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Ahmed, Mobyen Uddin, DrORCID iD iconorcid.org/0000-0003-3802-4721
Alternative names
Biography [swe]

Mobyen Uddin Ahmed is Senior Lecturer/Assistant Professor in Computer Science and Artificial Intelligence at Artificial Intelligence and Intelligent Systems and a member of ESS-H - Embedded Sensor Systems for Health Research Profile. Mobyen has 100+ scientific publication and more than 1173 citations.

He is involved in research and development since 2005 after completing his M.Sc. in Computer Engineering (thesis) from Dalarna University, Sweden. He received his PhD (thesis) in computer science in 2011 from Mälardalen University. He has completed one postdoctoral study between the years 2012 and 2014 in Computer Science and Engineering (Center for Applied Autonomous Sensor Systems) at School of Science and Technology, Örebro University, Sweden

To mention some courses those, I am involved in teaching: Applied Artificial Intelligence, Project in intelligent embedded systemsMachine Learning With Big Data (a distance course for industrial professionals), Databases, Deep learning for industrial imaging (comming), Predictive analytics (comming) etc.

Publications (10 of 96) Show all publications
Barua, S., Ahmed, M. U., Ahlström, C. & Begum, S. (2019). Automatic driver sleepiness detection using EEG, EOG and contextual information. Expert systems with applications, 115, 121-135
Open this publication in new window or tab >>Automatic driver sleepiness detection using EEG, EOG and contextual information
2019 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 115, p. 121-135Article in journal (Refereed) Published
Abstract [en]

The many vehicle crashes that are caused by driver sleepiness each year advocates the development of automated driver sleepiness detection (ADSD) systems. This study proposes an automatic sleepiness classification scheme designed using data from 30 drivers who repeatedly drove in a high-fidelity driving simulator, both in alert and in sleep deprived conditions. Driver sleepiness classification was performed using four separate classifiers: k-nearest neighbours, support vector machines, case-based reasoning, and random forest, where physiological signals and contextual information were used as sleepiness indicators. The subjective Karolinska sleepiness scale (KSS) was used as target value. An extensive evaluation on multiclass and binary classifications was carried out using 10-fold cross-validation and leave-one-out validation. With 10-fold cross-validation, the support vector machine showed better performance than the other classifiers (79% accuracy for multiclass and 93% accuracy for binary classification). The effect of individual differences was also investigated, showing a 10% increase in accuracy when data from the individual being evaluated was included in the training dataset. Overall, the support vector machine was found to be the most stable classifier. The effect of adding contextual information to the physiological features improved the classification accuracy by 4% in multiclass classification and by and 5% in binary classification.

Place, publisher, year, edition, pages
Elsevier Ltd, 2019
Keywords
Contextual information, Driver sleepiness, Electroencephalography, Electrooculography, Machine learning, Accidents, Case based reasoning, Decision trees, Electrophysiology, Fisher information matrix, Learning systems, Nearest neighbor search, Support vector machines, 10-fold cross-validation, Binary classification, Classification accuracy, Individual Differences, Multi-class classification, Physiological features, Classification (of information)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-40526 (URN)10.1016/j.eswa.2018.07.054 (DOI)000448097700009 ()2-s2.0-85051410923 (Scopus ID)
Available from: 2018-08-23 Created: 2018-08-23 Last updated: 2019-01-10Bibliographically approved
Ahmed, M. U., Begum, S., Catalina, C. A., Limonad, L., Hök, B. & Flumeri, G. D. (2018). Cloud-based Data Analytics on Human Factor Measurement to Improve Safer Transport. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France (pp. 101-106).
Open this publication in new window or tab >>Cloud-based Data Analytics on Human Factor Measurement to Improve Safer Transport
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2018 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 101-106Conference paper, Published paper (Refereed)
Abstract [en]

Improving safer transport includes individual and collective behavioural aspects and their interaction. A system that can monitor and evaluate the human cognitive and physical capacities based on human factor measurement is often beneficial to improve safety in driving condition. However, analysis and evaluation of human factor measurement i.e. Demographics, Behavioural and Physiological in real-time is challenging. This paper presents a methodology for cloud-based data analysis, categorization and metrics correlation in real-time through a H2020 project called SimuSafe. Initial implementation of this methodology shows a step-by-step approach which can handle huge amount of data with variation and verity in the cloud.

