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Abstract
Federated learning (FL) refers to a system in which a central aggregator coordinates the efforts of several clients to solve the issues of machine learning. This setting allows the training data to be dispersed in order to protect the privacy of each device. This paper provides an overview of federated learning systems, with a focus on healthcare. FL is reviewed in terms of its frameworks, architectures and applications. It is shown here that FL solves the preceding issues with a shared global deep learning (DL) model via a central aggregator server. Inspired by the rapid growth of FL research, this paper examines recent developments and provides a comprehensive list of unresolved issues. Several privacy methods including secure multiparty computation, homomorphic encryption, differential privacy and stochastic gradient descent are described in the context of FL. Moreover, a review is provided for different classes of FL such as horizontal and vertical FL and federated transfer learning. FL has applications in wireless communication, service recommendation, intelligent medical diagnosis system and healthcare, which we review in this paper. We also present a comprehensive review of existing FL challenges for example privacy protection, communication cost, systems heterogeneity, unreliable model upload, followed by future research directions.
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Affiliation(s)
- Subrato Bharati
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - M. Rubaiyat Hossain Mondal
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Prajoy Podder
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - V.B. Surya Prasath
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA
- Department of Electrical Engineering and Computer Science, University of Cincinnati, OH, USA
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Bharati S, Podder P, Thanh DNH, Prasath VBS. Dementia classification using MR imaging and clinical data with voting based machine learning models. Multimed Tools Appl. [DOI: 10.1007/s11042-022-12754-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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3
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Abstract
This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.
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Affiliation(s)
- M. Rubaiyat Hossain Mondal
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Subrato Bharati
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Prajoy Podder
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
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Khamparia A, Pandey B, Al‐Turjman F, Podder P. An intelligent
IoMT
enabled feature extraction method for early detection of knee arthritis. Expert Systems 2021. [DOI: 10.1111/exsy.12784] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Aditya Khamparia
- Department of Computer Science Babasaheb Bhimrao Ambedkar University, Satellite Center, Amethi Amethi India
| | - Babita Pandey
- Department of Computer Science Babasaheb Bhimrao Ambedkar University Lucknow India
| | - Fadi Al‐Turjman
- Artificial Intelligence Engineering Department Research Center for AI and IoT, Near East University Nicosia Turkey
| | - Prajoy Podder
- Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh
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Abstract
This paper focuses on the application of deep learning (DL) based model in the analysis of novel coronavirus disease (COVID-19) from X-ray images. The novelty of this work is in the development of a new DL algorithm termed as optimized residual network (CO-ResNet) for COVID-19. The proposed CO-ResNet is developed by applying hyperparameter tuning to the conventional ResNet 101. CO-ResNet is applied to a novel dataset of 5,935 X-ray images retrieved from two publicly available datasets. By utilizing resizing, augmentation and normalization and testing different epochs our CO-ResNet was optimized for detecting COVID-19 versus pneumonia with normal healthy lung controls. Different evaluation metrics such as the classification accuracy, F1 score, recall, precision, area under the receiver operating characteristics curve (AUC) are used. Our proposed CO-ResNet obtains consistently best performance in the multi-level data classification problem, including health lung, pneumonia affected lung and COVID-19 affected lung samples. In the experimental evaluation, the detection rate accuracy in discerning COVID-19 is 98.74%, and for healthy normal lungs, pneumonia affected lungs are 92.08% and 91.32% respectively for our CO-ResNet with ResNet101 backbone. Further, our model obtained accuracy values of 83.68% and 82% for healthy normal lungs and pneumonia affected lungs with ResNet152 backbone. Experimental results indicate the potential usage of our new DL driven model for classification of COVID-19 and pneumonia.
