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Article ; Online: Development of Smart Healthcare Monitoring System in IoT Environment.

Islam, Md Milon / Rahaman, Ashikur / Islam, Md Rashedul

SN computer science

2020 Volume 1, Issue 3, Page(s) 185

Abstract: Healthcare monitoring system in hospitals and many other health centers has experienced significant growth, and portable healthcare monitoring systems with emerging technologies are becoming of great concern to many countries worldwide nowadays. The ... ...

Abstract	Healthcare monitoring system in hospitals and many other health centers has experienced significant growth, and portable healthcare monitoring systems with emerging technologies are becoming of great concern to many countries worldwide nowadays. The advent of Internet of Things (IoT) technologies facilitates the progress of healthcare from face-to-face consulting to telemedicine. This paper proposes a smart healthcare system in IoT environment that can monitor a patient's basic health signs as well as the room condition where the patients are now in real-time. In this system, five sensors are used to capture the data from hospital environment named heart beat sensor, body temperature sensor, room temperature sensor, CO sensor, and CO
Keywords	covid19
Language	English
Publishing date	2020-05-26
Publishing country	Singapore
Document type	Journal Article
ISSN	2661-8907
ISSN (online)	2661-8907
DOI	10.1007/s42979-020-00195-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Md. Zabirul Islam / Md. Milon Islam / Amanullah Asraf

Informatics in Medicine Unlocked, Vol 20, Iss , Pp 100412- (2020)

2020

Abstract: Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and ... ...

Abstract	Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and reduces the death rate. Coronavirus (COVID-19) has become one of the most severe and acute diseases in recent times and has spread globally. Therefore, an automated detection system, as the fastest diagnostic option, should be implemented to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 4575 X-ray images, including 1525 images of COVID-19, were used as a dataset in this system. The experimental results show that our proposed system achieved an accuracy of 99.4%, AUC of 99.9%, specificity of 99.2%, sensitivity of 99.3%, and F1-score of 98.9%. The system achieved desired results on the currently available dataset, which can be further improved when more COVID-19 images become available. The proposed system can help doctors to diagnose and treat COVID-19 patients easily.
Keywords	Coronavirus ; COVID-19 ; Deep learning ; Chest X-ray ; Convolutional neural network ; Long short-term memory ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	006
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images.

Islam, Md Zabirul / Islam, Md Milon / Asraf, Amanullah

Informatics in medicine unlocked

2020 Volume 20, Page(s) 100412

Abstract	Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and reduces the death rate. Coronavirus (COVID-19) has become one of the most severe and acute diseases in recent times and has spread globally. Therefore, an automated detection system, as the fastest diagnostic option, should be implemented to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 4575 X-ray images, including 1525 images of COVID-19, were used as a dataset in this system. The experimental results show that our proposed system achieved an accuracy of 99.4%, AUC of 99.9%, specificity of 99.2%, sensitivity of 99.3%, and F1-score of 98.9%. The system achieved desired results on the currently available dataset, which can be further improved when more COVID-19 images become available. The proposed system can help doctors to diagnose and treat COVID-19 patients easily.
Keywords	covid19
Language	English
Publishing date	2020-08-15
Publishing country	England
Document type	Journal Article
ISSN	2352-9148
ISSN	2352-9148
DOI	10.1016/j.imu.2020.100412
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Human activity recognition using tools of convolutional neural networks: A state of the art review, data sets, challenges, and future prospects.

Islam, Md Milon / Nooruddin, Sheikh / Karray, Fakhri / Muhammad, Ghulam

Computers in biology and medicine

2022 Volume 149, Page(s) 106060

Abstract: Human Activity Recognition (HAR) plays a significant role in the everyday life of people because of its ability to learn extensive high-level information about human activity from wearable or stationary devices. A substantial amount of research has been ... ...

Abstract	Human Activity Recognition (HAR) plays a significant role in the everyday life of people because of its ability to learn extensive high-level information about human activity from wearable or stationary devices. A substantial amount of research has been conducted on HAR and numerous approaches based on deep learning have been exploited by the research community to classify human activities. The main goal of this review is to summarize recent works based on a wide range of deep neural networks architecture, namely convolutional neural networks (CNNs) for human activity recognition. The reviewed systems are clustered into four categories depending on the use of input devices like multimodal sensing devices, smartphones, radar, and vision devices. This review describes the performances, strengths, weaknesses, and the used hyperparameters of CNN architectures for each reviewed system with an overview of available public data sources. In addition, a discussion of the current challenges to CNN-based HAR systems is presented. Finally, this review is concluded with some potential future directions that would be of great assistance for the researchers who would like to contribute to this field. We conclude that CNN-based approaches are suitable for effective and accurate human activity recognition system applications despite challenges including availability of data regarding composite or group activities, high computational resource requirements, data privacy concerns, and edge computing limitations. For widespread adaptation, future research should be focused on more efficient edge computing techniques, datasets incorporating contextual information with activities, more explainable methodologies, and more robust systems.
MeSH term(s)	Human Activities ; Humans ; Information Storage and Retrieval ; Neural Networks, Computer ; Privacy ; Smartphone
Language	English
Publishing date	2022-09-01
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2022.106060
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Article ; Online: EMCNet