Keywords
SimuSafe, safer transport, data-analysis, big data, human factor
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-37085 (URN)10.1007/978-3-319-76213-5_14 (DOI)2-s2.0-85042536073 (Scopus ID)9783319762128 (ISBN)
Conference
4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France
Projects
SimuSafe : Simulator of Behavioural Aspects for Safer Transport
Funder
EU, Horizon 2020, 723386
Available from: 2017-10-27 Created: 2017-10-27 Last updated: 2018-12-13Bibliographically approved
Rahman, H., Ahmed, M. U. & Begum, S. (2018). Deep Learning based Person Identification using Facial Images. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France (pp. 111-115).
Open this publication in new window or tab >>Deep Learning based Person Identification using Facial Images
2018 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 111-115Conference paper, Published paper (Refereed)
Abstract [en]

Person identification is an important task for many applications for example in security. A person can be identified using finger print, vocal sound, facial image or even by DNA test. However, Person identification using facial images is one of the most popular technique which is non-contact and easy to implement and a research hotspot in the field of pattern recognition and machine vision. n this paper, a deep learning based Person identification system is proposed using facial images which shows higher accuracy than another traditional machine learning, i.e. Support Vector Machine.

Keywords
Face recognition, Person Identification, Deep Learning.
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-37091 (URN)10.1007/978-3-319-76213-5_17 (DOI)2-s2.0-85042545019 (Scopus ID)9783319762128 (ISBN)
Conference
4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France
Projects
SafeDriver: A Real Time Driver's State Monitoring and Prediction System
Available from: 2017-10-26 Created: 2017-10-26 Last updated: 2018-03-08Bibliographically approved
Barua, S., Ahmed, M. U. & Begum, S. (2018). Distributed Multivariate Physiological Signal Analytics for Driver´s Mental State Monitoring. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France (pp. 26-33).
Open this publication in new window or tab >>Distributed Multivariate Physiological Signal Analytics for Driver´s Mental State Monitoring
2018 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 26-33Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a distributed data analytics approach for drivers’ mental state monitoring using multivariate physiological signals. Driver’s mental states such as cognitive distraction, sleepiness, stress, etc. can be fatal contributing factors and to prevent car crashes these factors need to be understood. Here, a cloud-based approach with heterogeneous sensor sources that generates extremely large data sets of physiological signals need to be handled and analyzed in a big data scenario. In the proposed physiological big data analytics approach, for driver state monitoring, heterogeneous data coming from multiple sources i.e., multivariate physiological signals are used, processed and analyzed to aware impaired vehicle drivers. Here, in a distributed big data environment, multi-agent case-based reasoning facilitates parallel case similarity matching and handles data that are coming from single and multiple physiological signal sources.

Keywords
Physiological signals, distributed analytics, case-based reasoning
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-37076 (URN)10.1007/978-3-319-76213-5_4 (DOI)2-s2.0-85042522774 (Scopus ID)9783319762128 (ISBN)
Conference
4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France
Projects
VDM - Vehicle Driver Monitoring
Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2018-03-08Bibliographically approved
Ahmed, M. U., Rahman, H. & Begum, S. (2018). Quality index analysis on camera- A sed R-eak identification considering movements and light illumination. In: Studies in Health Technology and Informatics, vol 249: . Paper presented at 15th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2018; Gjovik; Norway; 12 June 2018 through 14 June 2018 (pp. 84-92). IOS Press
Open this publication in new window or tab >>Quality index analysis on camera- A sed R-eak identification considering movements and light illumination
2018 (English)In: Studies in Health Technology and Informatics, vol 249, IOS Press , 2018, p. 84-92Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a quality index (QI) analysis on R-peak extracted by a camera system considering movements and light illumination. Here, the proposed camera system is compared with a reference system named Shimmer PPG sensor. The study considers five test subjects with a 15 minutes measurement protocol, where the protocol consists of several conditions. The conditions are: Normal sittings, head movements i.e., up/down/left/right/forward/backword, with light on/off and with moving flash on/off. A percentage of corrected R-peaks are calculated based on time difference in milliseconds (MS) between the R-peaks extracted both from camera-based and sensor-based systems. A comparison results between normal, movements, and lighting condition is presented as individual and group wise. Furthermore, the comparison is extended considering gender and origin of the subjects. According to the results, more than 90% R-peaks are correctly identified by the camera system with ±200 MS time differences, however, it decreases with while there is no light than when it is on. At the same time, the camera system shows more 95% accuracy for European than Asian men. 

