Communication & Teamwork

Comfortable in communicating with people of different age, group, culture and social class.

• Can speak & write English, Bangla
• Leader in various group assignments

Technical Skills

Knowledgeable in Machine Learning algorithms, Deep Learning algorithms, libraries and frameworks

• Classification and Regression models
• Pandas, scikit-learn, numpy, MLlib
• Keras, Tensorflow

Knowledgeable in multiple programming languages, markup languages, libraries and frameworks.

• Python, C, C++, Java, Matlab, R, PHP, JavaScript
• HTML, CSS

Familiar with multiple Integrated Development Environment and Text Editor.

• Anaconda
• Android Studio
• Microsoft Visual Studio
• Matlab, Octave
• Pycharm
• Eclipse
• R Studio
• Atom
• Sublime Text

Analytical

Possess solid algorithmic thinking, problem solving and analytical skills.

• A quick learner

Possess solid mathematics background.

• Discrete Mathematics
• Calculus
• Numerical Method
• Geometry
• Linear Algebra
• Statistics

Kaggle Competitions

Ranking of Kaggle Competitions

• Ranked top 32% in "Toxic Comment Classification Challenge". Click Here.
• Ranked top 23% in "Final project: predict future sales". Click Here.
• Ranked top 16% in "LANL Earthquake Prediction". Click Here.
• Position: 2317 in "2019 Data Science Bowl". Click Here.
• Position: 1941 in "Bengali.AI Handwritten Grapheme Classification". Click Here.
• Position: 610 in "RSNA STR Pulmonary Embolism Detection". Click Here.

Build Basic Generative Adversarial Networks (GANs), offered by DeepLearning.AI

About this Course

• Certification name: Build Basic Generative Adversarial Networks (GANs)
• Achieved: December 22, 2020
• Certification authority: Coursera
• License number: QMV9NEVQ5SXG
• Certification URL: Click Here

What is Data Science?, offered by IBM

About this Course

• Certification name: What is Data Science?
• Achieved: June 01, 2020
• Certification authority: Coursera
• License number: LFXZEAY276QH
• Certification URL: Click Here

Getting Started with Essay Writing, by University of California, Irvine

About this Course

• Certification name: Getting Started with Essay Writing
• Achieved: May 29, 2020
• Certification authority: Coursera
• License number: PYGG3CJ26JAW
• Certification URL: Click Here

Grammar and Punctuation, by University of California, Irvine

About this Course

• Certification name: Grammar and Punctuation
• Achieved: May 15, 2020
• Certification authority: Coursera
• License number: 7LW9SBJHRVV5
• Certification URL: Click Here

How to Win a Data Science Competition: Learn from Top Kagglers(With Honors), by National Research University Higher School of Economics

About this Course

• Certification name: How to Win a Data Science Competition: Learn from Top Kagglers(With Honors)
• Achieved: May 07, 2018
• Certification authority: Coursera
• License number: 6EMSVQ6WVWR4
• Certification URL: Click Here

Introduction to Deep Learning, by National Research University Higher School of Economics

About this Course

• Certification name: Introduction to Deep Learning
• Achieved: Apr 10, 2018
• Certification authority: Coursera
• License number: QLWDW3HKJVZN
• Certification URL: Click Here

The Data Scientist’s Toolbox, by Johns Hopkins University

About this Course

• Certification name: The Data Scientist’s Toolbox
• Achieved: Feb 27, 2018
• Certification authority: Coursera
• License number: SLAZ5K264LC5
• Certification URL: Click Here

Deep Learning Specialization, by deeplearning.ai

Specialization Description

• Certification name: Deep Learning Specialization
• Achieved: February, 2018
• Certification authority: Coursera
• License number: NHNHYQ32CCGJ
• Certification URL: Click Here
• Course Teacher: Andrew Ng

Sequence Models, by deeplearning.ai

About this Course

• Certification name: Sequence Models
• Achieved: Feb 24, 2018
• Certification authority: Coursera
• License number: MTEC8WLQXZEZ
• Certification URL: Click Here
• Course Teacher: Andrew Ng

Convolutional Neural Networks, by deeplearning.ai

About this Course

• Certification name: Convolutional Neural Networks
• Achieved: Feb 09, 2018
• Certification authority: Coursera
• License number: FJKK2MVTPJ9E
• Certification URL: Click Here
• Course Teacher: Andrew Ng