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Affiliation(s)
- Subrato Bharati
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Prajoy Podder
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - M. Rubaiyat Hossain Mondal
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - V.B. Surya Prasath
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, USA
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Mondal MRH, Bharati S, Podder P. Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A Review. Curr Med Imaging 2021; 17:1403-1418. [PMID: 34259149 DOI: 10.2174/1573405617666210713113439] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 03/29/2021] [Accepted: 04/08/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND This paper provides a systematic review of the application of artificial intelligence (AI) in the form of machine learning (ML) and deep learning (DL) techniques in fighting against the effects of novel coronavirus disease (COVID-19). OBJECTIVE & METHOD The objective is to perform a scoping review on AI for COVID-19 using preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. A literature search was performed for relevant studies published from 1 January 2020 till 27 March 2021. Out of 4050 research papers available in reputed publishers, a full-text review of 440 articles was done based on the keywords of AI, COVID-19, ML, forecasting, DL, X-ray, and computed tomography (CT). Finally, 52 articles were included in the result synthesis of this paper. As part of the review, different ML regression methods were reviewed first in predicting the number of confirmed and death cases. Secondly, a comprehensive survey was carried out on the use of ML in classifying COVID-19 patients. Thirdly, different datasets on medical imaging were compared in terms of the number of images, number of positive samples and number of classes in the datasets. The different stages of the diagnosis, including preprocessing, segmentation and feature extraction were also reviewed. Fourthly, the performance results of different research papers were compared to evaluate the effectiveness of DL methods on different datasets. RESULTS Results show that residual neural network (ResNet-18) and densely connected convolutional network (DenseNet 169) exhibit excellent classification accuracy for X-ray images, while DenseNet-201 has the maximum accuracy in classifying CT scan images. This indicates that ML and DL are useful tools in assisting researchers and medical professionals in predicting, screening and detecting COVID-19. CONCLUSION Finally, this review highlights the existing challenges, including regulations, noisy data, data privacy, and the lack of reliable large datasets, then provides future research directions in applying AI in managing COVID-19.
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Affiliation(s)
| | - Subrato Bharati
- Institute of ICT, Bangladesh University of Engineering and Technology, Dhaka-1205, Bangladesh
| | - Prajoy Podder
- Institute of ICT, Bangladesh University of Engineering and Technology, Dhaka-1205, Bangladesh
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Podder P, Khamparia A, Mondal MRH, Rahman MA, Bharati S. Forecasting the Spread of COVID-19 and ICU Requirements. Int J Onl Eng 2021; 17:81. [DOI: 10.3991/ijoe.v17i05.20009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
<span lang="EN-US">Since December 2019, the world is fighting against coronavirus disease (COVID-19). This disease is caused by a novel coronavirus termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This work focuses on the applications of machine learning algorithms in the context of COVID-19. Firstly, regression analysis is performed to model the number of confirmed cases and death cases. Our experiments show that autoregressive integrated moving average (ARIMA) can reliably model the increase in the number of confirmed cases and can predict future cases. Secondly, a number of classifiers are used to predict whether a COVID-19 patient needs to be admitted to an intensive care unit (ICU) or semi-ICU. For this, classification algorithms are applied to a dataset having 5644 samples. Using this dataset, the most significant attributes are selected using features selection by ExtraTrees classifier, and Proteina C reativa (mg/dL) is found to be the highest-ranked feature. In our experiments, random forest, logistic regression, support vector machine, XGBoost, stacking and voting classifiers are applied to the top 10 selected attributes of the dataset. Results show that random forest and hard voting classifiers achieve the highest classification accuracy values near 98%, and the highest recall value of 98% in predicting the need for admission into ICU/semi ICU units.</span>
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Rao PMM, Singh SK, Khamparia A, Bhushan B, Podder P. Multi-class Breast Cancer Classification using Ensemble of Pretrained models and Transfer Learning. Curr Med Imaging 2021; 18:409-416. [PMID: 33602102 DOI: 10.2174/1573405617666210218101418] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/21/2020] [Accepted: 01/06/2021] [Indexed: 02/08/2023]
Abstract
AIMS Early detection of breast cancer has reduced many deaths. Earlier CAD systems used to be the second opinion for radiologists and clinicians. Machine learning and deep learning has brought tremendous changes in medical diagnosis and imagining. BACKGROUND Breast cancer is the most commonly occurring cancer in the women and it is the second most common cancer overall. According to the 2018 statistics, there were over 2million cases all over the world. Belgium and Luxembourg have the highest rate of cancer. OBJECTIVE Proposed a method for breast cancer detection using Ensemble learning. 2-class and 8-class classification is performed. METHOD To deal with imbalance classification the authors have proposed an ensemble of pretrained models. RESULT 98.5% training accuracy and 89% of test accuracy are achieved on 8-class classification. And 99.1% and 98% train and test accuracy are achieved on 2 class classification. CONCLUSION it is found that there are high misclassifications in class DC when compared to the other classes, this is due to the imbalance in the dataset. In future, one can increase the size of the datasets or use different methods. In implement this research work, authors have used 2 Nvidia Tesla V100 GPU's in google cloud platform.
