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Publications (10 of 19) Show all publications
Jamil, M. N., Hossain, M. S., Islam, R. U. & Andersson, K. (2019). A Belief Rule Based Expert System for Evaluating Technological Innovation Capability of High-Tech Firms Under Uncertainty. In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV): . Paper presented at Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 26 - 29 April 2019, Spokane, United States. IEEE
Open this publication in new window or tab >>A Belief Rule Based Expert System for Evaluating Technological Innovation Capability of High-Tech Firms Under Uncertainty
2019 (English)In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Technological innovation capability (TIC) is a complicated and subtle concept which is based on multiple quantitative and qualitative criteria. The cores of a firm’s long-term competitive dominance are defined by technological innovation capability which is the incentive for a firm’s innovation. Various types of uncertainty can be noticed while considering multiple criteria for evaluating TIC. In order to evaluate TIC in a reliable way, a Belief Rule Base (BRB) Expert System can be used to handle both quantitative and qualitative data and their associated uncertainties. In this paper, a RESTful API-based BRB expert system is introduced to evaluate technological innovation capability by taking uncertainties into consideration. This expert system will facilitate firms’ managers to obtain a recapitulation of the TIC evaluation. It will help them to take essential steps to ensure corporate survival and strengthen their weak capabilities continuously to facilitate a competitive advantage. Other users can also use this API to apply BRB for a different domain. However, a comparison between the knowledge-driven approach (BRBES) and several data-driven models has been performed to find out the reliability in evaluating TIC. The result shows that the outcome of BRBES is better than other data-driven approaches.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Technological Innovation Capability, Belief Rule Base, Uncertainty, RESTful API
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-73309 (URN)
Conference
Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 26 - 29 April 2019, Spokane, United States
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Funder
Swedish Research Council, 2014-4251
Available from: 2019-03-25 Created: 2019-03-25 Last updated: 2019-04-02Bibliographically approved
Chowdhury, R. R., Hossain, M. S., Islam, R. U., Andersson, K. & Hossain, S. (2019). Bangla Handwritten Character Recognition using Convolutional Neural Network with Data Augmentation. In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV): . Paper presented at Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 26 - 29 April 2019, Spokane, United States.
Open this publication in new window or tab >>Bangla Handwritten Character Recognition using Convolutional Neural Network with Data Augmentation
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2019 (English)In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a process of Handwritten Character Recognition to recognize and convert images of individual Bangla handwritten characters into electronically editable format, which will create opportunities for further research and can also have various practical applications. The dataset used in this experiment is the BanglaLekha-Isolated dataset [1]. Using Convolutional Neural Network, this model achieves 91.81% accuracy on the alphabets (50 character classes) on the base dataset, and after expanding the number of images to 200,000 using data augmentation, the accuracy achieved on the test set is 95.25%. The model was hosted on a web server for the ease of testing and interaction with the model. Furthermore, a comparison with other machine learning approaches is presented.

Keywords
Convolutional Neural Network, handwritten character recognition, Bangla handwritten characters, Data augmentation
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-73307 (URN)
Conference
Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 26 - 29 April 2019, Spokane, United States
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Funder
Swedish Research Council, 2014-4251
Available from: 2019-03-25 Created: 2019-03-25 Last updated: 2019-04-02Bibliographically approved
Uddin Ahmed, T., Hossain, S., Hossain, M. S., Islam, R. U. & Andersson, K. (2019). Facial Expression Recognition using Convolutional Neural Network with Data Augmentation. In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV): . Paper presented at Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 26 - 29 April 2019, Spokane, United States.
Open this publication in new window or tab >>Facial Expression Recognition using Convolutional Neural Network with Data Augmentation
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2019 (English)In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 2019Conference paper, Published paper (Refereed)
Abstract [en]

Detecting emotion from facial expression has become an urgent need because of its immense applications in artificial intelligence such as human-computer collaboration, data-driven animation, human-robot communication etc. Since it is a demanding and interesting problem in computer vision, several works had been conducted regarding this topic. The objective of this research is to develop a facial expression recognition system based on convolutional neural network with data augmentation. This approach enables to classify seven basic emotions consist of angry, disgust, fear, happy, neutral, sad and surprise from image data. Convolutional neural network with data augmentation leads to higher validation accuracy than the other existing models (which is 96.24%) as well as helps to overcome their limitations.

