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Publications (10 of 40) Show all publications
Hossain, M. S., Ahmed, M., Raihan, S. M., Sharma, A., Islam, R. U. & Andersson, K. (2023). A Belief Rule Based Expert System to Diagnose Schizophrenia Using Whole Blood DNA Methylation Data. In: Md. Shahriare Satu; Mohammad Ali Moni; M. Shamim Kaiser; Mohammad Shamsul Arefin; Mohammad Shamsul Arefin (Ed.), Machine Intelligence and Emerging Technologies - First International Conference, MIET 2022, Proceedings, part 2: . Paper presented at 1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022, Noakhali, Bangladesh, September 23-25, 2022 (pp. 271-282). Springer Science and Business Media Deutschland GmbH, 1
Open this publication in new window or tab >>A Belief Rule Based Expert System to Diagnose Schizophrenia Using Whole Blood DNA Methylation Data
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2023 (English)In: Machine Intelligence and Emerging Technologies - First International Conference, MIET 2022, Proceedings, part 2 / [ed] Md. Shahriare Satu; Mohammad Ali Moni; M. Shamim Kaiser; Mohammad Shamsul Arefin; Mohammad Shamsul Arefin, Springer Science and Business Media Deutschland GmbH , 2023, Vol. 1, p. 271-282Conference paper, Published paper (Refereed)
Abstract [en]

Schizophrenia is a severe neurological disease where a patient’s perceptions of reality are disrupted. Its symptoms include hallucinations, delusions, and profoundly strange thinking and behavior, which make the patient’s daily functions difficult. Despite identifying genetic variations linked to Schizophrenia, causative genes involved in pathogenesis and expression regulations remain unknown. There is no particular way in life sciences for diagnosing Schizophrenia. Commonly used machine learning and deep learning are data-oriented. They lack the ability to deal with uncertainty in data. Belief Rule Based Expert System (BRBES) methodology addresses various categories of uncertainty in data with evidential reasoning. Previous researches showed the association of DNA methylation (DNAm) with risk of Schizophrenia. Whole blood DNAm data, hence, is useful for smart diagnosis of Scizophrenia. However, to our knowledge, no previous studies have investigated the performance of BRBES to diagnose Schizophrenia. Therefore, in this study, we explore BRBES’ performance in diagnosing Schizophrenia using whole blood DNAm data. BRBES was optimized by gradient-free algorithms due to the limitations of gradient-based optimization. Classification thresholds were optimized to yield better results. Finally, we compared performance to two machine learning models after 5-fold cross-validation where our model achieved the highest average sensitivity (76.8%) among the three.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2023
Series
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, ISSN 1867-8211, E-ISSN 1867-822X ; 491
Keywords
BRBES, disjunctive BRBES, Dna methylation data, Scizophrenia
National Category
Other Computer and Information Science
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-99536 (URN)10.1007/978-3-031-34622-4_21 (DOI)2-s2.0-85164141484 (Scopus ID)978-3-031-34621-7 (ISBN)978-3-031-34622-4 (ISBN)
Conference
1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022, Noakhali, Bangladesh, September 23-25, 2022
Available from: 2023-08-11 Created: 2023-08-11 Last updated: 2023-09-05Bibliographically approved
Jamil, M. N., Hossain, M. S., Islam, R. U. & Andersson, K. (2023). Workload Orchestration in Multi-access Edge Computing Using Belief Rule-Based Approach. IEEE Access, 11, 118002-118023
Open this publication in new window or tab >>Workload Orchestration in Multi-access Edge Computing Using Belief Rule-Based Approach
2023 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 118002-118023Article in journal (Refereed) Published
Abstract [en]

Multi-access Edge Computing (MEC) is a standard network architecture for edge computing, which is proposed to handle enormous computation demands from emerging resource-intensive and latency-sensitive applications and services as well as accommodate Quality of Service (QoS) requirements for ever-growing users through computation offloading. Since the demand of end-users is unknown in a rapidly changing dynamic environment, processing offloaded tasks in a non-optimal server can deteriorate QoS due to high latency and increasing task failures. In order to deal with such a challenge in MEC, a two-stage Belief Rule-Based (BRB) workload orchestrator is proposed to distribute the workload of end-users to optimum computing units, support strict QoS requirements, ensure efficient utilization of computational resources, minimize task failures, and reduce the overall service time. The proposed BRB workload orchestrator decides the optimal execution location for each offloaded task from User Equipment (UE) within the overall MEC architecture based on network conditions, computational resources, and task requirements. EdgeCloudSim simulator is used to conduct comprehensive simulation experiments for evaluating the performance of the proposed BRB orchestrator in contrast to four workload orchestration approaches from the literature with different types of applications. Based on the simulation experiments, the proposed workload orchestrator outperforms state-of-the-art workload orchestration approaches and ensures efficient utilization of computational resources while minimizing task failures and reducing the overall service time.