Prottoy Saha / Muhammad Sheikh Sadi / Md. Milon Islam

Informatics in Medicine Unlocked, Vol 22, Iss , Pp 100505- (2021)

Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers

2021

Abstract: Recently, coronavirus disease (COVID-19) has caused a serious effect on the healthcare system and the overall global economy. Doctors, researchers, and experts are focusing on alternative ways for the rapid detection of COVID-19, such as the development ... ...

Abstract	Recently, coronavirus disease (COVID-19) has caused a serious effect on the healthcare system and the overall global economy. Doctors, researchers, and experts are focusing on alternative ways for the rapid detection of COVID-19, such as the development of automatic COVID-19 detection systems. In this paper, an automated detection scheme named EMCNet was proposed to identify COVID-19 patients by evaluating chest X-ray images. A convolutional neural network was developed focusing on the simplicity of the model to extract deep and high-level features from X-ray images of patients infected with COVID-19. With the extracted features, binary machine learning classifiers (random forest, support vector machine, decision tree, and AdaBoost) were developed for the detection of COVID-19. Finally, these classifiers’ outputs were combined to develop an ensemble of classifiers, which ensures better results for the dataset of various sizes and resolutions. In comparison with other recent deep learning-based systems, EMCNet showed better performance with 98.91% accuracy, 100% precision, 97.82% recall, and 98.89% F1-score. The system could maintain its great importance on the automatic detection of COVID-19 through instant detection and low false negative rate.
Keywords	COVID-19 ; Convolutional neural network ; Ensemble of classifiers ; Automatic diagnosis ; X-ray images ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	006
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Islam, Md. Zabirul / Islam, Md. Milon / Asraf, Amanullah

Informatics in Medicine Unlocked

2020 Volume 20, Page(s) 100412

Keywords	covid19
Language	English
Publisher	Elsevier BV
Publishing country	us
Document type	Article ; Online
ISSN	2352-9148
DOI	10.1016/j.imu.2020.100412
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: In Vitro Antioxidant and In Vivo Hepatoprotective Properties of

Hossain, Kazi Nadim / Islam, Md Shafiqul / Rahman, Sheikh Hasibur / Sarker, Subroto / Mondal, Milon / Rahman, Mohammad Asikur / Alhag, Sadeq K / Al-Shuraym, Laila A / Alghamdi, Othman A / Islam, Muhammad Torequl / Al-Farga, Ammar / El-Shazly, Mohamed / Alam, Md Jahir / El-Nashar, Heba A S

ACS omega

2023 Volume 8, Issue 49, Page(s) 47001–47011

Abstract: Wissadula ... ...

Abstract	Wissadula periplocifolia
Language	English
Publishing date	2023-11-28
Publishing country	United States
Document type	Journal Article
ISSN	2470-1343
ISSN (online)	2470-1343
DOI	10.1021/acsomega.3c06614
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic.

Asraf, Amanullah / Islam, Md Zabirul / Haque, Md Rezwanul / Islam, Md Milon

SN computer science

2020 Volume 1, Issue 6, Page(s) 363

Abstract: During this global pandemic, researchers around the world are trying to find out innovative technology for a smart healthcare system to combat coronavirus. The evidence of deep learning applications on the past epidemic inspires the experts by giving a ... ...

