Browsing by Author "Akter, Morium"

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Analysis of Skin Cancer feature pattern using GNN incorporating a Novel segmentation method.
(Daffodil International University, 2024-07-13) Akter, Morium
This study presents a comprehensive approach for the classification of skin lesions into benign and malignant categories using a combination of k-means clustering segmentation and advanced computational models. The methodology begins with the segmentation of lesion images through k-means clustering to extract relevant regions, followed by the computation of various features, including SIFT key points, WH-ratio, ECL-ratio, Harris corner, circularity, solidity, asymmetry score, color variation, and compactness index. Feature importance is determined through ranking and ANOVA tests. For model selection, both feature-based (GNN, GAT, 1D CNN) and image- based (VGG16, VGG19, Inception V3, MobileNetV1, MobileNetV2) models are evaluated, with the Graph Neural Network (GNN) emerging as the best model. The GNN model achieved a test accuracy of 98.62%. An ablation study is conducted to assess the contributions of individual components, and additional experimental analysis explores different thresholds in graph generation. The results are thoroughly analyzed using evaluation metrics, loss and AUC curves, statistical analysis, linear regression analysis, and 5-fold cross-validation. The proposed methodology demonstrates superior accuracy and robustness in the classification of skin lesions, offering a powerful tool for early detection and treatment planning.
Bangla Handwritten Digit Recognition Using Deep Convolutional Neural Network
(ACM International Conference Proceeding Series, 2020) Basri, Rabeya; Haque, Mohammad Reduanul; Akter, Morium; Uddin, Mohammad Shorif
Handwritten Bangla digit recognition is one of the most challenging computer vision problems due to its diverse shapes and writing style. Recently deep learning based convolutional neural network known as deep CNN finds wide-spread applications in recognizing different objects due to its high accuracy. This paper investigates the performance of some state-of-the-art deep CNN techniques for the recognition of handwritten digits. It considers four deep CNN architectures, such as AlexNet, MobileNet, GoogLeNet (Inception V3), and CapsuleNet models. These four deep CNNs have been experimented on a large, unbiased and highly augmented standard dataset, NumtaDB and confirmed that the AlexNet showed the best performance on the basis of accuracy and computation time.
Design & Development of a Microcontroller Based Electronic Queue Control
(East West University, 12/21/2016) Akter, Morium; Khan, Naheed Md. Tousif
In modern days, we must use various high-tech electronic devices and equipment to get our jobs done and make the life easier. The purpose of this project is to design an Electronic Queue Control System. Here, we have used 4-bit parallel registers (74LS175), BCD to 7-segment display decoder driver (74LS47) and 7-segment display unit and an Arduino Uno board. Four I/O pins of Arduino are arranging in the multiplexed form to send data to the display units. Different Clock pulses for 74LS175 registers have been used to save the input data in a particular register. 4-bit parallel register save the input data from Arduino and send to the input of BCD to 7-segment display decoder driver. Getting the output, BCD to 7-segment decoder driver display the binary number as (0-9) in the 7-segment displays. This project will show how three 7-segment displays can be used for counter numbers (0-9) (where services to the customers will be provided) and serial numbers (0- 99). The developed system can be used like a Queue Control system of any Supermarket checkouts, Banks, Customer Care, Airport security etc. The system has been designed and implemented practically. It is found that the system works perfectly.
Image-based automated haze removal using dark channel prior
(IEEE, 2018-02-12) Uddin, Mohammad Shorif; Gautam, Bishal; Sarker, Aditi; Akter, Morium; Haque, Mohammad Reduanul
Haze, fog, rain usually hampered the performance of vision systems. So, removal of haze appearance in a scene should be the first priority for clear vision. Previously a dehazing mechanism was developed based on dark channel prior which cannot automatically set the patch size and the sky-region's transmission value. The current paper tries to fill this gap to automate these values. Experimentation has been conducted to find the performance on the basis of subjective as well as objective metrics. We have obtained satisfactory results compared to the existing techniques.
Logic Based Verification of Software Product Line Feature Model
(East West University, 12/18/2014) Bari, Mirza Faisal Md. Abdul; Akter, Morium
Feature diagrams are widely used to model product line variant . Formal Verification of variant requirements has gained much interest in the software product line(SPL) community . However, there is a lack of precisely defined formal notation for representing and verifying such models. This report presents an approach to modeling and analyzing SPL variant feature by Logic Based and also First order logic. The logical representation provides a precise and rigorous formal interpretation of the feature diagrams. Logical expressions can be built by modeling variants and their dependencies by using propositional connectives. These expressions can then be validated by any suitable verification tool such as Alloy. A case study of two Feature Model (GPL & Hall Booking System) variant feature model is presented to illustrate the analysis and verification process.
Machine Vision Based Papaya Disease Recognition
(Journal of King Saud University - Computer and Information Sciences, Elsevier, 2020-03) Habib, Md. Tarek; Majumder, Anup; Jakaria, A.Z.M.; Akter, Morium; Uddin, Mohammad Shorif; Ahmed, Farruk
Over the years little research has been performed for vision-based papaya disease recognition system in order to help distant farmers, most of whom require proper support for cultivation. Due to advancement of vision-based technology we find a good solution to this problem. Papaya disease recognition mainly involves two challenging problems: one is disease detection and another is disease classification. Considering this scenario, here we present an online machine vision-based agro-medical expert system that processes an image captured through mobile or handheld device and determines the diseases in order to help distant farmers to address the problem. Some experiments are performed to show the utility of the proposed expert system. First, we propose a set of features from the view point of distinguishing attributes. K-means clustering algorithm is used in order to segment out the disease-attacked region from the captured image and then required features are extracted to classify the diseases with the help of support vector machine. More than 90% classification accuracy has been achieved, which appears to be good as well as promising by comparing performances obtained with recently reported relevant works.
Machine vision based papaya disease recognition
(Elsevier B.V., 2018-06-18) Habib, Md. Tarek; Majumder, Anup; Jakaria, A.Z.M.; Akter, Morium; Uddin, Mohammad Shorif; Ahmed, Farruk
Over the years little research has been performed for vision-based papaya disease recognition system in order to help distant farmers, most of whom require proper support for cultivation. Due to advancement of vision-based technology we find a good solution to this problem. Papaya disease recognition mainly involves two challenging problems: one is disease detection and another is disease classification. Considering this scenario, here we present an online machine vision-based agro-medical expert system that processes an image captured through mobile or handheld device and determines the diseases in order to help distant farmers to address the problem. Some experiments are performed to show the utility of the proposed expert system. First, we propose a set of features from the view point of distinguishing attributes. K-means clustering algorithm is used in order to segment out the disease-attacked region from the captured image and then required features are extracted to classify the diseases with the help of support vector machine. More than 90% classification accuracy has been achieved, which appears to be good as well as promising by comparing performances obtained with recently reported relevant works.