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Book ; Online ; E-Book: Emerging technologies for combatting pandemics

Mondal, M. Rubaiyat Hossain

AI, IoMT, and analytics

2023

Abstract: This book focuses on both the design and implementation of AI-based approaches for systems that aid in monitoring and combating pandemics such as occurred with COVID-19. These systems are based on the Internet of Medical Things (IoMT), using sensor ... ...

Author's details	edited by M. Rubaiyat Hossain Mondal [and five others]
Abstract	This book focuses on both the design and implementation of AI-based approaches for systems that aid in monitoring and combating pandemics such as occurred with COVID-19. These systems are based on the Internet of Medical Things (IoMT), using sensor networks and other advanced technologies. The book examines different aspects of AI and IoMT.
Keywords	Pandemics/Prevention/Data processing
Subject code	362.19624144
Language	English
Size	1 online resource (311 pages)
Publisher	CRC Press
Publishing place	Boca Raton, Florida ; Abingdon, Oxon
Document type	Book ; Online ; E-Book
Remark	Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
ISBN	1-00-332444-4 ; 1-003-32444-4 ; 1-000-77770-7 ; 1-03-232828-2 ; 978-1-00-332444-7 ; 978-1-003-32444-7 ; 978-1-000-77770-3 ; 978-1-03-232828-7
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Book ; Online ; E-Book: Computational intelligence for managing pandemics

Khamparia, Aditya / Mondal, M. Rubaiyat Hossain / Podder, Prajoy / Bhushan, Bharat / Albuquerque, Victor Hugo C. de / Kumar, Sachin

(Intelligent biomedical data analysis ; 5)

2021

Author's details	edited by Aditya Khamparia, M. Rubaiyat Hossain Mondal, Prajoy Podder, Bharat Bhushan, Victor Hugo C. de Albuquerque, Sachin Kumar
Series title	Intelligent biomedical data analysis ; 5 Intelligent biomedical data analysis (IBDA)
Collection	Intelligent biomedical data analysis (IBDA)
Keywords	COMPUTERS / Intelligence (AI) & Semantics
Language	English
Size	1 Online-Ressource (XIX, 321 Seiten), Illustrationen, Diagramme
Publisher	De Gruyter
Publishing place	Berlin
Publishing country	Germany
Document type	Book ; Online ; E-Book
Remark	Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
HBZ-ID	HT021145790
ISBN	978-3-11-071225-4 ; 978-3-11-071227-8 ; 9783110700206 ; 3-11-071225-3 ; 3-11-071227-X ; 3110700204
DOI	10.1515/9783110712254
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: RBFK cipher

Sohel Rana / M. Rubaiyat Hossain Mondal / Joarder Kamruzzaman

Cybersecurity, Vol 6, Iss 1, Pp 1-

a randomized butterfly architecture-based lightweight block cipher for IoT devices in the edge computing environment

2023 Volume 19

Abstract: Abstract Internet security has become a major concern with the growing use of the Internet of Things (IoT) and edge computing technologies. Even though data processing is handled by the edge server, sensitive data is generated and stored by the IoT ... ...

Abstract	Abstract Internet security has become a major concern with the growing use of the Internet of Things (IoT) and edge computing technologies. Even though data processing is handled by the edge server, sensitive data is generated and stored by the IoT devices, which are subject to attack. Since most IoT devices have limited resources, standard security algorithms such as AES, DES, and RSA hamper their ability to run properly. In this paper, a lightweight symmetric key cipher termed randomized butterfly architecture of fast Fourier transform for key (RBFK) cipher is proposed for resource-constrained IoT devices in the edge computing environment. The butterfly architecture is used in the key scheduling system to produce strong round keys for five rounds of the encryption method. The RBFK cipher has two key sizes: 64 and 128 bits, with a block size of 64 bits. The RBFK ciphers have a larger avalanche effect due to the butterfly architecture ensuring strong security. The proposed cipher satisfies the Shannon characteristics of confusion and diffusion. The memory usage and execution cycle of the RBFK cipher are assessed using the fair evaluation of the lightweight cryptographic systems (FELICS) tool. The proposed ciphers were also implemented using MATLAB 2021a to test key sensitivity by analyzing the histogram, correlation graph, and entropy of encrypted and decrypted images. Since the RBFK ciphers with minimal computational complexity provide better security than recently proposed competing ciphers, these are suitable for IoT devices in an edge computing environment.
Keywords	Avalanche effects ; Block ciphers ; Butterfly architecture ; Edge computing ; FELICS ; IoT ; Computer engineering. Computer hardware ; TK7885-7895 ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	005 ; 303
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	SpringerOpen
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: RVCNet: A hybrid deep neural network framework for the diagnosis of lung diseases.

Alam, Fatema Binte / Podder, Prajoy / Mondal, M Rubaiyat Hossain

PloS one

2023 Volume 18, Issue 12, Page(s) e0293125

Abstract: Early evaluation and diagnosis can significantly reduce the life-threatening nature of lung diseases. Computer-aided diagnostic systems (CADs) can help radiologists make more precise diagnoses and reduce misinterpretations in lung disease diagnosis. ... ...