Abstract	During this global pandemic, researchers around the world are trying to find out innovative technology for a smart healthcare system to combat coronavirus. The evidence of deep learning applications on the past epidemic inspires the experts by giving a new direction to control this outbreak. The aim of this paper is to discuss the contributions of deep learning at several scales including medical imaging, disease tracing, analysis of protein structure, drug discovery, and virus severity and infectivity to control the ongoing outbreak. A progressive search of the database related to the applications of deep learning was executed on COVID-19. Further, a comprehensive review is done using selective information by assessing the different perspectives of deep learning. This paper attempts to explore and discuss the overall applications of deep learning on multiple dimensions to control novel coronavirus (COVID-19). Though various studies are conducted using deep learning algorithms, there are still some constraints and challenges while applying for real-world problems. The ongoing progress in deep learning contributes to handle coronavirus infection and plays an effective role to develop appropriate solutions. It is expected that this paper would be a great help for the researchers who would like to contribute to the development of remedies for this current pandemic in this area.
Keywords	covid19
Language	English
Publishing date	2020-11-03
Publishing country	Singapore
Document type	Journal Article ; Review
ISSN	2661-8907
ISSN (online)	2661-8907
DOI	10.1007/s42979-020-00383-w
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Machine Learning Approaches for Tackling Novel Coronavirus (COVID-19) Pandemic.

Rahman, Mohammad Marufur / Islam, Md Milon / Manik, Md Motaleb Hossen / Islam, Md Rabiul / Al-Rakhami, Mabrook S

SN computer science

2021 Volume 2, Issue 5, Page(s) 384

Abstract: Novel coronavirus (COVID-19) has become a global problem in recent times due to the rapid spread of this disease. Almost all the countries of the world have been affected by this pandemic that made a major consequence on the medical system and healthcare ...

Abstract	Novel coronavirus (COVID-19) has become a global problem in recent times due to the rapid spread of this disease. Almost all the countries of the world have been affected by this pandemic that made a major consequence on the medical system and healthcare facilities. The healthcare system is going through a critical time because of the COVID-19 pandemic. Modern technologies such as deep learning, machine learning, and data science are contributing to fight COVID-19. The paper aims to highlight the role of machine learning approaches in this pandemic situation. We searched for the latest literature regarding machine learning approaches for COVID-19 from various sources like IEEE Xplore, PubMed, Google Scholar, Research Gate, and Scopus. Then, we analyzed this literature and described them throughout the study. In this study, we noticed four different applications of machine learning methods to combat COVID-19. These applications are trying to contribute in various aspects like helping physicians to make confident decisions, policymakers to take fruitful decisions, and identifying potentially infected people. The major challenges of existing systems with possible future trends are outlined in this paper. The researchers are coming with various technologies using machine learning techniques to face the COVID-19 pandemic. These techniques are serving the healthcare system in a great deal. We recommend that machine learning can be a useful tool for proper analyzing, screening, tracking, forecasting, and predicting the characteristics and trends of COVID-19.
Language	English
Publishing date	2021-07-19
Publishing country	Singapore
Document type	Journal Article ; Review
ISSN	2661-8907
ISSN (online)	2661-8907
DOI	10.1007/s42979-021-00774-7
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Synthesis of PEDOT:PSS Solution-Processed Electronic Textiles for Enhanced Joule Heating.

Jalil, Mohammad Abdul / Ahmed, Abbas / Hossain, Md Milon / Adak, Bapan / Islam, M Tauhidul / Moniruzzaman, Md / Parvez, Md Sohan / Shkir, Mohd / Mukhopadhyay, Samrat

ACS omega

2022 Volume 7, Issue 15, Page(s) 12716–12723

Abstract: Textile-based flexible and wearable electronic devices provide an excellent solution to thermal management systems, thermal therapy, and deicing applications through the Joule heating approach. However, challenges persist in designing such cost-effective ...

Abstract	Textile-based flexible and wearable electronic devices provide an excellent solution to thermal management systems, thermal therapy, and deicing applications through the Joule heating approach. However, challenges persist in designing such cost-effective electronic devices for efficient heating performance. Herein, this study adopted a facile solution-processed strategy, "dip-coating", to develop a high-performance Joule heating device by unformly coating the intrinsically conducting polymer (CP) poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) onto the surface of cotton textiles. The structural and morphological attributes of the cotton/CP mixture were evaluated using various characterization techniques. The electrothermal characteristics of the cotton/CP sample included rapid thermal response, uniform surface temperature distribution up to 94 °C, excellent stability, and endurance in heating performance under various mechanical deformations. The real-time illustration of the fabric heater affixed on a human finger has demonstrated its outstanding potential for thermal therapy applications. The fabricated heater may further expand it purposes toward deicing, defogging, and defrosting applications.
Language	English
Publishing date	2022-04-07
Publishing country	United States
Document type	Journal Article
ISSN	2470-1343
ISSN (online)	2470-1343
DOI	10.1021/acsomega.1c07148
Database	MEDical Literature Analysis and Retrieval System OnLINE

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