Place, publisher, year, edition, pages
IEEE, 2023
National Category
Computer Systems
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-101777 (URN)10.1109/access.2023.3326244 (DOI)
Funder
Vinnova, 5G Edge Innovations for Mining (2021-03663)
Note

Validerad;2023;Nivå 2;2023-10-31 (joosat);

Funder: Erasmus Mundus Joint Masters Degree in GrEen NetworkIng And cLoud computing (GENIAL), (610619-EPP-1-2019-1-FR-EPPKA1-JMD-MOB)

CC BY 4.0 License

Available from: 2023-10-26 Created: 2023-10-26 Last updated: 2023-10-31Bibliographically approved
Raihan, S. M., Ahmed, M., Sharma, A., Hossain, M. S., Islam, R. U. & Andersson, K. (2022). A Belief Rule Based Expert System to Diagnose Alzheimer’s Disease Using Whole Blood Gene Expression Data. In: Mufti Mahmud, Jing He, Stefano Vassanelli, André van Zundert, Ning Zhong (Ed.), Brain Informatics: 15th International Conference, BI 2022, Padua, Italy, July 15–17, 2022, Proceedings: . Paper presented at 15th International Conference on Brain Informatics (BI 2022), Padua, Italy, July 15-17, 2022 (pp. 301-315). Springer
Open this publication in new window or tab >>A Belief Rule Based Expert System to Diagnose Alzheimer’s Disease Using Whole Blood Gene Expression Data
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2022 (English)In: Brain Informatics: 15th International Conference, BI 2022, Padua, Italy, July 15–17, 2022, Proceedings / [ed] Mufti Mahmud, Jing He, Stefano Vassanelli, André van Zundert, Ning Zhong, Springer, 2022, p. 301-315Conference paper, Published paper (Refereed)
Abstract [en]

Alzheimer’s disease (AD) is a degenerative neurological disease that is the most common cause of dementia. It is also the fifth-greatest reason for death in adults aged 65 and over. However, there is no accurate way of diagnosing neurological Alzheimer’s disorders in medical research. Blood gene expression analysis offers a realistic option for identifying those at risk of AD. Blood gene expression patterns have previously proved beneficial in diagnosing several brain disorders, despite the blood-brain barrier’s restricted permeability. The most extensively used statistical machine learning and deep learning algorithms are data-driven and do not address data uncertainty. Belief Rule-Based Expert System (BRBES) is an approach that can identify various forms of uncertainty in data and reason using evidential reasoning. No previous research studies have examined BRBES’ performance in diagnosing AD. As a result, this study aims to identify how effective BRBES is at diagnosing Alzheimer’s disease from blood gene expression data. We used a gradient-free technique to optimize the BRBES because prior research had shown the limits of gradient-based optimization. We have also attempted to address the class imbalance problem using BRBES’ consequent utility parameters. Finally, after 5-fold cross-validation, we compared our model to three classic ML models, finding that our model had a greater specificity than the other three models across all folds. The average specificity of our models for all folds was 32%

