| 1. |
- Islam, Md. Zahirul, et al.
(författare)
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Static Hand Gesture Recognition using Convolutional Neural Network with Data Augmentation
- 2019
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Ingår i: Joint 2019 8th International Conference on Informatics, Electronics and Vision (ICIEV) & 3rd International Conference on Imaging, Vision & Pattern Recognition (IVPR) with International Conference on Activity and Behavior Computing (ABC). - : IEEE. ; , s. 324-329
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Konferensbidrag (refereegranskat)abstract
- Computer is a part and parcel in our day to day life and used in various fields. The interaction of human and computer is accomplished by traditional input devices like mouse, keyboard etc. Hand gestures can be a useful medium of human-computer interaction and can make the interaction easier. Gestures vary in orientation and shape from person to person. So, non-linearity exists in this problem. Recent research has proved the supremacy of Convolutional Neural Network (CNN) for image representation and classification. Since, CNN can learn complex and non-linear relationships among images, in this paper, a static hand gesture recognition method using CNN was proposed. Data augmentation like re-scaling, zooming, shearing, rotation, width and height shifting was applied to the dataset. The model was trained on 8000 images and tested on 1600 images which were divided into 10 classes. The model with augmented data achieved accuracy 97.12% which is nearly 4% higher than the model without augmentation (92.87%).
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| 2. |
- Raihan, Shafkat, et al.
(författare)
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A Belief Rule Base Approach to Support Comparison of Digital Speech Signal Features for Parkinson’s Disease Diagnosis
- 2021
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Ingår i: Brain Informatics. - Cham : Springer. ; , s. 388-400
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 3. |
- Raihan, S. M. Shafkat, et al.
(författare)
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A Belief Rule Based Expert System to Diagnose Alzheimer’s Disease Using Whole Blood Gene Expression Data
- 2022
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Ingår i: Brain Informatics: 15th International Conference, BI 2022, Padua, Italy, July 15–17, 2022, Proceedings. - Cham : Springer. - 9783031150364 - 9783031150371 ; , s. 301-315
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Konferensbidrag (refereegranskat)abstract
- 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%
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| 4. |
- Shafkat Raihan, S.M., et al.
(författare)
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A BRBES to Support Diagnosis of COVID-19 Using Clinical and CT Scan Data
- 2022
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Ingår i: Proceedings of the International Conference on Big Data, IoT, and Machine Learning. - Singapore : Springer. ; , s. 483-496
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 5. |
- Ahmed, Faisal, et al.
(författare)
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An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty
- 2021
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Ingår i: Applied Sciences. - Basel, Switzerland : MDPI. - 2076-3417. ; 11:13
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Tidskriftsartikel (refereegranskat)abstract
- Accurate and rapid identification of the severe and non-severe COVID-19 patients is necessary for reducing the risk of overloading the hospitals, effective hospital resource utilization, and minimizing the mortality rate in the pandemic. A conjunctive belief rule-based clinical decision support system is proposed in this paper to identify critical and non-critical COVID-19 patients in hospitals using only three blood test markers. The experts’ knowledge of COVID-19 is encoded in the form of belief rules in the proposed method. To fine-tune the initial belief rules provided by COVID-19 experts using the real patient’s data, a modified differential evolution algorithm that can solve the constraint optimization problem of the belief rule base is also proposed in this paper. Several experiments are performed using 485 COVID-19 patients’ data to evaluate the effectiveness of the proposed system. Experimental result shows that, after optimization, the conjunctive belief rule-based system achieved the accuracy, sensitivity, and specificity of 0.954, 0.923, and 0.959, respectively, while for disjunctive belief rule base, they are 0.927, 0.769, and 0.948. Moreover, with a 98.85% AUC value, our proposed method shows superior performance than the four traditional machine learning algorithms: LR, SVM, DT, and ANN. All these results validate the effectiveness of our proposed method. The proposed system will help the hospital authorities to identify severe and non-severe COVID-19 patients and adopt optimal treatment plans in pandemic situations.
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| 6. |
- Ahmed, Mumtahina, et al.
(författare)
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Explainable Text Classification Model for COVID-19 Fake News Detection
- 2022
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Ingår i: Journal of Internet Services and Information Security (JISIS). - : Innovative Information Science & Technology Research Group. - 2182-2069 .- 2182-2077. ; 12:2, s. 51-69
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 7. |
- Ahmed, Tawsin Uddin, et al.
(författare)
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A Deep Learning Approach with Data Augmentation to Recognize Facial Expressions in Real Time
- 2022
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Ingår i: Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering. - Singapore : Springer Nature. ; , s. 487-500
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 8. |
- Ahmed, Tawsin Uddin, et al.
(författare)
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An Integrated Deep Learning and Belief Rule Base Intelligent System to Predict Survival of COVID-19 Patient under Uncertainty
- 2022
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Ingår i: Cognitive Computation. - : Springer. - 1866-9956 .- 1866-9964. ; 14:2, s. 660-676
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 9. |
- Chowdhury, Rumman Rashid, et al.
(författare)
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Bangla Handwritten Character Recognition using Convolutional Neural Network with Data Augmentation
- 2019
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Ingår i: Joint 2019 8th International Conference on Informatics, Electronics and Vision (ICIEV) & 3rd International Conference on Imaging, Vision & Pattern Recognition (IVPR) with International Conference on Activity and Behavior Computing (ABC). - : IEEE. ; , s. 318-323
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 10. |
- Hridoy, Md Rafiul Sabbir, et al.
(författare)
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A Web Based Belief Rule Based Expert System for Assessing Flood Risk
- 2017
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Ingår i: iiWAS'17. - New York : ACM Digital Library. - 9781450352994 ; , s. 434-440
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Konferensbidrag (refereegranskat)abstract
- Natural calamities such as flooding, volcanic eruption, tornado hampers our daily life and causes many sufferings. Flood is one of the most catastrophic among the natural calamities. Assessing flood risk helps us to take necessary steps and save human lives. Several heterogeneous factors are used to assess flood risk on the livelihood of an area. Moreover, several types of uncertainties can be associated with each factor. In this paper, we propose a web based flood risk assessment expert system by combining belief rule base with the capability of reading data and generating web-based output. This paper also introduces a generic RESTful API which can be used without writing the belief rule based expert system from scratch. This expert system will facilitate the monitoring of the various flood risk factors, contributing in increasing the flood risk on livelihood of an area. Eventually, the decision makers should be able to take measures to control those factors and to reduce the risk of flooding in an area. Data for the expert system has been collected from a case study area by conducting interviews.
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