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Vital Events Data Visualization Tool
Vital Events Data Visualization Tool
During my co-op term, I developed an R Shiny application that visualizes population health indicators across British Columbia. This interactive dashboard presents aggregated statistical data related to vital events, allowing users to explore demographic patterns and trends.
The project involved extensive data processing, statistical analysis, and interactive visualization development. I collaborated with healthcare professionals to implement features that made complex population health data more accessible and actionable for decision-makers.
Skills demonstrated: R programming, Shiny framework, data visualization, healthcare analytics, statistical analysis, and collaborative development with subject matter experts.
Ontario Vital Events- Interactive Dashboard
Ontario Vital Events- Interactive Dashboard
Used an open-access dataset containing the number of births, deaths, marriages, and stillbirths registered by the Registrar General from 1994.
Dataset: https://data.ontario.ca/dataset/vital-events-data-by-month
Date range: January 1, 1994 - December 31, 2022.
ETL performed using PowerBI
Utilized other provincial data sources for data analysis, including data visualization techniques.
https://github.com/shakirul15-311/Ontario-Vital-Events--Interactive-Dashboard
RaMSeS: Researcher’s Management of Search and Storage
RaMSeS: Researcher’s Management of Search and Storage
– Introduced the RaMSeS model, delineating the academic document collection management process within the context of academic document creation.
– Designed and deployed a survey to collect user feedback and preferences.
– Utilized Python for data analysis, including data visualization techniques like cluster analysis and correlation analysis. Utilized statistical significance analysis for robust results.
– Collaborated effectively in a multidisciplinary team to achieve project objectives.
– Maintained comprehensive documentation of design decisions and research findings.
https://github.com/shakirul15-311/Academic-Research-Material-Habits-and-Challenges
Information on Teachers in BC School Districts
Information on Teachers in BC School Districts
This was a Project for INFORMATION VISUALIZATION (CSC 511) course by Prof. Charles Perin
- Our first dataset is the “British Columbia (BC) Public School Teacher Statistics”, which includes all data used in public reports from 2013 to 2021.
The data dimensions are employment category, headcount, predicted gender, average age, and average annual salary.
The data cases are the years from 2013 to 2021 and the district name.
- The second dataset is records of “Student enrolment and FTE by Grade including facility type and counts by indigeneity”.
- The targeted users are the education policymakers to analyze the condition of teachers across BC's various school districts.
Doctor Finder Mobile App
Doctor Finder Mobile App
– Developed this mobile application for locating doctors and scheduling appointments.
– Used Retrofit API for handling HTTP requests and RESTful API interactions, Postman for API testing.
– XML for UI design, Java for development, and Python for certain backend functionalities.
– Version control using GitLab for efficient collaboration and code management
IFIC Bank: IT Support Portal
IFIC Bank: IT Support Portal
During my stay at IFIC Bank Limited Bangladesh, I have got the chance to work with amazing technologies. I have developed a web solution for the end-users to mitigate their common tech-related issues. The main goal of this project was to give them the solution of common issues like core-banking software setup procedure, setting the devices they are using.
Bayanno-Net: Bangla Handwritten Digit Recognition using Convolutional Neural Networks
Bayanno-Net: Bangla Handwritten Digit Recognition using Convolutional Neural Networks
-- Explored the topic of handwritten digit recognition, a novel and evolving field in recent years.
-- Acknowledged the complexities of recognizing handwriting, which vary among languages due to differences in shapes, character numbers, and strokes.
-- Highlighted the significance of recognizing Bangla as the 7th most popular language based on the number of first language speakers.
-- Differentiated from traditional approaches that use discrete feature extraction methods and algorithms, emphasizing the use of Deep Learning and Convolutional Neural Networks for improved accuracy in image classification.
-- Proposed the use of a Convolutional Neural Network named "ByannoNet" for identifying Bangla handwritten digits.
-- Utilized the rich NumtaDB dataset, generated and published by the Bengali.ai community.
-- Demonstrated the success of the proposed model, achieving an impressive accuracy rate of 97% with a very low cross-entropy rate.
InceptB: A CNN Based Classification Approach for Recognizing Traditional Bengali Games
InceptB: A CNN Based Classification Approach for Recognizing Traditional Bengali Games
-- Introduced autonomous decision-making and predictive models in computer vision to recognize and analyze various sports events and activities, reflecting the emerging trend in the field.
-- Addressed the scarcity of efforts in applying computer vision to recognize traditional Bangladeshi games, despite the advances in recognizing popular Western games.
-- Presented a novel Deep Learning-based approach for identifying traditional Bengali games.
-- Employed retraining of the final layer of the Inception V3 architecture by Google for the classification approach.
-- Achieved promising results, with an average accuracy of approximately 80%, in correctly recognizing five traditional Bangladeshi sports events.
https://www.sciencedirect.com/science/article/pii/S1877050918321343
A Novel Approach for Tomato Diseases Classification Based on Deep Convolutional Neural Networks
A Novel Approach for Tomato Diseases Classification Based on Deep Convolutional Neural Networks
-- Leveraged neural networks for solving agricultural challenges, specifically focusing on early detection of tomato diseases.
-- Developed a 15-layered Deep Convolutional Neural Network to classify five different tomato diseases with high accuracy and low cross-entropy.
-- Addressed a critical issue in the agriculture industry, where plant diseases have long been a major factor affecting product quality.
-- Demonstrated the potential of artificial intelligence to contribute to the success and rapid growth of the agricultural sector.
-- Provided a basic approach for tomato disease classification, contributing to the agroindustry's success and the improvement of product quality.
https://link.springer.com/chapter/10.1007/978-981-13-7564-4_49
Shot-Net: A Convolutional Neural Network for Classifying Different Cricket Shots.
Shot-Net: A Convolutional Neural Network for Classifying Different Cricket Shots.
-- Leveraged Artificial Intelligence for data analytics in the context of sports, specifically in classifying six categories of cricket shots.
-- Developed a 13-layered Convolution Neural Network known as "Shot-Net" for this task.
-- Successfully classified cricket shots, including Cut Shot, Cover Drive, Straight Drive, Pull Shot, Scoop Shot, and Leg Glance Shot.
-- Achieved a high level of accuracy with a low cross-entropy rate, demonstrating the effectiveness of Deep Neural Networks in sports data analysis.
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