Place, publisher, year, edition, pages
IOS Press, 2018
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-40196 (URN)10.3233/978-1-61499-868-6-84 (DOI)2-s2.0-85049018248 (Scopus ID)9781614998679 (ISBN)
Conference
15th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2018; Gjovik; Norway; 12 June 2018 through 14 June 2018
Available from: 2018-07-05 Created: 2018-07-05 Last updated: 2018-07-05Bibliographically approved
Ahmed, M. U., Rahman, H. & Begum, S. (2018). Quality Index Analysis on Camera-based R-peak Identification Considering Movements and Light Illumination. In: 15th International Conference on Wearable, Micro & Nano technologies for Personalized Health pHealth2018: . Paper presented at 15th International Conference on Wearable, Micro & Nano technologies for Personalized Health pHealth2018, 12 Jun 2018, Gjövik, Norway.
Open this publication in new window or tab >>Quality Index Analysis on Camera-based R-peak Identification Considering Movements and Light Illumination
2018 (English)In: 15th International Conference on Wearable, Micro & Nano technologies for Personalized Health pHealth2018, 2018Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a quality index (QI) analysis on R-peak extracted by a camera system considering movements and light illumination. Here, the proposed camera system is compared with a reference system named Shimmer PPG sensor. The study considers five test subjects with a 15 minutes measurement protocol, where the protocol consists of several conditions. The conditions are: normal sittings, head movements i.e., up/down/left/right/forward/backword, with light on/off and with moving flash on/off. A percentage of corrected R-peaks are calculated based on time difference in milliseconds (MS) between the R-peaks extracted both from camera-based and sensor-based systems. A comparison results between normal, movements, and lighting condition is presented as individual and group wise. Furthermore, the comparison is extended considering gender and origin of the subjects. According to the results, more than 90% R-peaks are correctly identified by the camera system with ?200 MS time differences, however, it decreases with while there is no light than when it is on. At the same time, the camera system shows more 95% accuracy for European than Asian men.

Keywords
Quality Index Analysis, Camera-based system, R-peak Identification
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-39258 (URN)
Conference
15th International Conference on Wearable, Micro & Nano technologies for Personalized Health pHealth2018, 12 Jun 2018, Gjövik, Norway
Projects
HR R-peak detection quality index analysis
Available from: 2018-05-23 Created: 2018-05-23 Last updated: 2018-05-23Bibliographically approved
Ahmed, M. U., Andersson, P., Andersson, T., Tomas Aparicio, E., Baaz, H., Barua, S., . . . Zambrano, J. (2018). Real-time Biomass Characterization in Energy Conversion Processes using Near Infrared Spectroscopy - A Machine Learning Approach. In: Elsevier (Ed.), “Innovative Solutions for Energy Transitions”: . Paper presented at International Conference on Applied Energy, 2018.
Open this publication in new window or tab >>Real-time Biomass Characterization in Energy Conversion Processes using Near Infrared Spectroscopy - A Machine Learning Approach
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2018 (English)In: “Innovative Solutions for Energy Transitions” / [ed] Elsevier, 2018Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

The aim of this work is to apply and evaluate different chemometric approaches employing several machine learning techniques in order to characterize the moisture content in biomass from data obtained by Near Infrared (NIR) spectroscopy. The approaches include three main parts: a) data pre-processing, b) wavelength selection and c) development of a regression model enabling moisture content measurement. Standard Normal Variate (SNV), Multiplicative Scatter Correction and Savitzky-Golay first (SG1) and second (SG2) derivatives and its combinations were applied for data pre-processing. Genetic algorithm (GA) and iterative PLS (iPLS) were used for wavelength selection. Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and traditional Partial Least Squares (PLS) regression, were employed as machine learning regression methods. Results shows that SNV combined with SG1 first derivative performs the best in data pre-processing. The GA is the most effective methods for variable selection and GPR achieved a high accuracy in regression modeling while having low demands on computation time. Overall, the machine learning techniques demonstrate a great potential to be used in future NIR spectroscopy applications.

