Hey there,
Welcome to my portfolio! I hold a Bachelor’s degree in Computer Science and Engineering from North South University, Bangladesh, and a Master’s in Information Technology (majoring in Computer Science) from Queensland University of Technology, Australia.
My passions span both Data Science and Software Development, and I’m currently seeking exciting opportunities in Software Engineering here in Australia.
At the moment, I’m building a Secure Notes app for the App Store using React Native — a project that combines my love for creating practical tools with clean, user-friendly design. I’m also proud to have four research publications, presented at international conferences including ICCCM 2020, ICCCI 2020, IEEE IS 2020, and ACAI 2022.
Beyond work and research, I’m a lifelong Arsenal supporter. In my free time, you’ll often find me watching football, managing my Fantasy Premier League team, enjoying anime, or diving into the occasional video game.
– Sayeed Md. Shaiban
🚀 Project Spotlights
Secure Notes App
A cross-platform React Native app that enables users to securely create, store, and manage private notes with biometric authentication. Built with a strong focus on privacy and encryption, this project demonstrates expertise in mobile app development, state management, and modern UI design.
🔗 Coming Soon
📱 DOST: Mobile Approval Application
DOST is a mobile approval application that streamlines approval workflows for mobile platforms. It’s built using a modern tech stack, emphasizing security, responsiveness, and usability.
🔗 Visit ProjectResearch Papers
Forensic Analysis of Bangla Handwritten Letters
Publication: Proceedings of the 8th International Conference on Computer and Communications Management
Abstract: Handwriting says a lot about a person. Sometimes the information hidden there becomes the most important clues for an investigation. Traditionally detectives take help from the Graphologists. However, given that handwriting analysis is mostly based on visual features, machine learning algorithms should be able to find out important features. In this research, we used a Bangla handwriting dataset to identify the age, gender, and location (district) of the writer using deep learning algorithms. To the best of our knowledge, we are the first to address these three features from Bangla handwriting. We found that age could be identified with around 87.2% accuracy, location with 65.2% accuracy, and gender with 55.8% accuracy. The main causes of the low accuracy are the complex geometric shapes of Bangla letters. Mastery of those shapes is clearly reflected by the age of the writers.
View More Details →Soil Analysis and Unconfined Compression Test Study using Data Mining Techniques
Publication: ICCCI 2020: Advances in Computational Collective Intelligence
Abstract: In this study, Random Forest Regressor, Linear Regression, Generalized Regression Neural Network (GRNN) and Fully connected Neural Network (FCNN) models are leveraged for predicting unconfined compression coefficient with respect to standard penetration test (N-value), depth and soil type. The study is focused on a particular correlation of undrained shear strength of clay (Cu) with the standard penetration strength. The data used is from 14 no. ward in Mymensingh and Rangamati districts which are situated in Bangladesh. By using this data, the study tries to solidify the correlation of SPT (N-value) with Cu. It also tries to check the goodness of the relationship by comparing it with unconfined compression strength values gained from the unconfined compression test calculated from the field by experts.
View More Details →Analysis of Soil and Various Geo-technical Properties using Data Mining Techniques
Publication: IEEE
Abstract: In this study, General Regression Neural Network(GRNN), Artificial Neural Network (ANN), Fully Connected Neural Network (FCNN), Support Vector Regression (SVR) and Linear Regression (LR) models have been implemented in order to predict the composition of soil with respect to the Standard Penetration Test (SPT), and soil depth. The primary focus has been on determining a significant correlation between the soil composition with SPT value and depth. Data sets have been used from ward 14, Mymensingh district of Bangladesh and from a construction project along India-Myanmar border. In this study, 8 types of soil, namely, fine sand, silty clay, clayey silt with fine sand, clayey silt, fine sand with silt, silty fine sand, sandy silt, and rubbish has been classified, and the probability of obtaining the soil type classification has been determined.
View More Details →Lip Reading Bengali Words
Publication: ACAI 2022: Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence
Abstract: This work aims to lip-read Bengali words from talking faces without using audio. Lip reading for English words and sentences is well explored in literature. However, to our knowledge, we are the first to explore this for Bengali words, a language spoken by about 272 million people in south-east Asia [7]. We used a CNN to extract features from the video frames in sequence and provided the features to a bidirectional LSTM network followed by a classifier. We trained the entire network end-to-end. We investigated the effects of using different types of convolution operations during feature collection. We used convolution with filters of multiple scales in a single stage (Inception [24]), depthwise and pointwise convolution (MobileNet [25]), traditional CNN (VGG16 [26], ResNet [17], DenseNet [27], ResNeXt [28]), and a custom CNN. For Bengali word lip reading, MobileNet [25] (as CNN) followed by a bidirectional LSTM and classifier achieved the highest accuracy of 84.75%. Moreover, we found that longer words have better detection rates than shorter ones using any type of convolution.
View More Details →Data Science Projects
Bartender
About: In recent times, the state of the art model BERT has been getting a lot of spotlight. BERT is a massive model. And it's often easy to get lost in it. So here, we present a framework that will automate the creation of embeddings using the BERT model and help understand these embeddings better, by visualizing them, tracking their source sentences and similar other features. This will let users visualize the high dimensional word embeddings to lower dimension and find out how the words are retaining context without dwelling into the technical details of setting up a model. The present framework has been tested with two languages namely Bangla and Arabic. The experiments reveals the usability of this framework and how it can help NLP reseachers and linguists in finding more about their corpus.
Github Link: https://github.com/sayeedk06/BertenderClick for more details....
Text Summarization on COVID-19 Articles
About: Summarization has long been a significant area of concern in the field of Natural Language Processing, mostly due to the its dependency on human intervention. In this study, we use a denoising autoencoder, BART, to carry out abstractive summarization on the dataset of COVID-19, using ROUGE scores as an evaluation metric. The primary focus of the study has been the use of medical articles based on COVID-19, which is aimed to provide a significant support for the research purpose during the pandemic
Github Link: https://github.com/sayeedk06/CSE465-project-Covid19SummaryClick for more details....
Software Development
Rick and Morty character index
Technology: Next.js
This project uses the Rick and Morty APIto build a character deck of the show Rick and Morty. The idea was to create collectible-style cards like Pokémon and eventually expand into an online card game.
For now, it demonstrates my frontend capability with React and Next.js.Click here to visit →
Personal Projects / Coursework
Veditor
This application leverages AWS services to allow users the function to convert photos into a video slideshow and video to gif. Tech Stack:Docker, AWS-S3, AWS-DynamoDb, AWS-cognito, AWS-lambda, AWS-Elastic Container Service(ECS), AWS-Simple Queue Service(SQS), nodeJs, reactjs
Visit Project →InterportCargo
Developed a web application using ASP.NET Razor Pages to streamline the quotation request process between customers and employers at InterportCargo.
Visit Project →TrebleCross
A TrebleCross game implemented using c#. It implements a strategy patterns for both the Player and Board class for extensibility and to solve the issue of generalizing the code to be used for different games without code repetition
Visit Project →Populace
A web application named Populace that tries to bring piazza and google-classroom in one platform using the pypi piazza api and the official google-classroom api. (Built with Django).
Visit Project →HACKNSU SEASON 2.0
The main idea, is to create a more company centric platform to tackle the problem of vendors to company interaction and vendor’s inability to adapt to the digital platform. In this platform, company ‘A’ will keep an open list of items it wants to purchase, with details about the amount and delivery timing. This window can be seen by any vendor to send a proposal on the items as per their specification. This proposal will then be monitored by the company representative to choose the best offer and send the acceptance message
Visit Project →