Shahina Begum, Associate Professor

E-mail:
shahina.begum@mdh.se
Room:
U1-131
Phone:
+46-21-107370
Division:
Division of Intelligent Future Technologies
Research group:
Intelligent Systems


Shahina Begum received her PhD in computer science in 2011 from Mälardalen University. Shahina’s research focuses on developing intelligent systems in medical and industrial applications. Her research areas are decision support systems, knowledge-based systems, big data analytics and intelligent monitoring systems.

[Show all publications]

Latest publications:

Quality Index Analysis on Camera-based R-peak Identification Considering Movements and Light Illumination (Jun 2018)
Mobyen Uddin Ahmed, Hamidur Rahman, Shahina Begum
15th International Conference on Wearable, Micro & Nano technologies for Personalized Health (pHealth2018)

Internet of Things (IoT) Technologies for HealthCare (Mar 2018)
Mobyen Uddin Ahmed, Shahina Begum, Jean-Baptiste Bastel
4th EAI International Conference on IoT Technologies for HealthCare (HealthyIoT'17)

Automated EEG Artifact Handling with Application in Driver Monitoring (Dec 2017)
Shaibal Barua, Mobyen Uddin Ahmed, Christer Ahlström , Shahina Begum, Peter Funk
IEEE Journal of Biomedical and Health Informatics (J-BHI)

Cloud-based Data Analytics on Human Factor Measurement to Improve Safer Transport (Nov 2017)
Mobyen Uddin Ahmed, Shahina Begum, Carlos Alberto Catalina , Lior Limonad , Bertil Hök , Gianluca Di Flumeri
4th EAI International Conference on IoT Technologies for HealthCare (HealthyIoT'17)

Big Data Analytics in Health Monitoring at Home (Oct 2017)
Mobyen Uddin Ahmed, Shahina Begum
Medicinteknikdagarna 2017 (MTD 2017)

Vision-Based Remote Heart Rate Variability Monitoring using Camera (Oct 2017)
Hamidur Rahman, Mobyen Uddin Ahmed, Shahina Begum
4th EAI International Conference on IoT Technologies for HealthCare (HealthyIoT'17)

PhD students supervised as main supervisor:

Hamidur Rahman
Shaibal Barua

PhD students supervised as assistant supervisor:

Sara Abbaspour
Taha Kahn (former)

MSc theses supervised (or examined):
Thesis TitleStatus
Feature Selection through Artificial Intelligence for EEG Signal Classification available
A Decision Support System for medical diagnosis using Data Mining and Machine Learning available
Activity monitoring in daily life using Shimmer sensing available
Classifying cognitive load using vehicle’s state and driving behaviour signals available
Correlation analysis among EEG, EOG and EMG signals for identification of ocular and muscle activities available
Data-driven actors modelling for road transportation available
Data-driven cognitive load classification system using machine-learning algorithm available
Data-driven Modelling on Powered Two Wheelers using Machine Learning available
Deep Learning based Eye Tracking and Head Movement Detection available
Detect drug abuse by AI processed eye movement data from a smart phone film available
GameAlyzer - a wearables and AI based system to monitor gambling and gaming available
Machine learning based pedestrian event monitoring using IMU and GPS available
Non-Contact Intelligent System to monitor driver’s alcoholic state using Biological Signals available
Artifact handling or filtering noise from the biological sensor signals EEG and ECG selected
Case representation methodology for a scalable Case-Based Reasoning selected
Distributed case retrieval for big data using Spark platform and Case-Based Reasoning selected
Decision support system: Knowledge capture and sharing for Telecom network management in progress
NOISY BIG DATA CLASSIFICATION USING MAPREDUCE DISTRIBUTED FUZZY RANDOM FOREST in progress
A case study on Heart Rate Variability and Finger Temperature to use it in a stress diagnosis system finished
A decision support system for stress diagnosis using ECG signal. finished
An Intelligent Portable Sensor System in Diagnosing Stress finished
Decision Support System for Lung Diseases (DSS) finished
Develop an Automated System for EEG Artifacts Identification finished
Evaluation of jCOLIBRI finished
Feature Extraction From Sensor Data To Represent And Matching Cases For Patient Health Care finished
Individual Stress Diagnosis Using Skin Conductance Sensor Signals finished
Intelligent System for Monitoring Physiological Parameters Using Camera finished
Investigation of Feature Optimization Algorithms for EEG Signal Analysis For Monitoring the Drivers finished
Monitoring of Micro-sleep and Sleepiness for the Drivers Using EEG Signal finished
Multi-Sensor Information Fusion for Classification of Driver's Physiological Sensor Data finished