Keywords
Convolutional neural network, data augmentation, validation accuracy, emotion detection
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-73310 (URN)
Conference
Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 26 - 29 April 2019, Spokane, United States
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Funder
Swedish Research Council, 2014-4251
Available from: 2019-03-25 Created: 2019-03-25 Last updated: 2019-04-02Bibliographically approved
Islam, R. U., Andersson, K. & Hossain, M. S. (2019). Network Intelligence for Enhanced Multi-Access Edge Computing (MEC) in 5G. In: : . Paper presented at 15th Swedish National Computer Networking Workshop (SNCNW 2019), Luleå, June 4-5, 2019 (pp. 13-17).
Open this publication in new window or tab >>Network Intelligence for Enhanced Multi-Access Edge Computing (MEC) in 5G
2019 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

5G networks will enable people and machines to communicate at high speeds and very low latencies, in a reliable way. This opens up opportunities for totally new usage patterns and the fully connected Industry 4.0-enabled enterprise covering the entire value chain from design, production, deployment, to usage of products. 5G will be rolled out across the whole world, including Sweden where the first 5G test network was launched late 2018. One important new feature in 5G is the emerging edge computing capabilities, where users can easily offload computational tasks to the network’s edge very close to the user. At the same time, computational tasks traditionally performed in central nodes can be offloaded from remotely located data centres to the network’s edge. Multi-access Edge Computing (MEC) is a promising network architecture delivering solutions along these lines offering a platform for applications with requirements on low latencies and high reliability. This paper targets this environment with a novel Belief-rule-based (BRB) unsupervised learning algorithm for clustering helping 5G applications to take intelligent decisions on software deployment. The scenarios consist of different combinations of numbers of users and connections and mobility patterns. The target environment is built up using a three-tier structure with a container-based solution where software components can easily be spread around the network.

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ltu:diva-74031 (URN)
Conference
15th Swedish National Computer Networking Workshop (SNCNW 2019), Luleå, June 4-5, 2019
Available from: 2019-05-24 Created: 2019-05-24 Last updated: 2019-08-12
Islam, M. Z. Z., Hossain, M. S., Islam, R. U. & Andersson, K. (2019). Static Hand Gesture Recognition using Convolutional Neural Network with Data Augmentation. In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV): . Paper presented at Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 26 - 29 April 2019, Spokane, United States. IEEE
Open this publication in new window or tab >>Static Hand Gesture Recognition using Convolutional Neural Network with Data Augmentation
2019 (English)In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Computer is a part and parcel in our day to day life and used in various fields. The interaction of human and computer is accomplished by traditional input devices like mouse, keyboard etc. Hand gestures can be a useful medium of human-computer interaction and can make the interaction easier. Gestures vary in orientation and shape from person to person. So, non-linearity exists in this problem. Recent research has proved the supremacy of Convolutional Neural Network (CNN) for image representation and classification. Since, CNN can learn complex and non-linear relationships among images, in this paper, a static hand gesture recognition method using CNN was proposed. Data augmentation like re-scaling, zooming, shearing, rotation, width and height shifting was applied to the dataset. The model was trained on 8000 images and tested on 1600 images which were divided into 10 classes. The model with augmented data achieved accuracy 97.12% which is nearly 4% higher than the model without augmentation (92.87%).

