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:
Artificial Intelligence and 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, machine learning, big data analytics and intelligent monitoring systems.

 
 

 

 
 

[Show all publications]

Latest publications:

Drivers' Sleepiness Classification using Machine Learning with Physiological and Contextual data (Mar 2019)
Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum
First International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2019)

Supervised Learning for Road Junctions Identification using IMU (Mar 2019)
Mohammed Ghaith Altarabichi, Mobyen Uddin Ahmed, Shahina Begum
First International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2019)

A Vision based Indoor Navigation System for Individual with Visual Impairment (Jan 2019)
Mobyen Uddin Ahmed, Mohammed Ghaith Altarabichi, Shahina Begum, Fredrik Ginsberg , Robert Glaes , Magnus Östgren , Hamidur Rahman, Magnus Sörensen
International Conference on Modern Intelligent Systems Concepts (MISC'18)

Automatic Driver Sleepiness Detection using EEG, EOG and Contextual Information (Jan 2019)
Shaibal Barua, Mobyen Uddin Ahmed, Christer Ahlström , Shahina Begum
Expert Systems with Applications (ESWA)

Towards Distributed k-NN similarity for Scalable Case Retrieval (Jul 2018)
Shaibal Barua, Shahina Begum, Mobyen Uddin Ahmed
The Third Workshop on Synergies between CBR and Machine Learning ( CBRML 2018)

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)

PhD students supervised as main supervisor:

Hamidur Rahman
Mir Riyanul Islam
Mohammed Ghaith Altarabichi
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
A systematic review on theoretical aspects of k-nearest neighbour algorithm available
Activity monitoring in daily life using Shimmer sensing 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
Deep learning to classify driving events using GPS data 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
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
Distributed case retrieval for big data using Spark platform and Case-Based Reasoning selected
A case study on Heart Rate Variability and Finger Temperature to use it in a stress diagnosis system finished
Using AI and Statistics on Structured Electronic Patient Records for Clinical Decision Support Systems finished
A decision support system for stress diagnosis using ECG signal. finished
An Intelligent Portable Sensor System in Diagnosing Stress finished
An optimized case matching algorithm in diagnosing the stress patients finished
Case representation methodology for a scalable Case-Based Reasoning finished
Decision Support System for Lung Diseases (DSS) finished
Decision support system: Knowledge capture and sharing for Telecom network management 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
MACHINE LEARNING BASED PEDESTRIAN EVENT MONITORING USING IMU AND GPS 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
NOISY BIG DATA CLASSIFICATION USING MAPREDUCE DISTRIBUTED FUZZY RANDOM FOREST finished