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Publications (10 of 128) 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
Akter, S., Nahar, N., Hossain, M. S. & Andersson, K. (2019). A New Crossover Technique to Improve Genetic Algorithm and Its Application to TSP. In: Proceedings of 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE): . Paper presented at International Conference on Electrical, Computer and Communication Engineering (ECCE 2019), 07-09 February, 2019, Cox's Bazar, Bangladesh.. IEEE, Article ID 18566123.
Open this publication in new window or tab >>A New Crossover Technique to Improve Genetic Algorithm and Its Application to TSP
2019 (English)In: Proceedings of 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), IEEE, 2019, article id 18566123Conference paper, Published paper (Refereed)
Abstract [en]

Optimization problem like Travelling Salesman Problem (TSP) can be solved by applying Genetic Algorithm (GA) to obtain perfect approximation in time. In addition, TSP is considered as a NP-hard problem as well as an optimal minimization problem. Selection, crossover and mutation are the three main operators of GA. The algorithm is usually employed to find the optimal minimum total distance to visit all the nodes in a TSP. Therefore, the research presents a new crossover operator for TSP, allowing the further minimization of the total distance. The proposed crossover operator consists of two crossover point selection and new offspring creation by performing cost comparison. The computational results as well as the comparison with available well-developed crossover operators are also presented. It has been found that the new crossover operator produces better results than that of other cross-over operators.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
TSP, GA, crossover operator, offspring, chromosome, substring
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-72626 (URN)10.1109/ECACE.2019.8679367 (DOI)2-s2.0-85064611070 (Scopus ID)978-1-5386-9111-3 (ISBN)
Conference
International Conference on Electrical, Computer and Communication Engineering (ECCE 2019), 07-09 February, 2019, Cox's Bazar, Bangladesh.
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Funder
Swedish Research Council, 2014-4251
Available from: 2019-01-19 Created: 2019-01-19 Last updated: 2019-05-15Bibliographically approved
Hossain, M. S., Sultana, Z., Nahar, L. & Andersson, K. (2019). An Intelligent System to Diagnose Chikungunya under Uncertainty. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 10(2), 37-54
Open this publication in new window or tab >>An Intelligent System to Diagnose Chikungunya under Uncertainty
2019 (English)In: Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, ISSN 2093-5374, E-ISSN 2093-5382, Vol. 10, no 2, p. 37-54Article in journal (Refereed) Published
Abstract [en]

Chikungunya is a virus-related disease, bring about by the virus called CHIKV that spreads throughmosquito biting. This virus first found in Tanzania, while blood from patients was isolated. Thecommon signs and symptoms, associated with Chikungunya are considered as fever, joint swelling,joint pain, muscle pain and headache. The examination of these signs and symptoms by the physician constitutes the typical preliminary diagnosis of this disease. However, the physician is unable tomeasure them with accuracy. Therefore, the preliminary diagnosis in most of the cases could sufferfrom inaccuracy, which leads to wrong treatment. Hence, this paper introduces the design and implementation of a belief rule based expert system (BRBES) which is capable to represent uncertainknowledge as well as inference under uncertainty. Here, the knowledge is illustrated by employing belief rule base while deduction is carried out by evidential reasoning. The real patient data of250 have been considered to demonstrate the accuracy and the robustness of the expert system. Acomparison has been performed with the results of BRBES and Fuzzy Logic Based Expert System(FLBES) as well as with the expert judgment. Furthermore, the result of BRBES has been contrastedwith various data-driven machine learning approaches, including ANN (Artificial Neural networks)and SVM (Support Vector Machine). The reliability of BRBESs was found better than those of datadriven machine learning approaches. Therefore, the BRBES presented in this paper could enable thephysician to conduct the analysis of Chikungunya more accurately.

Place, publisher, year, edition, pages
Korea: JoWUA, 2019
Keywords
Belief Rule Base, Uncertainty, Evidential Reasoning, Expert System, Chikungunya
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-75300 (URN)10.22667/JOWUA.2019.06.30.037 (DOI)
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Funder
Swedish Research Council, 2014-4251
Note

Validerad;2019;Nivå 1;2019-08-14 (johcin)

Available from: 2019-07-14 Created: 2019-07-14 Last updated: 2019-08-14Bibliographically 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
Islam, R. U., Ruci, X., Hossain, M. S., Andersson, K. & Kor, A.-L. (2019). Capacity Management of Hyperscale Data Centers Using Predictive Modelling. Energies, 12(18), Article ID 3438.
Open this publication in new window or tab >>Capacity Management of Hyperscale Data Centers Using Predictive Modelling
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2019 (English)In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 12, no 18, article id 3438Article in journal (Refereed) Published
Abstract [en]

Big Data applications have become increasingly popular with the emergence of cloud computing and the explosion of artificial intelligence. The increasing adoption of data-intensive machines and services is driving the need for more power to keep the data centers of the world running. It has become crucial for large IT companies to monitor the energy efficiency of their data-center facilities and to take actions on the optimization of these heavy electricity consumers. This paper proposes a Belief Rule-Based Expert System (BRBES)-based predictive model to predict the Power Usage Effectiveness (PUE) of a data center. The uniqueness of this model consists of the integration of a novel learning mechanism consisting of parameter and structure optimization by using BRBES-based adaptive Differential Evolution (BRBaDE), significantly improving the accuracy of PUE prediction. This model has been evaluated by using real-world data collected from a Facebook data center located in Luleå, Sweden. In addition, to prove the robustness of the predictive model, it has been compared with other machine learning techniques, such as an Artificial Neural Network (ANN) and an Adaptive Neuro Fuzzy Inference System (ANFIS), where it showed a better result. Further, due to the flexibility of the BRBES-based predictive model, it can be used to capture the nonlinear dependencies of many variables of a data center, allowing the prediction of PUE with much accuracy. Consequently, this plays an important role to make data centers more energy-efficient.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
learning, differential evolution, belief rule-based expert systems, predictive modelling, data center
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-75875 (URN)10.3390/en12183438 (DOI)2-s2.0-85071916245 (Scopus ID)
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networksPERCCOM
Funder
Swedish Research Council, 2014-4251
Note