Structuring Machine Learning Projects, by deeplearning.ai

About this Course

• Certification name: Structuring Machine Learning Projects
• Achieved: Jan 31, 2018
• Certification authority: Coursera
• License number: YUC56KMA34EQ
• Certification URL: Click Here
• Course Teacher: Andrew Ng

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, by deeplearning.ai

About this Course

• Certification name: Improving Deep Neural Networks:Hyperparameter tuning, Regularization and Optimization
• Achieved: Nov 21, 2017
• Certification authority: Coursera
• License number: QFBKKNT4XFCY
• Certification URL: Click Here
• Course Teacher: Andrew Ng

Neural Networks and Deep Learning, by deeplearning.ai

About this Course

• Certification name: Neural Networks and Deep Learning
• Achieved: Oct 21, 2017
• Certification authority: Coursera
• License number: 9J7NCZTAE66G
• Certification URL: Click Here
• Course Teacher: Andrew Ng

Machine Learning, by Stanford University

About this course

• Certification name: Machine Learning
• Achieved: Apr 07, 2017
• Certification authority: Coursera
• License number: U9PTRVDU3VRD
• Certification URL: Click Here
• Course Teacher: Andrew Ng

Title: Prediction of Cervical Cancer from Behavior Risk Using Machine Learning Techniques.

Abstract: Cervical cancer growth is the fourth maximum of regular diseases in females. It is one of the sicknesses which is compromising ladies' wellbeing everywhere in the world and it is difficult to notice any sign in the beginning phase. But the screening process of cervical cancer sometimes is being hampered due to some social-behavioral factors. There is still a limited number of researches directed in cervical cancer identification dependent on the behavior and machine learning in the area of gynecology and computer science. In this research, we have proposed three machine learning models such as Decision Tree, Random Forest, and XGBoost to predict cervical cancer from behavior and its variables and we got significantly improved outcomes than the current methods with 93.33% accuracy. Moreover, we have shown the top features from the dataset according to the feature important scores to know their impacts on the development of the classification model.

• Published in: SN Computer Science 2 (3), 1-10 | journal-article
• Article DOI: https://doi.org/10.1007/s42979-021-00551-6
• Publisher: Springer Nature
• Article URL: Click Here

Title: Hemoglobin and glucose level estimation from PPG characteristics features of fingertip video using MGGP-based model.

Abstract: Hemoglobin and the glucose level can be measured after taking a blood sample using a needle from the human body and analyzing the sample, the result can be observed. This type of invasive measurement is very painful and uncomfortable for the patient who is required to measure hemoglobin or glucose regularly. However, the non-invasive method only needed a bio-signal (image or spectra) to estimate blood components with the advantages of being painless, cheap, and user-friendliness. In this work, a non-invasive hemoglobin and glucose level estimation model have been developed based on multigene genetic programming (MGGP) using photoplethysmogram (PPG) characteristic features extracted from fingertip video captured by a smartphone. The videos are processed to generate the PPG signal. Analyzing the PPG signal, its first and second derivative, and applying Fourier analysis total of 46 features have been extracted. Additionally, age and gender are also included in the feature set. Then, a correlation-based feature selection method using a genetic algorithm is applied to select the best features. Finally, an MGGP based symbolic regression model has been developed to estimate hemoglobin and glucose level. To compare the performance of the MGGP model, several classical regression models are also developed using the same input condition as the MGGP model. A comparison between MGGP based model and classical regression models have been done by estimating different error measurement indexes. Among these regression models, the best results (±0.304 for hemoglobin and ±0.324 for glucose) are found using selected features and symbolic regression based on MGGP.

• Published in: Biomedical Signal Processing and Control, Elsevier | journal-article
• Article DOI: https://doi.org/10.1016/j.bspc.2021.102478
• Publisher: Elsevier
• Article URL: Click Here

Title: A Novel Technique for Non-Invasive Measurement of Human Blood Component Levels from Fingertip Video Using DNN Based Models.