Abstract	Early evaluation and diagnosis can significantly reduce the life-threatening nature of lung diseases. Computer-aided diagnostic systems (CADs) can help radiologists make more precise diagnoses and reduce misinterpretations in lung disease diagnosis. Existing literature indicates that more research is needed to correctly classify lung diseases in the presence of multiple classes for different radiographic imaging datasets. As a result, this paper proposes RVCNet, a hybrid deep neural network framework for predicting lung diseases from an X-ray dataset of multiple classes. This framework is developed based on the ideas of three deep learning techniques: ResNet101V2, VGG19, and a basic CNN model. In the feature extraction phase of this new hybrid architecture, hyperparameter fine-tuning is used. Additional layers, such as batch normalization, dropout, and a few dense layers, are applied in the classification phase. The proposed method is applied to a dataset of COVID-19, non-COVID lung infections, viral pneumonia, and normal patients' X-ray images. The experiments take into account 2262 training and 252 testing images. Results show that with the Nadam optimizer, the proposed algorithm has an overall classification accuracy, AUC, precision, recall, and F1-score of 91.27%, 92.31%, 90.48%, 98.30%, and 94.23%, respectively. Finally, these results are compared with some recent deep-learning models. For this four-class dataset, the proposed RVCNet has a classification accuracy of 91.27%, which is better than ResNet101V2, VGG19, VGG19 over CNN, and other stand-alone models. Finally, the application of the GRAD-CAM approach clearly interprets the classification of images by the RVCNet framework.
MeSH term(s)	Humans ; Neural Networks, Computer ; Algorithms ; COVID-19/diagnostic imaging ; Computer Systems ; Hydrolases ; COVID-19 Testing
Chemical Substances	Hydrolases (EC 3.-)
Language	English
Publishing date	2023-12-28
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0293125
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Deep Learning and Federated Learning for Screening COVID-19

M. Rubaiyat Hossain Mondal / Subrato Bharati / Prajoy Podder / Joarder Kamruzzaman

BioMedInformatics, Vol 3, Iss 45, Pp 691-

A Review

2023 Volume 713

Abstract: Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts a thorough study of the use of deep learning (DL) and federated learning (FL) approaches to COVID-19 screening. To begin, an evaluation ... ...

Abstract	Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts a thorough study of the use of deep learning (DL) and federated learning (FL) approaches to COVID-19 screening. To begin, an evaluation of research articles published between 1 January 2020 and 28 June 2023 is presented, considering the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. The review compares various datasets on medical imaging, including X-ray, computed tomography (CT) scans, and ultrasound images, in terms of the number of images, COVID-19 samples, and classes in the datasets. Following that, a description of existing DL algorithms applied to various datasets is offered. Additionally, a summary of recent work on FL for COVID-19 screening is provided. Efforts to improve the quality of FL models are comprehensively reviewed and objectively evaluated.
Keywords	coronavirus ; computed tomography ; COVID-19 ; deep learning ; federated learning ; DenseNet ; Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571 ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	006
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: RVCNet

Fatema Binte Alam / Prajoy Podder / M Rubaiyat Hossain Mondal

PLoS ONE, Vol 18, Iss 12, p e

A hybrid deep neural network framework for the diagnosis of lung diseases.

2023 Volume 0293125

Abstract	Early evaluation and diagnosis can significantly reduce the life-threatening nature of lung diseases. Computer-aided diagnostic systems (CADs) can help radiologists make more precise diagnoses and reduce misinterpretations in lung disease diagnosis. Existing literature indicates that more research is needed to correctly classify lung diseases in the presence of multiple classes for different radiographic imaging datasets. As a result, this paper proposes RVCNet, a hybrid deep neural network framework for predicting lung diseases from an X-ray dataset of multiple classes. This framework is developed based on the ideas of three deep learning techniques: ResNet101V2, VGG19, and a basic CNN model. In the feature extraction phase of this new hybrid architecture, hyperparameter fine-tuning is used. Additional layers, such as batch normalization, dropout, and a few dense layers, are applied in the classification phase. The proposed method is applied to a dataset of COVID-19, non-COVID lung infections, viral pneumonia, and normal patients' X-ray images. The experiments take into account 2262 training and 252 testing images. Results show that with the Nadam optimizer, the proposed algorithm has an overall classification accuracy, AUC, precision, recall, and F1-score of 91.27%, 92.31%, 90.48%, 98.30%, and 94.23%, respectively. Finally, these results are compared with some recent deep-learning models. For this four-class dataset, the proposed RVCNet has a classification accuracy of 91.27%, which is better than ResNet101V2, VGG19, VGG19 over CNN, and other stand-alone models. Finally, the application of the GRAD-CAM approach clearly interprets the classification of images by the RVCNet framework.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: A Review on Explainable Artificial Intelligence for Healthcare

Bharati, Subrato / Mondal, M. Rubaiyat Hossain / Podder, Prajoy

Why, How, and When?