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Convolutional Neural Network, Static hand gestures recognition, Data augmentation.
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-73308 (URN)
Conference
Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 26 - 29 April 2019, Spokane, United States
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Funder
Swedish Research Council, 2014-4251
Available from: 2019-03-25 Created: 2019-03-25 Last updated: 2019-04-02Bibliographically approved
Monrat, A. A., Islam, R. U., Hossain, M. S. & Andersson, K. (2018). A Belief Rule Based Flood Risk Assessment Expert System using Real Time Sensor Data Streaming. In: Proveedings of the 43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops): . Paper presented at 43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops), Chicago, October 1-4, 2018 (pp. 8-45). Piscataway, NJ: IEEE Computer Society
Open this publication in new window or tab >>A Belief Rule Based Flood Risk Assessment Expert System using Real Time Sensor Data Streaming
2018 (English)In: Proveedings of the 43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops), Piscataway, NJ: IEEE Computer Society, 2018, p. 8-45Conference paper, Published paper (Refereed)
Abstract [en]

Among the various natural calamities, flood is considered one of the most catastrophic natural hazards, which has a significant impact on the socio-economic lifeline of a country. The Assessment of flood risks facilitates taking appropriate measures to reduce the consequences of flooding. The flood risk assessment requires Big data which are coming from different sources, such as sensors, social media, and organizations. However, these data sources contain various types of uncertainties because of the presence of incomplete and inaccurate information. This paper presents a Belief rule-based expert system (BRBES) which is developed in Big data platform to assess flood risk in real time. The system processes extremely large dataset by integrating BRBES with Apache Spark while a web-based interface has developed allowing the visualization of flood risk in real time. Since the integrated BRBES employs knowledge driven learning mechanism, it has been compared with other data-driven learning mechanisms to determine the reliability in assessing flood risk. The integrated BRBES produces reliable results in comparison to other data-driven approaches. Data for the expert system has been collected by considering different case study areas of Bangladesh to validate the system.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Computer Society, 2018
Keywords
Belief Rule Base, Flood risk assessment, Uncertainty, Expert systems, Sensor data streaming, Big data
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-70318 (URN)10.1109/LCNW.2018.8628607 (DOI)000461284400006 ()2-s2.0-85062868652 (Scopus ID)
Conference
43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops), Chicago, October 1-4, 2018
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Funder
Swedish Research Council, 2014-4251
Available from: 2018-08-09 Created: 2018-08-09 Last updated: 2019-07-11Bibliographically approved
Islam, R. U., Hossain, M. S. & Andersson, K. (2018). A novel anomaly detection algorithm for sensor data under uncertainty. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 22(5), 1623-1639
Open this publication in new window or tab >>A novel anomaly detection algorithm for sensor data under uncertainty
2018 (English)In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 22, no 5, p. 1623-1639Article in journal (Refereed) Published
Abstract [en]

It is an era of Internet of Things, where various types of sensors, especially wireless, are widely used to collect huge amount of data to feed various systems such as surveillance, environmental monitoring, and disaster management. In these systems, wireless sensors are deployed to make decisions or to predict an event in a real-time basis. However, the accuracy of such decisions or predictions depends upon the reliability of the sensor data. Unfortunately, erroneous data are received from the sensors. Consequently, it hampers the appropriate operations of the mentioned systems, especially in making decisions and prediction. Therefore, the detection of anomaly that exists with the sensor data drew significant attention and hence, it needs to be filtered before feeding a system to increase its reliability in making decisions or prediction. There exists various sensor anomaly detection algorithms, but few of them are able to address the uncertain phenomenon, associated with the sensor data. If these uncertain phenomena cannot be addressed by the algorithms, the filtered data into the system will not be able to increase the reliability of the decision-making process. These uncertainties may be due to the incompleteness, ignorance, vagueness, imprecision and ambiguity. Therefore, in this paper we propose a new belief-rule-based association rule (BRBAR) with the ability to handle the various types of uncertainties as mentioned.The reliability of this novel algorithm has been compared with other existing anomaly detection algorithms such as Gaussian, binary association rule and fuzzy association rule by using sensor data from various domains such as rainfall, temperature and cancer cell data. Receiver operating characteristic curves are used for comparing the performance of our proposed BRBAR with the aforementioned algorithms. The comparisons demonstrate that BRBAR is more accurate and reliable in detecting anomalies from sensor data under uncertainty. Hence, the use of such algorithm to feed the decision-making systems could be beneficial. Therefore, we have used this algorithm to feed appropriate sensor data to our recently developed belief-rule-based expert system to predict flooding in an area. Consequently, the reliability and the accuracy of the flood prediction system increase significantly. Such novel algorithm (BRBAR) can be used in other areas of applications. 

