbulence Flow Around the Airfoils. (In preparation)
3. A. Haque, T. Hossain, M. Murshed and K. I. B. Iqbal and M. M. Uddin, Estimating aerodynamic data via supervised learning, 25th International Conference on Computer and Information Technology (ICCIT), IEEE, Dhaka, 2020.

Imagine one day, your breathing became consistently labored and shallow. Months later you were finally diagnosed with pulmonary fibrosis, a progressive disease that naturally gets worse over time with no known cause and no known cure, created by scarring of the lungs. If that happened to you, you would want to know your prognosis. That’s where a troubling disease becomes frightening for the patient. Outcomes can range from long-term stability to rapid deterioration, Natural history of IPF is unknown and the prediction of disease progression at the time of diagnosis is notoriously difficult and doctors aren’t easily able to tell where an individual may fall on that spectrum. Data science, may be able to aid in this prediction. If successful, patients and their families would better understand their prognosis when they are first diagnosed with this incurable lung disease. Improved severity detection would also positively impact treatment trial design and accelerate the clinical development of novel treatments.

Lung function is assessed based on output from a spirometer, which measures the forced vital capacity (FVC), i.e. the volume of air exhaled. Our aim is to predict a patient’s severity of decline in lung function based on a CT scan of their lungs, metadata, and baseline FVC as input. We want to predict the final three FVC measurements for each patient, as well as a confidence value in our prediction.

In our work we have shown how we can use deep learning technique to predict the idiopathic pulmonary fibrosis progression. This type of prediction analysis, if applied effectively in medical sectors, can help patients by analysing their lungs condition from Ct scan and the corresponding other information so that treatment can be started as early as possible. Early treatment can improve the survival rate of a patient. Thus,deep learning can help medical practitioners to understand their prognosis in a better way when they are first diagnosed with idiopathic pulmonary fibrosis. Hence, deep learning algorithms are adding significant value to the healthcare industry as well as to the society aiming towards a healthy and normal social lifestyle.

Model Order Reduction (MOR) is one of the indispensable techniques to minimize the computational complexity and to maximize the efficiency of the numerical experiment during the simulation of the practical models of different engineering and technological aspects. As a result, the usage of MOR algorithms is rapidly increasing day after day in different branches of scientific analysis. Hence, the main goal of this research project, which is to develop robust algorithms and software based on different effective approaches of the MOR, will become the subject of application in the diverse fields of futuristic research. The possible significant outcomes of this research are as follows.

• The algorithms and software developed from this research would be interesting and rewarding materials for further research and development in both academics and industries.
• The algorithms can be useful in Industries for the controller design, optimization and simulation of large-scale mathematical models.
• The generated data models would be used in versatile scientific research, especially in data science, where large-scale data sets are necessary for analysis.
• With the results of this project besides several journal papers, we may also publish a book.
• The researches will be benefited tremendously. The project will help them to acquire knowledge in Control theory, Optimizations, Scientific computing, Mathematical Algorithms and Software. They can see how to apply the Mathematical knowledge in the real life applications. Moreover, if necessary the obtained results may help them to obtain higher academic degrees.

We have planed to complete the project as the following steps

• 1st year: Literature Review, understanding the problems and developing some fundamental algorithms and software.
• 2nd year: Developing algorithms and software for the model reduction of model reduction of large-scale descriptor systems, some numerical experiments and pressing in conferences.
• 3rd year: Numerical experiments with the rigorous data in a high performance computer lab and preparing and submitting papers for publications.

According to our planes the achievement discussed above is up to mark. We have already completed most of the tasks we noted. Few of the research outcomes are under processing for publication. It is expected that the entire goals of this project will be completed within the shortest possible time.