Place, publisher, year, edition, pages
Springer, 2022
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13406
Keywords
BRBES, Alzheimer’s disease, Gene expression data, Disjunctive BRBES, Class imbalance
National Category
Other Computer and Information Science
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-92962 (URN)10.1007/978-3-031-15037-1_25 (DOI)000878133000025 ()2-s2.0-85136942425 (Scopus ID)978-3-031-15036-4 (ISBN)978-3-031-15037-1 (ISBN)
Conference
15th International Conference on Brain Informatics (BI 2022), Padua, Italy, July 15-17, 2022
Available from: 2022-09-13 Created: 2022-09-13 Last updated: 2023-09-05Bibliographically approved
Shafkat Raihan, S., Islam, R. U., Hossain, M. S. & Andersson, K. (2022). A BRBES to Support Diagnosis of COVID-19 Using Clinical and CT Scan Data. In: Mohammad Shamsul Arefin; M. Shamim Kaiser; Anirban Bandyopadhyay; Md. Atiqur Rahman Ahad; Kanad Ray (Ed.), Proceedings of the International Conference on Big Data, IoT, and Machine Learning: . Paper presented at BIM 2021,Cox’s Bazar, Bangladesh (pp. 483-496). Springer, 95
Open this publication in new window or tab >>A BRBES to Support Diagnosis of COVID-19 Using Clinical and CT Scan Data
2022 (English)In: Proceedings of the International Conference on Big Data, IoT, and Machine Learning / [ed] Mohammad Shamsul Arefin; M. Shamim Kaiser; Anirban Bandyopadhyay; Md. Atiqur Rahman Ahad; Kanad Ray, Springer, 2022, Vol. 95, p. 483-496Conference paper, Published paper (Refereed)
Abstract [en]

In the prevailing COVID-19 pandemic, accurate diagnosis plays a vital role in preventing the mass transmission of the SARS-CoV-2 virus. Especially patients with pneumonia need correct diagnosis for proper treatment of their respiratory distress. However, the current standard diagnosis method, RT-PCR testing has a significant false negative and false positive rate. As alternatives, diagnosis methods based on artificial intelligence can be applied for faster and more accurate diagnosis. Currently, various machine learning and deep learning techniques are being researched on to develop better COVID-19 diagnosis system. However, these approaches do not consider the uncertainty in data. Deep learning approaches use backpropagation. It is an unexplainable black box approach and is prone to problems like catastrophic forgetting. This article applies a belief rule-based expert system (BRBES) for diagnosis of COVID-19 on hematological data and CT scan data of lung tissue infection of adult pneumonia patients. The system is optimized with nature-inspired optimization algorithm—BRBES-based adaptive differential evolution (BRBaDE). This model has been evaluated on a real-world dataset of COVID-19 patients published in a previous work. Also, performance of the BRBaDE has been compared with BRBES optimized with genetic algorithm and MATLAB’s fmincon function where BRBaDE outperformed genetic algorithm and fmincon and showed best accuracy of 73.91%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Place, publisher, year, edition, pages
Springer, 2022
Series
Lecture Notes on Data Engineering and Communications Technologies, ISSN 2367-4512, E-ISSN 2367-4520 ; 95
Keywords
Biomimetics, Computerized tomography, Deep learning, Diseases, Expert systems, Genetic algorithms, Patient treatment, SARS, Belief rule-based expert system, Belief rules, BRBES-based adaptive differential evolution, COVID-19 diagnose, CT-scan, Diagnosis methods, Differential Evolution, Hematological data, Rule-based expert system, Scan data, Diagnosis
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-88522 (URN)10.1007/978-981-16-6636-0_37 (DOI)2-s2.0-85120858165 (Scopus ID)
Conference
BIM 2021,Cox’s Bazar, Bangladesh
Available from: 2021-12-20 Created: 2021-12-20 Last updated: 2023-09-05Bibliographically approved
Ahmed, T. U., Hossain, S., Hossain, M. S., Islam, R. U. & Andersson, K. (2022). A Deep Learning Approach with Data Augmentation to Recognize Facial Expressions in Real Time. In: M. Shamim Kaiser; Kanad Ray; Anirban Bandyopadhyay; Kavikumar Jacob; Kek Sie Long (Ed.), Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering: TCCE 2021. Paper presented at 3rd International Conference on Trends in Cognitive Computation Engineering (TCCE 2021), Johor, Malaysia, October 21-22, 2021 (pp. 487-500). Springer Nature
Open this publication in new window or tab >>A Deep Learning Approach with Data Augmentation to Recognize Facial Expressions in Real Time
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2022 (English)In: Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering: TCCE 2021 / [ed] M. Shamim Kaiser; Kanad Ray; Anirban Bandyopadhyay; Kavikumar Jacob; Kek Sie Long, Springer Nature, 2022, p. 487-500Conference paper, Published paper (Refereed)
Abstract [en]

The enormous use of facial expression recognition in various sectors of computer science elevates the interest of researchers to research this topic. Computer vision coupled with deep learning approach formulates a way to solve several real-world problems. For instance, in robotics, to carry out as well as to strengthen the communication between expert systems and human or even between expert agents, it is one of the requirements to analyze information from visual content. Facial expression recognition is one of the trending topics in the area of computer vision. In our previous work, a facial expression recognition system is delivered which can classify an image into seven universal facial expressions—angry, disgust, fear, happy, neutral, sad, and surprise. This is the extension of our previous research in which a real-time facial expression recognition system is proposed that can recognize a total of ten facial expressions including the previous seven facial expressions and additional three facial expressions—mockery, think, and wink from video streaming data. After model training, the proposed model has been able to gain high validation accuracy on a combined facial expression dataset. Moreover, the real-time validation of the proposed model is also promising.

