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Publications (10 of 114) Show all publications
Akter, S., Nahar, N., Hossain, M. S. & Andersson, K. (2019). 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
Open this publication in new window or tab >>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, 2019Conference 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)
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: 2019-02-05
Hossain, M. S., Al Hasan, A., Guha, S. & Andersson, K. (2018). A Belief Rule Based Expert System to Predict Earthquake under Uncertainty. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 9(2), 26-41
Open this publication in new window or tab >>A Belief Rule Based Expert System to Predict Earthquake under Uncertainty
2018 (English)In: Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, ISSN 2093-5374, E-ISSN 2093-5382, Vol. 9, no 2, p. 26-41Article in journal (Refereed) Published
Abstract [en]

The impact of earthquake is devastating, which has the capability to stop the socio-economic activities of a region within a short span of time. Therefore, an earlier prediction of earthquake could play an important role to save human lives as well as socio-economic activities. The signs of animal behavior along with environmental and chemical changes in nature could be considered as a way to predict the earthquake. These factors cannot be determined accurately because of the presence of different categories of uncertainties. Therefore, this article presents a belief rule based expert system (BRBES) which has the capability to predict earthquake under uncertainty. Historical data of various earthquakes of the world with specific reference to animal behavior as well as environmental and chemical changes have been considered in validating the BRBES. The reliability of our proposed BRBES’s output is measured in comparison with Fuzzy Logic Based Expert System (FLBES) and Artificial Neural Networks (ANN) based system, whereas our BRBES’s results are found more reliable than that of FLBES and ANN. Therefore, this BRBES can be considered to predict the occurrence of an earthquake in a region by taking account of the data, related to the animal, environmental and chemical changes.

Place, publisher, year, edition, pages
JoWUA, 2018
Keywords
Earthquake, Prediction, Expert system, Uncertainty, Belief rule base.
National Category
Natural Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-70025 (URN)10.22667/JOWUA.2018.06.30.026 (DOI)
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 1;2018-08-02 (rokbeg)

Available from: 2018-07-01 Created: 2018-07-01 Last updated: 2018-08-10Bibliographically 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. 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), IEEE Computer Society, 2018Conference 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
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)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: 2019-03-25
Hossain, M. S., Rahaman, S., Mustafa, R. & Andersson, K. (2018). A belief rule-based expert system to assess suspicion of acute coronary syndrome (ACS) under uncertainty. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 22(22), 7571-7586
Open this publication in new window or tab >>A belief rule-based expert system to assess suspicion of acute coronary syndrome (ACS) under uncertainty
2018 (English)In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 22, no 22, p. 7571-7586Article in journal (Refereed) Published
Abstract [en]

Acute coronary syndrome (ACS) is responsible for the obstruction of coronary arteries, resulting in the loss of lives. The onset of ACS can be determined by looking at the various signs and symptoms of a patient. However, the accuracy of ACS determination is often put into question since there exist different types of uncertainties with the signs and symptoms. Belief rule-based expert systems (BRBESs) are widely used to capture uncertain knowledge and to accomplish the task of reasoning under uncertainty by employing belief rule base and evidential reasoning. This article presents the process of developing a BRBES to determine ACS predictability. The BRBES has been validated against the data of 250 patients suffering from chest pain. It is noticed that the outputs created from the BRBES are more dependable than that of the opinion of cardiologists as well as other two expert system tools, namely artificial neural networks and support vector machine. Hence, it can be argued that the BRBES is capable of playing an important role in decision making as well as in avoiding costly laboratory investigations. A procedure to train the system, allowing its enhancement of performance, is also presented.

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Acute coronary syndrome (ACS), Expert system, Belief rule base, Suspicion, Signs and symptoms, Uncertainty
National Category
Computer and Information Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-64893 (URN)10.1007/s00500-017-2732-2 (DOI)000448418300020 ()2-s2.0-85025083671 (Scopus ID)
Projects
BRBWSN
Funder
Swedish Research Council, 2014-4251
Note

Validerad;2018;Nivå 2;2018-10-15 (svasva)

Available from: 2017-07-19 Created: 2017-07-19 Last updated: 2018-12-04Bibliographically 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: 2018-09-14Bibliographically approved
Alam, M. E. E., Kaiser, M. S., Hossain, M. S. & Andersson, K. (2018). An IoT-Belief Rule Base Smart System to Assess Autism. In: Proceedings of the 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018): . Paper presented at The 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018), Dhaka, Bangladesh, September 13-15, 2018 (pp. 671-675). IEEE
Open this publication in new window or tab >>An IoT-Belief Rule Base Smart System to Assess Autism
2018 (English)In: Proceedings of the 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018), IEEE, 2018, p. 671-675Conference paper, Published paper (Refereed)
Abstract [en]

An Internet-of-Things (IoT)-Belief Rule Base (BRB) based hybrid system is introduced to assess Autism spectrum disorder (ASD). This smart system can automatically collect sign and symptom data of various autistic children in realtime and classify the autistic children. The BRB subsystem incorporates knowledge representation parameters such as rule weight, attribute weight and degree of belief. The IoT-BRB system classifies the children having autism based on the sign and symptom collected by the pervasive sensing nodes. The classification results obtained from the proposed IoT-BRB smart system is compared with fuzzy and expert based system. The proposed system outperformed the state-of-the-art fuzzy system and expert system.