Validerad;2019;Nivå 2;2019-09-09 (johcin)

Available from: 2019-09-06 Created: 2019-09-06 Last updated: 2019-09-18Bibliographically approved
Kor, A.-L., Rondeau, E., Andersson, K., Porras, J. & Georges, J.-P. (2019). Education in Green ICT and Control of Smart Systems: A First Hand Experience from the International PERCCOM Masters Programme. In: Proceedings of the 12th International Federation of Automatic Control Symposium on Advances in Control Education (IFAC-ACE 2019): . Paper presented at The 12th International Federation of Automatic Control Symposium on Advances in Control Education (IFAC-ACE 2019) (pp. 1-8). , 52, Article ID 9.
Open this publication in new window or tab >>Education in Green ICT and Control of Smart Systems: A First Hand Experience from the International PERCCOM Masters Programme
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2019 (English)In: Proceedings of the 12th International Federation of Automatic Control Symposium on Advances in Control Education (IFAC-ACE 2019), 2019, Vol. 52, p. 1-8, article id 9Conference paper, Published paper (Refereed)
Abstract [en]

PERCCOM (PERvasive Computing and COMmunications in sustainable development) Masters is the first innovative international programme in Green ICT for educating and equipping new IT engineers with Green IT skills for sustainable digital applications design and implementation. After five years of running the PERCCOM programme, this paper provides an assessment of skills and employability in the context of Green jobs and skills. The paper ends with a list of recommendations for the development of environment related education curricula.

Keywords
Green ICT, Environment Control Systems, Green Jobs, Green Skills, Green Economy, Sustainable Development
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-75338 (URN)10.1016/j.ifacol.2019.08.114 (DOI)000485262600002 ()
Conference
The 12th International Federation of Automatic Control Symposium on Advances in Control Education (IFAC-ACE 2019)
Projects
PERvasive Computing and COMmunications for sustainable development (PERCCOM)
Funder
EU, Horizon 2020, 2013-0231
Available from: 2019-07-17 Created: 2019-07-17 Last updated: 2019-10-01Bibliographically approved
Poirot, V., Ericson, M., Nordberg, M. & Andersson, K. (2019). Energy efficient multi-connectivity algorithms for ultra-dense 5G networks. Wireless networks, 1-16
Open this publication in new window or tab >>Energy efficient multi-connectivity algorithms for ultra-dense 5G networks
2019 (English)In: Wireless networks, ISSN 1022-0038, E-ISSN 1572-8196, p. 1-16Article in journal (Refereed) Epub ahead of print
Abstract [en]

Two radio air interfaces, Evolved-LTE and New Radio, coexist in new 5G systems. New Radio operates in the millimeter band and provides a better bandwidth, but the higher frequencies also imply worse radio conditions. Multi-connectivity, a feature of 5G that allows users to connect to more than one base station simultaneously, can offer the advantages of both interfaces. In this paper, we investigate how multi-connectivity can improve user reliability and the system’s energy efficiency. Five algorithms for secondary cell association are presented and evaluated. We show a decrease in the radio link failure rate of up to 50% at high speeds and improvements of the energy efficiency of up to 20% at low speeds.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Multi connectivity, Energy efficiency, Ultra dense network, 5G, Multi-RAT
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-75301 (URN)10.1007/s11276-019-02056-w (DOI)2-s2.0-85068151739 (Scopus ID)
Available from: 2019-07-14 Created: 2019-07-14 Last updated: 2019-08-22
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
Chowdury, M. S., Bin Emranb, T., Ghosha, S., Pathak, A., Alama, M. M. M., Absar, N., . . . Hossain, M. S. (2019). IoT Based Real-time River Water Quality Monitoring System. Paper presented at The 16th International Conference on Mobile Systems and Pervasive Computing (MobiSPC) August 19-21, 2019, Halifax, Canada. Procedia Computer Science, 155, 161-168
Open this publication in new window or tab >>IoT Based Real-time River Water Quality Monitoring System
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2019 (English)In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 155, p. 161-168Article in journal (Refereed) Published
Abstract [en]

Current water quality monitoring system is a manual system with a monotonous process and is very time-consuming. This paper proposes a sensor-based water quality monitoring system. The main components of Wireless Sensor Network (WSN) include a microcontroller for processing the system, communication system for inter and intra node communication and several sensors. Real-time data access can be done by using remote monitoring and Internet of Things (IoT) technology. Data collected at the apart site can be displayed in a visual format on a server PC with the help of Spark streaming analysis through Spark MLlib, Deep learning neural network models, Belief Rule Based (BRB) system and is also compared with standard values. If the acquired value is above the threshold value automated warning SMS alert will be sent to the agent. The uniqueness of our proposed paper is to obtain the water monitoring system with high frequency, high mobility, and low powered. Therefore, our proposed system will immensely help Bangladeshi populations to become conscious against contaminated water as well as to stop polluting the water.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Water quality monitoring, Sensors, Big Data Analytics System, Internet of Things, Real-time
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-76002 (URN)10.1016/j.procs.2019.08.025 (DOI)
Conference
The 16th International Conference on Mobile Systems and Pervasive Computing (MobiSPC) August 19-21, 2019, Halifax, Canada
Note

Konferensartikel i tidskrift

Available from: 2019-09-14 Created: 2019-09-14 Last updated: 2019-09-20Bibliographically 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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0244-3561

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