Place, publisher, year, edition, pages
Springer Nature, 2022
Series
Lecture Notes in Networks and Systems, ISSN 2367-3370, E-ISSN 2367-3389 ; 348
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-89921 (URN)10.1007/978-981-16-7597-3_40 (DOI)2-s2.0-85126247773 (Scopus ID)
Conference
3rd International Conference on Trends in Cognitive Computation Engineering (TCCE 2021), Johor, Malaysia, October 21-22, 2021
Note

ISBN för värdpublikation: 978-981-16-7596-6, 978-981-16-7597-3

Available from: 2022-03-28 Created: 2022-03-28 Last updated: 2023-09-05Bibliographically approved
Kabir, S., Islam, R. U., Hossain, M. S. & Andersson, K. (2022). An Integrated Approach of Belief Rule Base and Convolutional Neural Network to Monitor Air Quality in Shanghai. Code Ocean
Open this publication in new window or tab >>An Integrated Approach of Belief Rule Base and Convolutional Neural Network to Monitor Air Quality in Shanghai
2022 (English)Data set
Abstract [en]

Accurate monitoring of air quality can reduce its adverse impact on earth. Ground-level sensors can provide fine particulate matter (PM2.5) concentrations and ground images. But, such sensors have limited spatial coverage and require deployment cost. PM2.5 can be estimated from satellite-retrieved Aerosol Optical Depth (AOD) too. However, AOD is subject to uncertainties associated with its retrieval algorithms and constrain the spatial resolution of estimated PM2.5. AOD is not retrievable under cloudy weather as well. In contrast, satellite images provide continuous spatial coverage with no separate deployment cost. Accuracy of monitoring from such satellite images is hindered due to uncertainties of sensor data of relevant environmental parameters, such as, relative humidity, temperature, wind speed and wind direction . Belief Rule Based Expert System (BRBES) is an efficient algorithm to address these uncertainties. Convolutional Neural Network (CNN) is suitable for image analytics. Hence, we propose a novel model by integrating CNN with BRBES to monitor air quality from satellite images with improved accuracy. We customized CNN and optimized BRBES to increase monitoring accuracy further. An obscure image has been differentiated between polluted air and cloud based on relationship of PM2.5 with relative humidity. Valid environmental data (temperature, wind speed and wind direction) have been adopted to further strengthen the monitoring performance of our proposed model. Three-year observation data (satellite images and environmental parameters) from 2014 to 2016 of Shanghai have been employed to analyze and design our proposed model.

Place, publisher, year
Code Ocean, 2022
Keywords
Artificial Intelligence, Convolutional Neural Network, Air Quality, Expert System, Nonlinear Model Predictive Control, Uncertainty
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-93419 (URN)10.24433/CO.8230207.v1 (DOI)
Note

Code: MIT license; Data: No Rights Reserved (CC0)

Available from: 2022-10-04 Created: 2022-10-04 Last updated: 2023-09-05Bibliographically approved
Kabir, S., Islam, R. U., Shahadat Hossain, M. & Andersson, K. (2022). An Integrated Approach of Belief Rule Base and Convolutional Neural Network to Monitor Air Quality in Shanghai. Expert systems with applications, 206, Article ID 117905.
Open this publication in new window or tab >>An Integrated Approach of Belief Rule Base and Convolutional Neural Network to Monitor Air Quality in Shanghai
2022 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 206, article id 117905Article in journal (Refereed) Published
Abstract [en]