Keywords
Artificial Neural Netwrok; Chemometrics; Gaussian Process Regression; Multiplicative Scatter Correction; Standard Normal Variate; Support Vector Regression; Partial Least Squares; Savitzky-Golay derivatives
National Category
Environmental Engineering Environmental Biotechnology Industrial Biotechnology
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-40396 (URN)
Conference
International Conference on Applied Energy, 2018
Projects
FUDIPO
Funder
EU, Horizon 2020, 723523
Available from: 2018-08-21 Created: 2018-08-21 Last updated: 2018-12-10Bibliographically approved
Ahmed, M. U., Fotouhi, H., Köckemann, U., Lindén, M., Tomasic, I., Tsiftes, N. & Voigt, T. (2018). Run-Time Assurance for the E-care@home System. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France (pp. 107-110).
Open this publication in new window or tab >>Run-Time Assurance for the E-care@home System
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2018 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 107-110Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents the design and implementation of the software for a run-time assurance infrastructure in the E-care@home system. An experimental evaluation is conducted to verify that the run-time assurance infrastructure is functioning correctly, and to enable detecting performance degradation in experimental IoT network deployments within the context of E-care@home.

National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-37086 (URN)10.1007/978-3-319-76213-5_15 (DOI)2-s2.0-85042521264 (Scopus ID)9783319762128 (ISBN)
Conference
4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France
Projects
ESS-H - Embedded Sensor Systems for Health Research Profileecare@home
Available from: 2017-10-30 Created: 2017-10-30 Last updated: 2018-03-08Bibliographically approved
Barua, S., Begum, S. & Ahmed, M. U. (2018). Scalable Framework for Distributed Case-based Reasoning for Big data analytics. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France (pp. 111-114).
Open this publication in new window or tab >>Scalable Framework for Distributed Case-based Reasoning for Big data analytics
2018 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 111-114Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a scalable framework for distributed case-based reasoning methodology to provide actionable knowledge based on historical big amount of data. The framework addresses several challenges, i.e., promptly analyse big data, cross-domain, use-case specific data processing, multi-source case representation, dynamic case-management, uncertainty, check the plausibility of solution after adaptation etc. through its’ five modules architectures. The architecture allows the functionalities with distributed data analytics and intended to provide solutions under different conditions, i.e. data size, velocity, variety etc.

Keywords
Distributed analytics, case-based reasoning
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-37087 (URN)10.1007/978-3-319-76213-5_16 (DOI)2-s2.0-85042527811 (Scopus ID)9783319762128 (ISBN)
Conference
4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France
Projects
VDM - Vehicle Driver Monitoring
Available from: 2017-10-27 Created: 2017-10-27 Last updated: 2018-03-08Bibliographically approved
Barua, S., Begum, S. & Ahmed, M. U. (2018). Towards Distributed k-NN similarity for Scalable Case Retrieval. In: ICCBR 2018: The 26th International Conference on Case-Based Reasoning July, 09th-12th 2018 in Stockholm, Sweden, Workshop Proceedings. Paper presented at XCBR: First Workshop on Case-Based Reasoning for the Explanation of Intelligent Systems. Workshop at the 26th International Conference on Case-Based Reasoning (ICCBR 2018) (pp. 151-160).
Open this publication in new window or tab >>Towards Distributed k-NN similarity for Scalable Case Retrieval
2018 (English)In: ICCBR 2018: The 26th International Conference on Case-Based Reasoning July, 09th-12th 2018 in Stockholm, Sweden, Workshop Proceedings, 2018, p. 151-160Conference paper, Published paper (Refereed)
Abstract [en]

In Big data era, the demand of processing large amount of data posing several challenges. One biggest challenge is that it is no longer possible to process the data in a single machine. Similar challenges can be assumed for case-based reasoning (CBR) approach, where the size of a case library is increasing and constructed using heterogenous data sources. To deal with the challenges of big data in CBR, a distributed CBR system can be developed, where case libraries or cases are distributed over clusters. MapReduce programming framework has the facilities of parallel processing massive amount of data through a distributed system. This paper proposes a scalable case-representation and retrieval approach using distributed k-NN similarity. The proposed approach is considered to be developed using MapReduce programming framework, where cases are distributed in many clusters.

Keywords
Case representation, k-NN similarity, Scalable Case Retrieval, distributed CBR, big data
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-40882 (URN)
Conference
XCBR: First Workshop on Case-Based Reasoning for the Explanation of Intelligent Systems. Workshop at the 26th International Conference on Case-Based Reasoning (ICCBR 2018)
Available from: 2018-09-18 Created: 2018-09-18 Last updated: 2018-09-18Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3802-4721

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