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Affiliation(s)
| | - Sanjay Kumar Singh
- School of computer science and engineering, Lovely professional university, Punjab. India
| | - Aditya Khamparia
- School of computer science and engineering, Lovely professional university, Punjab. India
| | - Bharat Bhushan
- Department of CSE, School of Engineering and Technology, Sharda University. India
| | - Prajoy Podder
- Department of ICT, Bangladesh University of Engineering & Technology, Dhaka. Bangladesh
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Khamparia A, Bharati S, Podder P, Gupta D, Khanna A, Phung TK, Thanh DNH. Diagnosis of breast cancer based on modern mammography using hybrid transfer learning. Multidimens Syst Signal Process 2021; 32:747-765. [PMID: 33456204 PMCID: PMC7798373 DOI: 10.1007/s11045-020-00756-7] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 12/09/2020] [Accepted: 12/19/2020] [Indexed: 02/06/2023]
Abstract
Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved.
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Affiliation(s)
- Aditya Khamparia
- School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab India
| | - Subrato Bharati
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, 1205 Bangladesh
| | - Prajoy Podder
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, 1205 Bangladesh
| | - Deepak Gupta
- Maharaja Agrasen Institute of Technology, Delhi, India
| | - Ashish Khanna
- Maharaja Agrasen Institute of Technology, Delhi, India
| | - Thai Kim Phung
- School of Business Information Technology, University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Dang N. H. Thanh
- School of Business Information Technology, University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam
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11
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Bharati S, Podder P, Mondal MRH. Hybrid deep learning for detecting lung diseases from X-ray images. Inform Med Unlocked 2020; 20:100391. [PMID: 32835077 PMCID: PMC7341954 DOI: 10.1016/j.imu.2020.100391] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 06/29/2020] [Accepted: 06/30/2020] [Indexed: 02/08/2023] Open
Abstract
Lung disease is common throughout the world. These include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, fibrosis, etc. Timely diagnosis of lung disease is essential. Many image processing and machine learning models have been developed for this purpose. Different forms of existing deep learning techniques including convolutional neural network (CNN), vanilla neural network, visual geometry group based neural network (VGG), and capsule network are applied for lung disease prediction. The basic CNN has poor performance for rotated, tilted, or other abnormal image orientation. Therefore, we propose a new hybrid deep learning framework by combining VGG, data augmentation and spatial transformer network (STN) with CNN. This new hybrid method is termed here as VGG Data STN with CNN (VDSNet). As implementation tools, Jupyter Notebook, Tensorflow, and Keras are used. The new model is applied to NIH chest X-ray image dataset collected from Kaggle repository. Full and sample versions of the dataset are considered. For both full and sample datasets, VDSNet outperforms existing methods in terms of a number of metrics including precision, recall, F0.5 score and validation accuracy. For the case of full dataset, VDSNet exhibits a validation accuracy of 73%, while vanilla gray, vanilla RGB, hybrid CNN and VGG, and modified capsule network have accuracy values of 67.8%, 69%, 69.5% and 63.8%, respectively. When sample dataset rather than full dataset is used, VDSNet requires much lower training time at the expense of a slightly lower validation accuracy. Hence, the proposed VDSNet framework will simplify the detection of lung disease for experts as well as for doctors.
