Browsing by Author "Maowa, Jannatul"

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A Deep Learning Approach For Precise Classification Of Nasal Polyps: Distinguishing Antrochoanal Polyps And Ethmoidal Polyps
(Daffodil International University, 2024-07-13) Maowa, Jannatul; Shahriar, Md.Shihab
Nasal polyps are benign growths that occur within the sinuses or nasal passages. Because of their interconnected characteristics and diverse appearance, they present significant challenges in terms of diagnosis and treatment. For these polyps to be effectively managed clinically, they must be accurately classified. However, manual examination-based traditional approaches are complicated and prone to error. In order to create an automated system that accurately classifies nasal polyps and can differentiate between antrochoanal polyps (AP) and ethmoidal polyps (EP), this work makes use of progressive deep learning techniques. Several advanced convolutional neural networks (CNNs) are used in the study, including ResNet-50, VGG16, PolyScanCNN (a customised CNN model), and EfficientNetB0. A large dataset of nasal MRI images, which underwent intensive preprocessing to improve picture quality and augment the data, served as the training set for these models. The models were assessed and compared using performance indicators such F1-score, recall, accuracy, and precision. The ResNet-50 model achieved a 96% accuracy rate, demonstrating its effectiveness in identifying nasal polyps. The study shows how well these models work in identifying nasal polyps, with ResNet-50 outperforming the other models in the majority of evaluation measures. Gradio was used to create an intuitive web-based interface that allows real-time nasal polyp picture categorization and visualization, hence promoting clinical adoption. This research highlights how deep learning can improve otolaryngology diagnostic efficiency and accuracy. It seeks to assist physicians in making wise decisions by offering a trustworthy tool for the classification of nasal polyps, ultimately leading to better patient outcomes. The results indicate that the field of medical image analysis can be greatly advanced by implementing advanced AI-driven solutions.
A New Algorithm for Solution of System of Linear Equations
(Military Institute of Science and Technology, 2012-12) Maowa, Jannatul; Kabir, Sraboni; Khosru, Ibn Md. Abu Saleh
Systems of linear equations are used in a variety of fields. The canonical problem of solving a system of linear equations arises in numerous contexts in information theory, communication theory, and related fields. This thesis is aimed at analyzing the available methods for solving a system of linear equations of the form n x n. Using a couple of iterative and/or direct methods, implement a program for these methods that could be run for different dimension size n of system of linear equation . At the end, a graph can be plotted with time taken for execution of a method considered V/s dimension size n. In this contribution, we develop a solution that does not involve direct matrix inversion. The iterative nature of our approach allows for a distributed message-passing implementation of the solution algorithm. We present test results which show that our solver achieves good results, both in terms of numerical accuracy as well as computing time. Furthermore, even very large systems (n 1000) can be solved given a cluster with sufficient resources. We also address some properties of the algorithm, including convergence, exactness, its complexity order and relation to classical solution methods.
IOT Based Switching and Data Observation for Home Appliance
(East West University, 8/27/2017) Maowa, Jannatul; Hasan, Md. Mehedi
The Internet of things (IoT) is the inter-networking of physical devices, vehicles, buildings, and other items embedded with electronics, software, sensors, and network connectivity which enable these objects to collect and exchange data. In this project, IoT based switching and data observation for home appliances as the topic since every device can be controlled from any position in the presence of internet. Again, if anyone wants to observe the data of the devices related to activation of every instance then s/he can also use this integrated device. In this project ESP8086 driver is used as the controller of the whole device as control board, magnetic relay as switch, and ESP Wi-Fi module for interfacing device with Wi-Fi. A software named as thinger.io is also developed for the betterment and ease of communication with cloud server and ESP driver module. By this software anyone can control the device and also observe data of each and every moment, if s/he away from room. It can also show us the rate of darkness as well as temperature in the room through LDR (Light Dependent Resistor) & Thirstier, whose functionality rely on light intensity & temperature respectively. The emergency switch on/off can be performed in that case. The goal of this project is to provide a low-cost and flexible solution to control and monitor home appliances. The extension cannot be completed due to the limitation of resources& enough time.