2023

Abstract: Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about the explainability of the decisions that are made by these AI models. In this article, we give a systematic analysis of ... ...

Abstract	Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about the explainability of the decisions that are made by these AI models. In this article, we give a systematic analysis of explainable artificial intelligence (XAI), with a primary focus on models that are currently being used in the field of healthcare. The literature search is conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for relevant work published from 1 January 2012 to 02 February 2022. The review analyzes the prevailing trends in XAI and lays out the major directions in which research is headed. We investigate the why, how, and when of the uses of these XAI models and their implications. We present a comprehensive examination of XAI methodologies as well as an explanation of how a trustworthy AI can be derived from describing AI models for healthcare fields. The discussion of this work will contribute to the formalization of the XAI field. Comment: 15 pages, 3 figures, accepted for publication in the IEEE Transactions on Artificial Intelligence
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Publishing date	2023-04-10
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Sentiment analysis on Bangla text using extended lexicon dictionary and deep learning algorithms

Nitish Ranjan Bhowmik / Mohammad Arifuzzaman / M. Rubaiyat Hossain Mondal

Array, Vol 13, Iss , Pp 100123- (2022)

2022

Abstract: Sentiment analysis (SA) is a subset of natural language processing (NLP) research. In the case of categorical weighted based dictionary with rule-based sentiment score generation, no work in SA has been done yet in Bangla language using deep learning (DL) ...

Abstract	Sentiment analysis (SA) is a subset of natural language processing (NLP) research. In the case of categorical weighted based dictionary with rule-based sentiment score generation, no work in SA has been done yet in Bangla language using deep learning (DL) approaches. This paper proposes DL models for SA on Bangla text using an extended lexicon data dictionary (LDD). We implement the rule-based method Bangla text sentiment score (BTSC) algorithm for extracting polarity from large texts. These polarities are then fed into the neural network along with the preprocessed text as training samples. The preprocessed texts are formatted as a vectorization of words of unique numbers of pre-trained word embedding models. Word2Vec matrix with top highest probability word is applied on embedding layer as a weighted matrix to fit the DL models. This paper also presents a remarkably detailed analysis of selective DL models with some fine tuning. The fine-tuning includes the use of drop out, optimizer regularization, learning rate, multiple layers, filters, attention mechanism, capsule layers, transformer with progressive training along with validation and testing accuracy, precision, recall and F1-score. Experimental results indicate that the proposed new long short-term memory (LSTM) models are highly accurate in performing SA tasks. For our proposed hierarchical attention based LSTM (HAN-LSTM), Dynamic routing based capsule neural network with Bi-LSTM (D-CAPSNET-Bi-LSTM) and bidirectional encoder representations from Transformers (BERT) with LSTM (BERT-LSTM) model we achieved accuracy values of 78.52%, 80.82% and 84.18% respectively.
Keywords	Sentiment analysis ; Bangla NLP ; Deep learning ; BTSC ; Word2Vec ; Fine-tuning ; Computer engineering. Computer hardware ; TK7885-7895 ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: CO-IRv2

M Rubaiyat Hossain Mondal / Subrato Bharati / Prajoy Podder

PLoS ONE, Vol 16, Iss 10, p e

Optimized InceptionResNetV2 for COVID-19 detection from chest CT images.

2021 Volume 0259179

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 ... ...

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%.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Machine learning for DCO-OFDM based LiFi.

Krishna Saha Purnita / M Rubaiyat Hossain Mondal

PLoS ONE, Vol 16, Iss 11, p e

2021 Volume 0259955

Abstract: Light fidelity (LiFi) uses different forms of orthogonal frequency division multiplexing (OFDM), including DC biased optical OFDM (DCO-OFDM). In DCO-OFDM, the use of a large DC bias causes optical power inefficiency, while a small bias leads to higher ... ...

Abstract	Light fidelity (LiFi) uses different forms of orthogonal frequency division multiplexing (OFDM), including DC biased optical OFDM (DCO-OFDM). In DCO-OFDM, the use of a large DC bias causes optical power inefficiency, while a small bias leads to higher clipping noise. Hence, finding an appropriate DC bias level for DCO-OFDM is important. This paper applies machine learning (ML) algorithms to find optimum DC-bias value for DCO-OFDM based LiFi systems. For this, a dataset is generated for DCO-OFDM using MATLAB tool. Next, ML algorithms are applied using Python programming language. ML is used to find the important attributes of DCO-OFDM that influence the optimum DC bias. It is shown here that the optimum DC bias is a function of several factors including, the minimum, the standard deviation, and the maximum value of the bipolar OFDM signal, and the constellation size. Next, linear and polynomial regression algorithms are successfully applied to predict the optimum DC bias value. Results show that polynomial regression of order 2 can predict the optimum DC bias value with a coefficient of determination of 96.77% which confirms the effectiveness of the prediction.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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