Mahbuba Habib

Electrical & Computer Engineer

This project is maintained by mahbuba26

AI & Deep learning Enthusiastic

I am an ECE graduate with a robust enthusiasm for accelerated learning and a commitment to exploring advanced concepts in academia. My primary research focus is on natural language processing, complemented by a strong passion for app development. I am dedicated to deepening my expertise in these domains and contributing to innovative advancements within the field.

Technical Skills:

Programming Languages: C, C++, Java, Python

Database: MySQL Server

Numerical Analysis & Image Processing: MATLAB

Microprocessor Programming: 8086 Emulator

Network Simulator: Cisco Packet Tracer

Frameworks & Libraries: NumPy, Pandas, Scikit-Learn, Matplotlib, Seaborn

Softwares: Android Studio, Jupyter Notebook, GitHub

Education

Projects

Online Food Ordering

Successfully delivered in-depth health information including Body Mass Index (BMI), Basal Metabolic Rate (BMR), and customized calorie requirements through the creation of a user-focused Android app using Java. This accomplishment was made possible by emphasizing user-friendly design and ease of access. Furthermore, achieved accurate monitoring and management of data within the app, ensuring precise tracking of users’ daily calorie consumption, through the skillful implementation of a specific feature. This success was a result of meticulous attention to detail and proficient use of Java programming skills.(Details in Github)

Food App Demo This is the basic layout of my food ordering app, illustrating the interaction between various components. It highlights the main page interface, as well as features such as how the admin can communicate with the shop owner and other key functionalities.

Food App Demo The customer interface is designed with simplicity in mind, featuring an intuitive user interface that facilitates easy food ordering.

Food App Demo Food App Demo These sections demonstrate the admin handling features of the app, including the management of different food items and updates as needed. The app also provides functionalities for order processing and management, as well as tracking riders efficiently.

Food App Demo This section illustrates how riders manage orders and interact with customers, showcasing the functionality for efficient order handling and communication between riders and customers.

Calorie Counter

Accomplished streamlined user experience as measured by enhanced user engagement and satisfaction, by developing a comprehensive Android application leveraging Java and Firebase. Integrated intuitive user interface design, admin panel for seamless management, user activity tracking, and customizable rider options.(Details in Github)

Calorie App Demo The custom layout of this app features a user-friendly interface designed for ease of use. It enables users to effortlessly track their daily food calories, ensuring a seamless experience for managing their dietary intake.

Calorie App Demo This layout demonstrates the functionality for measuring calorie intake, provides various food suggestions, and includes options for contacting the owner.

Online Quiz App

Improved students’ learning experience and assessment skills were achieved through the development of an interactive online quiz application. This was made possible by creating a user-friendly interface and providing customized multiple-choice questions, resulting in higher engagement and enhanced performance.(Details in Github)

Quiz App Demo This schema outlines the structure of a quiz, categorizing various subjects and their associated sets of questions for students.

Car Counter using Machine Learning

(Ongoing Project)

Led a team of three members in developing an OpenCV-based system for detecting and recording bi-directional car counts with corresponding dates.The project is presently in progress.

Half Bridge Inverter

This project, undertaken by a team of three members, involved developing a basic half-bridge inverter using Arduino technology.(Details in Github)

Inverter Demo The system utilized a 12V input, with the output analyzed and measured using an oscilloscope.We observed an output of approximately 5 volts.

Courses in Coursera:

Courses in Udemy:

Activities

Thesis

  1. Mahbuba Habib, Hafsa Binte Kibria, “Feature Selection-based Machine Learning Approaches for Detecting Android Malware with Explainable AI” (Accepted in ICAEEE 2024, 3rd International Conference on Advancement in Electrical and Electronic Engineering, DUET, Dhaka, Bangladesh)

Contact

Gmail

LinkedIn