Hey👋, I'm Asif!

I like deep learning, building websites, and a lot of other things!

**This is a work in progress!

About Me

Hello! I'm Asif Sijan. I am a Software Engineer and Researcher. I love building new things, as well as researching and working on several interesting topics! My research interest includes:

Deep Learning

Machine Learning

Computer Vision

Data Science

NLP

Besides them, I'm into digital drawing, game development, music, writing, and a lot of other things. Feel free to email or knock me in any of the above-mentioned places. Pretty sure we'll have something of the same interest to talk about!

Also, Here are few languages and technologies I have been working with recently:

Python

C++

Javascript

Tensorflow

React

Next.js

Node.js

Express

MongoDB

Tailwind

Research Works

1. S. M. Taslim Uddin Raju, Ashfaq Ahmad, Md Imran Hossain, Safin Ahmed Dipto, Araf Nishan, Asif Sijan, S. M. Tanvir Uddin, Md Abu Shahid Chowdhury and Md Mahamudul Hasan Khan. A Continuous Cuffless Blood Pressure Measurement from Optimal PPG Characteristic Features Using Machine Learning Algorithms, [under review].

Background and objective: Hypertension is a potentially dangerous health condition that can be detected by measuring blood pressure (BP). Blood pressure monitoring and measurement are essential for preventing and treating cardiovascular diseases. Cuff-based devices, on the other hand, are uncomfortable and prevent continuous BP measurement.

Methods: In this study, a new non-invasive and cuff-less method for estimating Systolic Blood Pressure (SBP), Mean Arterial Pressure (MAP), and Diastolic Blood Pressure (DBP) has been proposed using characteristic features of photoplethysmogram (PPG) signals and nonlinear regression algorithms. PPG signals were collected from 219 participants, which were then subjected to preprocessing and feature extraction steps. Analyzing PPG and its derivative signals, a total of 46 time, frequency, and time-frequency domain features were extracted. In addition, the age and gender of each subject were also included as features. Further, correlation-based feature selection (CFS) and Relief F feature selection (ReliefF) techniques were used to select the relevant features and reduce the possibility of over-fitting the models. Finally, support vector regression (SVR), K-nearest neighbour regression (KNR), decision tree regression (DTR), and random forest regression (RFR) were established to develop the BP estimation model. Regression models were trained and evaluated on all features as well as selected features. The best regression models for SBP, MAP, and DBP estimations were selected separately.

Results: The SVR model, along with the ReliefF based feature selection algorithm, outperforms other algorithms in estimating the SBP, MAP, and DBP with the mean absolute error of 2.49, 1.62 and 1.43 mmHg, respectively. The proposed method meets the Advancement of Medical Instrumentation standard for BP estimations. The achieved results also fall within Grade A for SBP, MAP, and DBP based on the British Hypertension Society standard.

Conclusion: The findings show that the method can be used to estimate blood pressure non-invasively, without the use of a cuff or calibration, and only by utilizing the PPG signal characteristic features.

2. Undergraduate Final Year Research Project: 9, 6, 3 Initializer and It’s Impact on Convergence

Different components make up a neural network made up of deep learning models. The accuracy and performance of a model are determined by a variety of components and niches, including activation functions, optimizers, loss or cost functions, augmentation, dropout regularization, and so on. But an often-overlooked step would be initializing a model’s weights and biases. We have presented a framework for weight and bias initialization of a deep learning model in this research. Applying it on different models with the same architecture, with the proposed framework- model’s efficiency increased by around 25 to 30 percent compared to initializing random weights and biases. Considering deeper networks, efficiency boost would result in significantly less time to finish training. And the suggested framework will further improve models that rely on efficiency measures, such as models that operate on mobile or embedded systems where resources are limited.(read here)

3. Undergraduate Third Year Project: Smart Inventory System using Market Basket Analysis

This project used sales data to predict customer behavior and buying patterns using which associations or connections were made between different products. This was achieved using certain data-mining techniques named association rule mining and apriori algorithm which falls under Market Basket Analysis domain. After getting the association results, several insights were shown to suggest to the customer what products are frequently bought together, and what to restock. All of the insights were represented through a web application for the ease of user’s use. For further improvement, a server with higher computation power will be required to deploy the backend portion which takes a significant time to create the associations from sample data.(read here)