Ittahad Uz Zaman
Senior Software Engineer / Deputy Engineering Manager

Intro

As a Senior Software Engineer at SELISE Digital Platforms, I develop and maintain microservices in .NET with CQRS architecture pattern and Domain Driven Design. I also lead a team here at SELISE, where we work on the Signature product that provides e-signature solutions to Swiss, German, Bhutan, and BD clients. I also research and prototype new technologies and ideas to enhance the performance, feasibility, and maintainability of cloud applications. I have experience with Docker and Kubernetes also hands-on experience with DevSecOps.

I graduated with a Bachelor of Science in Computer Science and Engineering from United International University in 2019, where I also worked as a teaching assistant for two courses. I have multiple certifications in cloud computing, web development, and Kubernetes. I have published two papers on IoT and machine learning for cardiac status prediction. I am passionate about learning new skills and contributing to innovative and impactful projects.

Professional Projects

SELISE Signature

SELISE Blocks

SELISE Apps

SELISE Document Manager

Certifications

Getting Started with Google Kubernetes Engine

Front-end JavaScript Frameworks: Angular

Server-side Development with NodeJS, Express and MongoDB

Cloud Computing Basics (Cloud 101)

Docker Mastery: with Kubernetes + Swarm

Kubernetes for the Absolute Beginners - Hands-on

The Complete Flutter Development Bootcamp with Dart

Tailwind CSS

OpenAPI Specification

Relational Database Design

Complete SQL Bootcamp

SQL for data science

Clean Code

United International University

Bachelor's degree, Computer Science and Engineering

CGPA

3.98 / 4.00

Publications

Nowadays, the burden of disease has shifted dramatically from communicable to non-communicable ailments, a trend particularly significant in Bangladesh. Deaths resulting from such diseases have a major impact on people's lives. Among all non-communicable diseases, cardiovascular disease is highly prevalent in our country. However, many people cannot afford the cost of regular check-ups.

Our paper proposes a prototype demonstrating a mechanism for identifying patients with cardiac disease. Our prototype consists of different sensor modules to collect data, such as Heart BPM, Cholesterol, and ECG readings. We utilize a Machine Learning model to classify patients based on the collected data. The prediction is made visible to the patient instantly so they can take necessary precautions beforehand.

To date, we have managed to collect data from Heart Rate sensors, ECG sensors, Cholesterol sensor modules, and Blood Pressure modules. Currently, we are working on integrating the Glucose sensor module.

Cardiac Disease Predictor: An IoT and ML-driven health care system

Towards IoT and ML Driven Cardiac Status Prediction System

The death rate in Bangladesh due to various types of non-communicable cardiac diseases is rapidly increasing every year. These diseases are chronic and progress slowly, often reaching a critical stage that is difficult to manage. Consequently, many people experience sudden heart attacks or only discover their condition when it is too late.

In this paper, we propose an IoT and Machine Learning-based method for predicting cardiac status. This method involves collecting essential data from the human body using IoT devices (sensors) and transferring this data to the cloud, where it is stored securely with user authentication. The data obtained from the human body are then normalized before applying machine learning algorithms to calculate and predict the overall condition of a patient's heart.

So far, we have been able to collect heart rate, ECG signals, and cholesterol levels through IoT devices from the human body. The results obtained for heart rate and cholesterol levels are quite satisfactory. However, we are still facing some challenges with interpreting the ECG signals, which we are actively working on resolving.