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Internet of Things, Wireless sensor networks, Anomaly detection, Flood prediction, Belief-rule-based expert systems
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-60360 (URN)10.1007/s00500-016-2425-2 (DOI)000426566400022 ()
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Funder
Swedish Research Council, 2014-4251
Note

Validerad;2018;Nivå 2;2018-03-05 (andbra)

Available from: 2016-11-12 Created: 2016-11-12 Last updated: 2019-09-13Bibliographically approved
Monrat, A. A., Islam, R. U., Hossain, M. S. & Andersson, K. (2018). Challenges and Opportunities of Using Big Data for Assessing Flood Risks. In: Mohammed M. Alani, Hissam Tawfik, Mohammed Saeed, Obinna Anya (Ed.), Applications of Big Data Analytics: Trends, Issues, and Challenges (pp. 31-42). Cham: Springer
Open this publication in new window or tab >>Challenges and Opportunities of Using Big Data for Assessing Flood Risks
2018 (English)In: Applications of Big Data Analytics: Trends, Issues, and Challenges / [ed] Mohammed M. Alani, Hissam Tawfik, Mohammed Saeed, Obinna Anya, Cham: Springer, 2018, p. 31-42Chapter in book (Refereed)
Abstract [en]

Among the various natural calamities, flood is considered one of the most catastrophic natural hazards, which has disastrous impact on the socioeconomic lifeline of a country. Nowadays, business organizations are using Big Data to improve their strategies and operations for revealing patterns and market trends to increase revenues. Eventually, the crisis response teams of a country have turned their interest to explore the potentialities of Big Data in managing disaster risks such as flooding. The reason for this is that during flooding, crisis response teams need to take decisions based on the huge amount of incomplete and inaccurate information, which are mainly coming from three major sources, including people, machines, and organizations. Hence, Big Data technologies can be used to monitor and to determine the people exposed to the risks of flooding in real time. This could be achieved by analyzing and processing sensor data streams coming from various sources as well as data collected from other sources such as Twitter, Facebook, and satellite and also from disaster organizations of a country by using Big Data technologies. Therefore, this chapter explores the challenges, the opportunities, and the methods, required to leverage the potentiality of Big Data to assess and predict the risk of flooding.

Place, publisher, year, edition, pages
Cham: Springer, 2018
Keywords
Big Data, Sensor streaming, Real time, Flooding, Risk assessment
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-70155 (URN)10.1007/978-3-319-76472-6 (DOI)978-3-319-76471-9 (ISBN)978-3-319-76472-6 (ISBN)
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Funder
Swedish Research Council, 2014-4251
Available from: 2018-07-24 Created: 2018-07-24 Last updated: 2018-08-09Bibliographically approved
Hridoy, M. R., Islam, R. U., Hossain, M. S. & Andersson, K. (2017). A Web Based Belief Rule Based Expert System for Assessing Flood Risk. In: iiWAS'17: Proceedings of the 19th International Conference on Information Integration and Web-based Applications & Services. Paper presented at 19th International Conference on Information Integration and Web-based Applications & Services (iiWAS2017), Salzburg, Austria, 4-6 december 2017 (pp. 434-440). New York: ACM Digital Library
Open this publication in new window or tab >>A Web Based Belief Rule Based Expert System for Assessing Flood Risk
2017 (English)In: iiWAS'17: Proceedings of the 19th International Conference on Information Integration and Web-based Applications & Services, New York: ACM Digital Library, 2017, p. 434-440Conference paper, Published paper (Refereed)
Abstract [en]