Accurate monitoring of air quality can reduce its adverse impact on earth. Ground-level sensors can provide fine particulate matter (PM2.5) concentrations and ground images. But, such sensors have limited spatial coverage and require deployment cost. PM2.5 can be estimated from satellite-retrieved Aerosol Optical Depth (AOD) too. However, AOD is subject to uncertainties associated with its retrieval algorithms and constrain the spatial resolution of estimated PM2.5. AOD is not retrievable under cloudy weather as well. In contrast, satellite images provide continuous spatial coverage with no separate deployment cost. Accuracy of monitoring from such satellite images is hindered due to uncertainties of sensor data of relevant enviromental parameters, such as, relative humidity, temperature, wind speed and wind direction . Belief Rule Based Expert System (BRBES) is an efficient algorithm to address these uncertainties. Convolutional Neural Network (CNN) is suitable for image analytics. Hence, we propose a novel model by integrating CNN with BRBES to monitor air quality from satellite images with improved accuracy. We customized CNN and optimized BRBES to increase monitoring accuracy further. An obscure image has been differentiated between polluted air and cloud in our model. Valid environmental data (temperature, wind speed and wind direction) have been adopted to further strengthen the monitoring performance of our proposed model. Three-year observation data (satellite images and environmental parameters) from 2014 to 2016 of Shanghai have been employed to analyze and design our proposed model. The results conclude that the accuracy of our model to monitor PM2.5 of Shanghai is higher than only CNN and other conventional Machine Learning methods. Real-time validation of our model on near real-time satellite images of April-2021 of Shanghai shows average difference between our calculated PM2.5 concentrations and the actual one within ±5.51.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Air quality monitoring, Belief Rule Based Expert System (BRBES), Convolutional Neural Network (CNN), Uncertainty
National Category
Environmental Sciences Other Computer and Information Science Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-91874 (URN)10.1016/j.eswa.2022.117905 (DOI)000832953800008 ()2-s2.0-85132745326 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-07-05 (joosat);

Available from: 2022-06-23 Created: 2022-06-23 Last updated: 2023-09-05Bibliographically approved
Ahmed, T. U., Jamil, M. N., Hossain, M. S., Islam, R. U. & Andersson, K. (2022). An Integrated Deep Learning and Belief Rule Base Intelligent System to Predict Survival of COVID-19 Patient under Uncertainty. Cognitive Computation, 14(2), 660-676
Open this publication in new window or tab >>An Integrated Deep Learning and Belief Rule Base Intelligent System to Predict Survival of COVID-19 Patient under Uncertainty
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2022 (English)In: Cognitive Computation, ISSN 1866-9956, E-ISSN 1866-9964, Vol. 14, no 2, p. 660-676Article in journal (Refereed) Published
Abstract [en]

The novel Coronavirus-induced disease COVID-19 is the biggest threat to human health at the present time, and due to the transmission ability of this virus via its conveyor, it is spreading rapidly in almost every corner of the globe. The unification of medical and IT experts is required to bring this outbreak under control. In this research, an integration of both data and knowledge-driven approaches in a single framework is proposed to assess the survival probability of a COVID-19 patient. Several neural networks pre-trained models: Xception, InceptionResNetV2, and VGG Net, are trained on X-ray images of COVID-19 patients to distinguish between critical and non-critical patients. This prediction result, along with eight other significant risk factors associated with COVID-19 patients, is analyzed with a knowledge-driven belief rule-based expert system which forms a probability of survival for that particular patient. The reliability of the proposed integrated system has been tested by using real patient data and compared with expert opinion, where the performance of the system is found promising.

Place, publisher, year, edition, pages
Springer, 2022
Keywords
COVID-19, Transfer Learning, VGG Net, Validation Accuracy, Belief Rule Base
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-88460 (URN)10.1007/s12559-021-09978-8 (DOI)000730523900001 ()34931129 (PubMedID)2-s2.0-85121357921 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-04-20 (hanlid)

Available from: 2021-12-16 Created: 2021-12-16 Last updated: 2023-09-05Bibliographically approved
Ahmed, M., Hossain, M. S., Islam, R. U. & Andersson, K. (2022). Explainable Text Classification Model for COVID-19 Fake News Detection. Journal of Internet Services and Information Security (JISIS), 12(2), 51-69
Open this publication in new window or tab >>Explainable Text Classification Model for COVID-19 Fake News Detection
2022 (English)In: Journal of Internet Services and Information Security (JISIS), ISSN 2182-2069, E-ISSN 2182-2077, Vol. 12, no 2, p. 51-69Article in journal (Refereed) Published
Abstract [en]

