Md Jobair Hossain Faruk

Conducting research, writing paper, developing software, presenting research papers in conferences.

Research domains:
- Machine Learning
- Software Security
- Health Informatics
- Blockchain Technology
- Brain-Computer Interfaces (BCI)

*from the aspects of software engineering.

Achievements:
- Best Special Paper Award (IEEE ICDH 2021)
- Innovative Research and Best Presenter in Coles Research Symposium on Homeland Security 2021
- Five Research Paper Publications in one year on IEEE

- Assist in the development, testing, and Maintenance of ODA's flagship system, which is content-driven, device responsive, and multilingual
- Software update and change management in AWS and GitHub environments
- Assist in the development of the SG-Agenda system using the Angular framework
- Develop and maintains ODA's champion in the AWS and WordPress environment
- Conceptualizes and prepares and coordinates the development and testing of new content on websites
- Assists in the development, testing, and maintenance of e-learning platforms using cutting-edge technologies (e.g. cloud computing, big data, machine learning, analytics/visualization, etc.)
- Assist the ODA's Information Outreach Branch in preparing and organizing events
- Support ODA's Information Technology (IT) services and systems

Traditional data collection, storage and processing of Electronic Health Records (EHR) utilize centralized techniques that pose several risks of single point of failure and lean the systems to a number of internal and external data breaches that compromise their reliability and availability. Blockchain is an emerging distributed technology that can solve these issues due to its immutability and architectural nature that prevent records manipulation or alterations. In this paper, we discuss the progress and opportunities of remote patient monitoring using futuristic blockchain technologies and its two primary frameworks: Ethereum and Hyperledger Fabric. We also discuss the possible blockchain use cases in software engineering for systematic, disciplined, and quantifiable application development. The study extends by introducing a system architecture for EHR data management using Ethereum as a model. We discuss the challenges and limitations along with the initial evaluation results of the proposed system and draw future research directions in this promising area.

Neurotechnology describes as one of the focal points of today’s research of hundreds of academicians and researchers globally; particularly around the domain of Brain-Computer Interfaces (BCI) that was introduced in the 1970s and has been at the forefront of many neurotechnological discoveries. The primary attempts of BCI research are to decoding human speech from brain signals, implementing creativity by imagination, and controlling neuro-psychological patterns by utilizing billion of neural activities that would significantly benefit people suffering from neurological disorders. In this study, we illustrate the progress of BCI research and present scores of unveiled contemporary approaches. First, we explore a decoding natural speech approach that is designed to decode human speech directly from the human brain onto a digital screen introduced by Facebook Reality Lab and University of California San Francisco. Then, we study a recently presented visionary project to control the human brain using Brain-Machine Interfaces (BMI) approach. We also investigate well-known electroencephalography (EEG) based Emotiv Epoc+ Neuroheadset to identify six emotional parameters including engagement, excitement, focus, stress, relaxation, and interest using brain signals by experimenting the neuroheadset among three human subjects where we utilize two supervised learning classifiers, Naïve Bayes and Linear Regression to show the accuracy and competency of the Epoc+ device and its associated applications in neurotechnological research. We present experimental studies and the demonstration indicates 69 % and 62 % improved accuracy for the aforementioned classifiers respectively in reading the performance matrices of the participants. We envision that non-invasive, insertable, and low-cost BCI approaches shall be the focal point for not only an alternative for patients with physical paralysis but also understanding the brain that would pave us to access and control the memories and brain somewhere very near.