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Publications (10 of 135) Show all publications
Biswas, M., Chowdury, M. S., Nahar, N., Hossain, M. S. & Andersson, K. (2019). A Belief Rule Base Expert System for staging Non-Small Cell Lung Cancer under Uncertainty. In: Proceedings of 2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON): . Paper presented at 2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON).
Open this publication in new window or tab >>A Belief Rule Base Expert System for staging Non-Small Cell Lung Cancer under Uncertainty
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2019 (English)In: Proceedings of 2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON), 2019Conference paper, Published paper (Refereed)
Abstract [en]

Non small cell Lung cancer (NSCLC) is one of the most well-known types of Lung cancer which is reason for cancer related demise in Bangladesh. The early detection stage of NSCLC is required for improving the survival rate by taking proper decision for surgery and radiotherapy. The most common factors for staging NSCLC are age, tumor size, lymph node distance, Metastasis and Co morbidity. Moreover, physicians’ diagnosis is unable to give more reliable outcome due to some uncertainty such as ignorance, incompleteness, vagueness, randomness, imprecision. Belief Rule Base Expert System (BRBES) is fit to deal with above mentioned uncertainty by applying both Belief Rule base and Evidential Reasoning approach .Therefore, this paper represents the architecture, development and interface for staging NSCLC by incorporating belief rule base as well as evidential reasoning with the capability of handling uncertainty. At last, a comparative analysis is added which indicate that the outcomes of proposed expert system is more reliable and efficient than the outcomes generated from traditional human expert as well as Support Vector Machine (SVM) or Fuzzy Rule Base Expert System (FRBES).

Keywords
Non-Small Cell Lung Cancer (NSCLC), Expert System, Uncertainty, Belief rules base, Evidential Reasoning
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-76793 (URN)
Conference
2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)
Available from: 2019-11-20 Created: 2019-11-20 Last updated: 2019-12-06
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
Hossain, M. S., Tuj-Johora, F. & Andersson, K. (2019). A Belief Rule Based Expert System to Assess Hypertension under Uncertainty. Journal of Internet Services and Information Security (JISIS), 9(4), 18-38, Article ID 2.
Open this publication in new window or tab >>A Belief Rule Based Expert System to Assess Hypertension under Uncertainty
2019 (English)In: Journal of Internet Services and Information Security (JISIS), ISSN 2182-2069, E-ISSN 2182-2077, Vol. 9, no 4, p. 18-38, article id 2Article in journal (Refereed) Published
Abstract [en]

Hypertension (HPT) plays an important role, especially for stroke and heart diseases. Therefore, theaccurate assessment of hypertension is becoming a challenge. However, the presence of uncertainties, associated with the signs and symptoms of HPT are becoming crucial to conduct the preciseassessment. This article presents a web-based expert system (web BRBES) by employing beliefrule based (BRB) methodology to assess HPT, allowing the generation of reliable results. In order tocheck the reliability of the system, a comparison has been performed among various approaches suchas decision tree, random forest, artificial neural networks, fuzzy rule based expert system and experts’opinion. Different performance metrics such as confusion matrix, accuracy, root mean square error,area under curve have been used to contrast the reliability of the approaches. The BRBES producesa more reliable result than from the other approaches. Moreover, the user friendliness of the webBRBES found high as obtained by using the PACT (People, Activities, Contexts, Technologies) approach over 200 people.

Place, publisher, year, edition, pages
Seoul, Republic of Korea: Innovative Information Science & Technology Research Group (ISYOU), 2019
Keywords
Expert System, Belief Rule Base, Hypertension, Uncertainty, Knowledge Base
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-77018 (URN)10.22667/JISIS.2019.11.30.018 (DOI)
Note

Validerad;2019;Nivå 1;2019-12-02 (johcin)

Available from: 2019-12-01 Created: 2019-12-01 Last updated: 2019-12-06Bibliographically approved
Nahar, N., Ara, F., Neloy, M. A. A., Barua, V., Hossain, M. S. & Andersson, K. (2019). A Comparative Analysis of the Ensemble Method for Liver Disease Prediction. In: Proceedings of International Conference on Innovation in Engineering and Technology (ICIET): . Paper presented at International Conference on Innovation in Engineering and Technology (ICIET).
Open this publication in new window or tab >>A Comparative Analysis of the Ensemble Method for Liver Disease Prediction
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2019 (English)In: Proceedings of International Conference on Innovation in Engineering and Technology (ICIET), 2019Conference paper, Published paper (Refereed)
Abstract [en]

Early diagnosis of liver disease is very important in order to save human lives and take appropriate measure to control the disease. In several fields, especially in the field of medical science, the ensemble method was successfully applied. This research work uses different ensemble methods to investigate the early detection of liver disease. The selected dataset for this analysis is made up of attributes such as total bilirubin, direct bilirubin, age, sex, total protein, albumin, and globulin ratio. This research mainly aims at measuring and comparing the efficiency of different ensemble methods. AdaBoost, LogitBoost, BeggRep, BeggJ48 and Random Forest are the ensemble method used in this research. The study shows that LogitBoost is the most accurate model than other ensemble approaches.