Abstract: Blood components such as hemoglobin, glucose, creatinine measuring are essential for monitoring one’s health condition. The current blood component measurement approaches still depend on invasive techniques that are painful, and uncomfortable for the patients. To facilitate measurement at home, we proposed a novel non-invasive technique to measure blood hemoglobin, glucose, and creatinine level based on PPG signal using Deep Neural Networks (DNN). Fingertip videos from 93 subjects have been collected using a smartphone. The PPG signal is generated from each video, and 46 characteristic features are then extracted from the PPG signal, its derivatives (1st and 2nd) and from Fourier analysis. Additionally, age and gender are also included to feature because of the significant effects on hemoglobin, glucose, and creatinine. A correlation-based feature selection (CFS) using genetic algorithms (GA) has been used to select the optimal features to avoid redundancy and over-fitting. Finally, DNN based models have been developed to estimate the blood Hemoglobin (Hb), Glucose (Gl), and Creatinine (Cr) levels from the selected features. The approach provides the best-estimated accuracy of R2 = 0.922 for Hb, R2 = 0.902 for Gl, and R2 = 0.969 for Cr. Experimental aftermaths show that the proposed method is a suitable technique to be used clinically to measure human blood component levels without taking blood samples. This paper also reveals that smartphone-based PPG signal has a great potential to measure the different blood components.

• Published in: IEEE Access 2021 | journal-article
• Article DOI: 10.1109/ACCESS.2021.3054236
• Publisher: IEEE Access
• Article URL: Click Here

Title: Scalable Telehealth Services to Combat Novel Coronavirus (COVID-19) Pandemic.

Abstract: An ongoing pandemic, the novel coronavirus disease 2019 (COVID-19) is threatening the nations of the world regardless of health infrastructure conditions. In the age of digital electronic information and telecommunication technology, scalable telehealth services are gaining immense importance by helping to maintain social distances while providing necessary healthcare services. This paper aims to review the various types of scalable telehealth services used to support patients infected by COVID-19 and other diseases during this pandemic. Recently published research papers collected from various sources such as Google Scholar, ResearchGate, PubMed, Scopus, and IEEE Xplore databases using the terms “Telehealth”, “Coronavirus”, “Scalable” and “COVID-19” are reviewed. The input data and relevant reports for the analysis and assessment of the various aspects of telehealth technology in the COVID-19 pandemic are taken from official websites. We described the available telehealth systems based on their communication media such as mobile networks, social media, and software based models throughout the review. A comparative analysis among the reviewed systems along with necessary challenges and possible future directions are also drawn for the proper selection of affordable technologies. The usage of scalable telehealth systems improves the quality of the healthcare system and also reduces the infection rate while keeping both patients and doctors safe during the pandemic.

• Published in: SN Computer Science volume 2, Article number: 18 (2021)
• Article DOI: https://doi.org/10.1007/s42979-020-00401-x
• Publisher: Springer Nature
• Article URL: Click Here

Title: Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic.

Abstract: During this global pandemic, researchers around the world are trying to find out innovative technology for a smart healthcare system to combat coronavirus. The evidence of deep learning applications on the past epidemic inspires the experts by giving a new direction to control this outbreak. The aim of this paper is to discuss the contributions of deep learning at several scales including medical imaging, disease tracing, analysis of protein structure, drug discovery, and virus severity and infectivity to control the ongoing outbreak. A progressive search of the database related to the applications of deep learning was executed on COVID-19. Further, a comprehensive review is done using selective information by assessing the different perspectives of deep learning. This paper attempts to explore and discuss the overall applications of deep learning on multiple dimensions to control novel coronavirus (COVID-19). Though various studies are conducted using deep learning algorithms, there are still some constraints and challenges while applying for real-world problems. The ongoing progress in deep learning contributes to handle coronavirus infection and plays an effective role to develop appropriate solutions. It is expected that this paper would be a great help for the researchers who would like to contribute to the development of remedies for this current pandemic in this area.

• Published in: SN Computer Science volume 1, Article number: 363 (2020)
• Article DOI: https://doi.org/10.1007/s42979-020-00383-w
• Publisher: Springer Nature
• Article URL: Click Here

Title: Breast Cancer Prediction: A Comparative Study Using Machine Learning Techniques.