Artificial intelligence has achieved notable advances across many applications, and the field is recently concerned with developing novel methods to explain machine learning models. Deep neural networks deliver the best performance accuracy in different domains, such as text categorization, image classification, and speech recognition. Since the neural network models are black-box types, they lack transparency and explainability in predicting results. During the COVID-19 pandemic, Fake News Detection is a challenging research problem as it endangers the lives of many online users by providing misinformation. Therefore, the transparency and explainability of COVID-19 fake news classification are necessary for building the trustworthiness of model prediction. We proposed an integrated LIME-BiLSTM model where BiLSTM assures classification accuracy, and LIME ensures transparency and explainability. In this integrated model, since LIME behaves similarly to the original model and explains the prediction, the proposed model becomes comprehensible. The performance of this model in terms of explainability is measured by using Kendall’s tau correlation coefficient. We also employ several machine learning models and provide a comparison of their performances. Therefore, we analyzed and compared the computation overhead of our proposed model with the other methods because the model takes the integrated strategy.

Place, publisher, year, edition, pages
Innovative Information Science & Technology Research Group, 2022
Keywords
fake news, COVID-19, Explainable AI, LIME, BiLSTM
National Category
Business Administration Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-92146 (URN)10.22667/JISIS.2022.05.31.051 (DOI)2-s2.0-85132973856 (Scopus ID)
Note

Validerad;2022;Nivå 1;2022-07-13 (joosat);

Available from: 2022-07-13 Created: 2022-07-13 Last updated: 2023-09-05Bibliographically approved
Raihan, S., Zisad, S. N., Islam, R. U., Hossain, M. S. & Andersson, K. (2021). A Belief Rule Base Approach to Support Comparison of Digital Speech Signal Features for Parkinson’s Disease Diagnosis. In: Mufti Mahmud, M Shamim Kaiser, Stefano Vassanelli, Qionghai Dai, Ning Zhong (Ed.), Brain Informatics: 14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings. Paper presented at 14th International Conference on Brain Informatics (BI 2021), virtual, September 17-19, 2021 (pp. 388-400). Springer
Open this publication in new window or tab >>A Belief Rule Base Approach to Support Comparison of Digital Speech Signal Features for Parkinson’s Disease Diagnosis
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2021 (English)In: Brain Informatics: 14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings / [ed] Mufti Mahmud, M Shamim Kaiser, Stefano Vassanelli, Qionghai Dai, Ning Zhong, Springer, 2021, p. 388-400Conference paper, Published paper (Refereed)
Abstract [en]

Parkinson’s disease is a neurological disorder. It affects the structures of the central and peripheral nervous system that control movement. One of the symptoms of Parkinson’s disease is difficulty in speaking. Hence, analysis of speech signal of patients may provide valuable features for diagnosing. Previous works on diagnosis based on speech data have employed machine learning and deep learning techniques. However, these approaches do not address the various uncertainties in data. Belief rule based expert system (BRBES) is an approach that can reason under various forms of data uncertainty. Thus, the main objective of this research is to compare the potential of BRBES on various speech signal features of patients of parkinson’s disease. The research took into account various types of standard speech signal features such MFCCs, TQWTs etc. A BRBES was trained on a dataset of 188 patients of parkinson’s disease and 64 healthy candidates with 5-fold cross validation. It was optimized using an exploitive version of the nature inspired optimization algorithm called BRB-based adaptive differential evolution (BRBaDE). The optimized model performed better than explorative BRBaDE, genetic algorithm and MATLAB’s FMINCON optimization on most of these features. It was also found that for speech based diagnosis of Parkinson’s disease under uncertainty, the features such as Glottis Quotient, Jitter variants, MFCCs, RPDE, DFA and PPE are relatively more suitable. 

Place, publisher, year, edition, pages
Springer, 2021
Series
Lecture Notes in Artificial Intelligence, ISSN 0302-9743, E-ISSN 1611-3349 ; 12960
Keywords
CNN, Speech emotion, RAVDESS, MFCC, Data augmentation
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-87120 (URN)10.1007/978-3-030-86993-9_35 (DOI)000768819800035 ()2-s2.0-85115862334 (Scopus ID)
Conference
14th International Conference on Brain Informatics (BI 2021), virtual, September 17-19, 2021
Note

ISBN för värdpublikation: 978-3-030-86992-2; 978-3-030-86993-9

Available from: 2021-09-20 Created: 2021-09-20 Last updated: 2023-09-05Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3090-7645

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