Natural calamities such as flooding, volcanic eruption, tornado hampers our daily life and causes many sufferings. Flood is one of the most catastrophic among the natural calamities. Assessing flood risk helps us to take necessary steps and save human lives. Several heterogeneous factors are used to assess flood risk on the livelihood of an area. Moreover, several types of uncertainties can be associated with each factor. In this paper, we propose a web based flood risk assessment expert system by combining belief rule base with the capability of reading data and generating web-based output. This paper also introduces a generic RESTful API which can be used without writing the belief rule based expert system from scratch. This expert system will facilitate the monitoring of the various flood risk factors, contributing in increasing the flood risk on livelihood of an area. Eventually, the decision makers should be able to take measures to control those factors and to reduce the risk of flooding in an area. Data for the expert system has been collected from a case study area by conducting interviews.

Place, publisher, year, edition, pages
New York: ACM Digital Library, 2017
Keywords
Belief Rule Based Expert System, Uncertainty, Flood Risk Assessment, RESTful API, Web Based Application
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-66764 (URN)10.1145/3151759.3151807 (DOI)2-s2.0-85044285529 (Scopus ID)978-1-4503-5299-4 (ISBN)
Conference
19th International Conference on Information Integration and Web-based Applications & Services (iiWAS2017), Salzburg, Austria, 4-6 december 2017
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Available from: 2017-11-27 Created: 2017-11-27 Last updated: 2018-07-23Bibliographically approved
Ul Islam, R., Andersson, K. & Hossain, M. S. (2017). Heterogeneous Wireless Sensor Networks Using CoAP and SMS to Predict Natural Disasters. In: Proceedings of the 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS): The 8th IEEE INFOCOM International Workshop on Mobility Management in the Networks of the Future World (MobiWorld'17),. Paper presented at The 2017 IEEE Conference on Computer Communications, Atlanta, GA, 1 May 2017 (pp. 30-35). Piscataway, NJ: IEEE Communications Society
Open this publication in new window or tab >>Heterogeneous Wireless Sensor Networks Using CoAP and SMS to Predict Natural Disasters
2017 (English)In: Proceedings of the 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS): The 8th IEEE INFOCOM International Workshop on Mobility Management in the Networks of the Future World (MobiWorld'17),, Piscataway, NJ: IEEE Communications Society, 2017, p. 30-35Conference paper, Published paper (Refereed)
Abstract [en]

Even in the 21 st century human is still handicapped with natural disaster. Flood is one of the most catastrophic natural disasters. Early warnings help people to take necessary steps to save human lives and properties. Sensors can be used to provide more accurate early warnings due to possibilities of capturing more detail data of surrounding nature. Recent advantages in protocol standardization and cost effectiveness of sensors it is possible to easily deploy and manage sensors in large scale. In this paper, a heterogeneous wireless sensor network is proposed and evaluated to predict natural disaster like flood. In this network CoAP is used as a unified application layer protocol for exchanging sensor data. Therefore, CoAP over SMS protocol is used for exchanging sensor data. Furthermore, the effectiveness of the heterogeneous wireless sensor network for predicting natural disaster is presented in this paper.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2017
Series
IEEE Conference on Computer Communications Workshops, ISSN 2159-4228
Keywords
WSN, CoAP, IEEE 802.15.4, 6LoWPAN, SMS, Belief Rule Base Expert System
National Category
Computer Sciences
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-63201 (URN)10.1109/INFCOMW.2017.8116348 (DOI)978-1-5386-2784-6 (ISBN)
Conference
The 2017 IEEE Conference on Computer Communications, Atlanta, GA, 1 May 2017
Projects
BRBWSN
Available from: 2017-04-29 Created: 2017-04-29 Last updated: 2018-03-26Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3090-7645

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