Abstract: Early detection of disease has become a crucial problem due to rapid population growth in medical research in recent times. With the rapid population growth, the risk of death incurred by breast cancer is rising exponentially. Breast cancer is the second most severe cancer among all of the cancers already unveiled. An automatic disease detection system aids medical staffs in disease diagnosis and offers reliable, effective, and rapid response as well as decreases the risk of death. In this paper, we compare five supervised machine learning techniques named support vector machine (SVM), K-nearest neighbors, random forests, artificial neural networks (ANNs) and logistic regression. The Wisconsin Breast Cancer dataset is obtained from a prominent machine learning database named UCI machine learning database. The performance of the study is measured with respect to accuracy, sensitivity, specificity, precision, negative predictive value, false-negative rate, false-positive rate, F1 score, and Matthews Correlation Coefficient. Additionally, these techniques were appraised on precision–recall area under curve and receiver operating characteristic curve. The results reveal that the ANNs obtained the highest accuracy, precision, and F1 score of 98.57%, 97.82%, and 0.9890, respectively, whereas 97.14%, 95.65%, and 0.9777 accuracy, precision, and F1 score are obtained by SVM, respectively.

• Published in: SN Computer Science volume 1, Article number: 290 (2020)
• Article DOI: https://doi.org/10.1007/s42979-020-00305-w
• Publisher: Springer Nature
• Article URL: Click Here

Title: A Computer Vision based Lane Detection Approach.

Abstract: Automatic lane detection to help the driver is an issue considered for the advancement of Advanced Driver Assistance Systems (ADAS) and a high level of application frameworks because of its importance in drivers and passerby safety in vehicular streets. But still, now it is a most challenging problem because of some factors that are faced by lane detection systems like as vagueness of lane patterns, perspective consequence, low visibility of the lane lines, shadows, incomplete occlusions, brightness and light reflection. The proposed system detects the lane boundary lines using computer vision-based technologies. In this paper, we introduced a system that can efficiently identify the lane lines on the smooth road surface. Gradient and HLS thresholding are the central part to detect the lane lines. We have applied the Gradient and HLS thresholding to identify the lane line in binary images. The color lane is estimated by a sliding window search technique that visualizes the lanes. The performance of the proposed system is evaluated on the KITTI road dataset. The experimental results show that our proposed method detects the lane on the road surface accurately in several brightness conditions.

• Published in: International Journal of Image, Graphics and Signal Processing(IJIGSP)
• Article DOI: 10.5815/ijigsp.2019.03.04
• Publisher: MECS
• Article URL: Click Here

Title: Performance Evaluation of Random Forests and Artificial Neural Networks for the Classification of Liver Disorder.

Abstract: Liver is the major organ inside the human body which is very supportive for digesting food, eliminating poisons, and stocking energy. The rate of Liver disorder patients is rapidly rising all over the world. But it is very hard to identify the disorder from its ambiguous symptoms which increases the mortality rate due to this disease. The paper represents an expert scheme for the classification of liver disorder using Random Forests (RFs) and Artificial Neural Networks (ANNs). The methods train the input features using 10-fold cross validation fashion. The dataset named as BUPA liver dataset is retrieved from UCI machine learning repository for our research study. The performance of the proposed scheme is assessed in view of accuracy, positive predictive value, negative predictive value, sensitivity, specificity and F1 score. The scheme delivers a better result for training but comparatively low for testing. The scheme obtained the accuracy of 80% and 85.29% by RFs and ANNs respectively along with the F1 score of 75.86% and 82.76% in testing phase.

• Published in: 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2)
• Paper DOI: 10.1109/IC4ME2.2018.8465658
• Publisher: IEEE
• Paper URL: Click Here

Title: Prediction of breast cancer using support vector machine and K-Nearest neighbors.

Abstract: Breast Cancer is one of the most exquisite and internecine disease among all of the diseases in medical science. It is one of the crucial reasons of death among the females all over the world. We present a novel modality for the prediction of breast cancer and introduces with the Support Vector Machine and K-Nearest Neighbors which are the supervised machine learning techniques for breast cancer detection by training its attributes. The proposed system uses 10-fold cross validation to get an accurate outcome. The breast cancer termed as Wisconsin breast cancer diagnosis data set is taken from UCI machine learning repository. The performance of the proposed system is appraised considering accuracy, sensitivity, specificity, false discovery rate, false omission rate and Matthews correlation coefficient. The approach provides better result both for training and testing. Furthermore, the techniques achieved the accuracy of 98.57% and 97.14% by Support Vector Machine and K-Nearest Neighbors individually along with the specificity of 95.65% and 92.31% in testing phase.

• Published in: Humanitarian Technology Conference (R10-HTC), 2017 IEEE Region 10
• Paper DOI: 10.1109/R10-HTC.2017.8288944
• Publisher: IEEE
• Paper URL: Click Here