Place, publisher, year, edition, pages
IEEE, 2018
Series
International Conference on Electrical Engineering and Information Communication Technology, ISSN 2475-1995
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-70527 (URN)10.1109/CEEICT.2018.8628131 (DOI)000459239000127 ()978-1-5386-8279-1 (ISBN)978-1-5386-8280-7 (ISBN)
Conference
The 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018), Dhaka, Bangladesh, September 13-15, 2018
Available from: 2018-08-21 Created: 2018-08-21 Last updated: 2019-03-11Bibliographically 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
Siddiquee, K. N., Andersson, K., Moreno Arrebola, F. J., Abedin, M. Z. Z. & Hossain, M. S. (2018). Estimation of Signal Coverage and Localization in Wi-Fi Networkswith AODV and OLSR. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 9(3), 11-24, Article ID 2.
Open this publication in new window or tab >>Estimation of Signal Coverage and Localization in Wi-Fi Networkswith AODV and OLSR
Show others...
2018 (English)In: Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, ISSN 2093-5374, E-ISSN 2093-5382, Vol. 9, no 3, p. 11-24, article id 2Article in journal (Refereed) Published
Abstract [en]

For estimation of signal coverage and localization, path loss is the major component for link budget of any communication system. Instead of traditional Doppler shift or Doppler spread techniques, the path loss has been chosen for IEEE 802.11 (Wi-Fi) signals of 2.5 and 5 GHz to measure the signal coverage and localization in this research. A Wi-Fi system was deployed in a MANET (Mobile Adhoc NETwork), involving both mobile and stationary nodes. The Adhoc network was also assessed in a routing environment under AODV and OLSR protocols. The proposal was evaluated using the OPNET Modeler simulation environment.

Place, publisher, year, edition, pages
Seoul, ​Republic of Korea: Innovative Information Science & Technology Research Group (ISYOU), 2018
Keywords
i-voting, signal coverage, localization, distributed scalable wireless networks, MANET, routing protocols, AODV, OLSR
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-71170 (URN)10.22667/JOWUA.2018.09.30.011 (DOI)
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 1;2018-10-19 (marisr)

Available from: 2018-10-10 Created: 2018-10-10 Last updated: 2018-10-19Bibliographically approved
Abedin, M. Z. Z., Siddiquee, K. N., Bhuyan, M. S., Karim, R., Hossain, M. S. & Andersson, K. (2018). Performance Analysis of Anomaly Based Network Intrusion Detection Systems. 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. IEEE Computer Society
Open this publication in new window or tab >>Performance Analysis of Anomaly Based Network Intrusion Detection Systems
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2018 (English)In: Proveedings of the 43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops), IEEE Computer Society, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Because of the increased popularity and fast expansion of the Internet as well as Internet of things, networks are growing rapidly in every corner of the society. As a result, huge amount of data is travelling across the computer networks that lead to the vulnerability of data integrity, confidentiality and reliability. So, network security is a burning issue to keep the integrity of systems and data. The traditional security guards such as firewalls with access control lists are not anymore enough to secure systems. To address the drawbacks of traditional Intrusion Detection Systems (IDSs), artificial intelligence and machine learning based models open up new opportunity to classify abnormal traffic as anomaly with a self-learning capability. Many supervised learning models have been adopted to detect anomaly from networks traffic. In quest to select a good learning model in terms of precision, recall, area under receiver operating curve, accuracy, F-score and model built time, this paper illustrates the performance comparison between Naïve Bayes, Multilayer Perceptron, J48, Naïve Bayes Tree, and Random Forest classification models. These models are trained and tested on three subsets of features derived from the original benchmark network intrusion detection dataset, NSL-KDD. The three subsets are derived by applying different attributes evaluator’s algorithms. The simulation is carried out by using the WEKA data mining tool.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Series
Proceedings of the 43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops)
Keywords
Intrusion detection systems, machine learning, NSL-KDD, feature selection, classification model, performance analysis
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-70317 (URN)2-s2.0-85062829453 (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: 2019-03-25
Zhohov, R., Minovski, D., Johansson, P. & Andersson, K. (2018). Real-time Performance Evaluation of LTE for IIoT. In: Soumaya Cherkaoui (Ed.), Proceedings of the 43rd IEEE Conference on Local Computer Networks (LCN): . Paper presented at 43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops), Chicago, October 1-4, 2018. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Real-time Performance Evaluation of LTE for IIoT
2018 (English)In: Proceedings of the 43rd IEEE Conference on Local Computer Networks (LCN) / [ed] Soumaya Cherkaoui, Institute of Electrical and Electronics Engineers (IEEE), 2018Conference paper, Published paper (Refereed)
Abstract [en]

Industrial Internet of Things (IIoT) is claimed to be a global booster technology for economic development. IIoT brings bulky use-cases with a simple goal of enabling automation, autonomation or just plain digitalization of industrial processes. The abundance of interconnected IoT and CPS generate additional burden on the telecommunication networks, imposing number of challenges to satisfy the key performance requirements. In particular, the QoS metrics related to real-time data exchange for critical machine-to-machine type communication. This paper analyzes a real-world example of IIoT from a QoS perspective, such as remotely operated underground mining vehicle. As part of the performance evaluation, a software tool is developed for estimating the absolute, one-way delay in end-toend transmissions. The measured metric is passed to a machine learning model for one-way delay prediction based on LTE RAN measurements using a commercially available cutting-edge software tool. The achieved results prove the possibility to predict the delay figures using machine learning model with a coefficient of determination up to 90%.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
IIoT, LTE, QoS, delay, jitter, real-time, critical IoT
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-70316 (URN)
Conference
43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops), Chicago, October 1-4, 2018
Available from: 2018-08-09 Created: 2018-08-09 Last updated: 2018-08-13
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0244-3561

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