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Affiliation(s)
- Subrato Bharati
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh
| | - Prajoy Podder
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh
| | - M Rubaiyat Hossain Mondal
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh
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Mondal MRH, Bharati S, Podder P, Podder P. Data analytics for novel coronavirus disease. Inform Med Unlocked 2020; 20:100374. [PMID: 32835073 PMCID: PMC7295495 DOI: 10.1016/j.imu.2020.100374] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 06/10/2020] [Accepted: 06/10/2020] [Indexed: 02/08/2023] Open
Abstract
This paper describes different aspects of novel coronavirus disease (COVID-19), presents visualization of the spread of the infection, and discusses the potential applications of data analytics on this viral infection. Firstly, a literature survey is done on COVID-19 highlighting a number of factors including its origin, its similarity with previous coronaviruses, its transmission capacity, its symptoms, etc. Secondly, data analytics is applied on a dataset of Johns Hopkins University to find out the spread of the viral infection. It is shown here that although the disease started in China in December 2019, the highest number of confirmed cases up to June 04, 2020 is in the USA. Thirdly, the worldwide increase in the number of confirmed cases over time is modelled here using a polynomial regression algorithm with degree 2. Fourthly, classification algorithms are applied on a dataset of 5644 samples provided by Hospital Israelita Albert Einstein of Brazil in order to diagnose COVID-19. It is shown here that multilayer perceptron (MLP), XGBoost and logistic regression can classify COVID-19 patients at an accuracy above 91%. Finally, a discussion is presented on the potential applications of data analytics in several important factors of COVID-19.
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Affiliation(s)
- M Rubaiyat Hossain Mondal
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh
| | - Subrato Bharati
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh
| | - Prajoy Podder
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh
| | - Priya Podder
- Dhaka National Medical College, Dhaka, 1100, Bangladesh
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Bharati S, Podder P. Adaptive PAPR Reduction Scheme for OFDM Using SLM with the Fusion of Proposed Clipping and Filtering Technique in Order to Diminish PAPR and Signal Distortion. Wireless Pers Commun 2020; 113:2271-88. [DOI: 10.1007/s11277-020-07323-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Bharati S, Podder P, Paul PK. Lung cancer recognition and prediction according to random forest ensemble and RUSBoost algorithm using LIDC data. HIS 2019. [DOI: 10.3233/his-190263] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Subrato Bharati
- Department of EEE, Ranada Prasad Shaha University, Narayanganj, Bangladesh
| | - Prajoy Podder
- Department of ECE, Khulna University of Engineering and Technology, Khulna, Bangladesh
| | - Pinto Kumar Paul
- Department of CSE, Daffodil International University, Dhaka, Bangladesh
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Raihan-Al-Masud M, Islam M, Hasan M, Podder P. Capacity Enhancement and Voltage Stability Improvement of Power Transmission Line by Series Compensation. EAI Endorsed Transactions on Energy Web 2019. [DOI: 10.4108/eai.13-7-2018.157034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Podder P, Mehedi Hasan M, Rafiqul Islam M, Sayeed M. Design and Implementation of Butterworth, Chebyshev-I and Elliptic Filter for Speech Signal Analysis. IJCA 2014. [DOI: 10.5120/17195-7390] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Podder P, Zaman Khan T, Haque Khan M, Muktadir Rahman M. Comparative Performance Analysis of Hamming, Hanning and Blackman Window. IJCA 2014. [DOI: 10.5120/16891-6927] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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