Keywords
Data Mining, Ensemble Method, Bagging, Boosting, Stacking, Liver Disease
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-76857 (URN)
Conference
International Conference on Innovation in Engineering and Technology (ICIET)
Available from: 2019-11-25 Created: 2019-11-25 Last updated: 2019-12-06
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
Gupta, D., Hossain, E., Hossain, M. S., Andersson, K. & Hossain, S. (2019). A Digital Personal Assistant using Bangla Voice Command Recognition and Face Detection. In: Proceedings of IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things 2019: . Paper presented at IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things 2019.
Open this publication in new window or tab >>A Digital Personal Assistant using Bangla Voice Command Recognition and Face Detection
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2019 (English)In: Proceedings of IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things 2019, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Though speech recognition has been a common interest of researchers over the last couple of decades, but very few works have been done on Bangla voice recognition. In this research, we developed a digital personal assistant for handicapped people which recognizes continuous Bangla voice commands. We employed the cross-correlation technique which compares the energy of Bangla voice commands with prerecorded reference signals. After recognizing a Bangla command, it executes a task specified by that command. Mouse cursor can also be controlled using the facial movement of a user. We validated our model in three different environments (noisy, moderate and noiseless) so that the model can act naturally. We also compared our proposed model with a combined model of MFCC & DTW, and another model which combines crosscorrelation with LPC. Results indicate that the proposed model achieves a huge accuracy and smaller response time comparing to the other two techniques.

Keywords
Bangla voice recognition, Speech recognition, Face detection, Personal assistance, Handicapped people
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-76791 (URN)
Conference
IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things 2019
Available from: 2019-11-20 Created: 2019-11-20 Last updated: 2019-12-06
Uddin Ahmed, T., Hossain, M. S., Alam, M. J. J. & Andersson, K. (2019). An Integrated CNN-RNN Framework to Assess Road Crack. In: Proceedings of the 2019 22nd International Conference on Computer and Information Technology (ICCIT): . Paper presented at 2019 22nd International Conference on Computer and Information Technology (ICCIT).
Open this publication in new window or tab >>An Integrated CNN-RNN Framework to Assess Road Crack
2019 (English)In: Proceedings of the 2019 22nd International Conference on Computer and Information Technology (ICCIT), 2019Conference paper, Published paper (Refereed)
Abstract [en]

Road crack detection and road damage assessment are necessary to support driving safety in a route network. Several unexpected incidents (e.g. road accidents) take place all over the world due to unhealthy road infrastructure. This paper proposes a deep learning approach for road crack detection and road damage assessment which will contribute to the transport sector of a country like Bangladesh where a plethora of roads undergo the crack problem. The proposed model consists of two phases. In the first phase, the model is trained using transfer learning (VGG16) to detect the existence of crack on the road surface. In the second phase, an integrated framework, combining CNN (VGG16) and RNN (LSTM), is trained to classify the crack in one of the two categories-severe and slight. After experiments, the validation accuracies obtained by the proposed models (VGG16 and VGG16-LSTM) are respectively 99.67% and 97.66%.

Keywords
Vgg16, Integrated framework, Validation accuracy, Road crack detection, Damage assessment
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-76655 (URN)
Conference
2019 22nd International Conference on Computer and Information Technology (ICCIT)
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Available from: 2019-11-08 Created: 2019-11-08 Last updated: 2019-11-15
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., Hossain, S. & Andersson, K. (2019). Analyzing Sentiment of Movie Reviews in Bangla by Applying Machine Learning Techniques. In: Proceedings of the International Conference on Bangla Speech and Language Processing: . Paper presented at International Conference on Bangla Speech and Language Processing.
Open this publication in new window or tab >>Analyzing Sentiment of Movie Reviews in Bangla by Applying Machine Learning Techniques
2019 (English)In: Proceedings of the International Conference on Bangla Speech and Language Processing, 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a process of sentiment analysis of movie reviews written in Bangla language. This process can automate the analysis of audience’s reaction towards a specific movie or TV show. With more and more people expressing their opinions openly in the social networking sites, analyzing the sentiment of comments made about a specific movie can indicate how well the movie is being accepted by the general public. The dataset used in this experiment was collected and labeled manually from publicly available comments and posts from social media websites. Using Support Vector Machine algorithm, this model achieves 88.90% accuracy on the test set and by using Long Short Term Memory network [1] the model manages to achieve 82.42% accuracy. Furthermore, a comparison with some other machine learning approaches is presented in this paper.

Keywords
Bangla sentiment analysis, Support Vector Machines, Long Short Term Memory
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-75891 (URN)
Conference
International Conference on Bangla Speech and Language Processing
Available from: 2019-09-07 Created: 2019-09-07 Last updated: 2019-10-22
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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0244-3561

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