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Article ; Online: Computer vision-based six layered ConvNeural network to recognize sign language for both numeral and alphabet signs

Muhammad Aminur Rahaman / Kabiratun Ummi Oyshe / Prothoma Khan Chowdhury / Tanoy Debnath / Anichur Rahman / Md. Saikat Islam Khan

Biomimetic Intelligence and Robotics, Vol 4, Iss 1, Pp 100141- (2024)

1481

Abstract: People who have trouble communicating verbally are often dependent on sign language, which can be difficult for most people to understand, making interaction with them a difficult endeavor. The Sign Language Recognition (SLR) system takes an input ... ...

Abstract	People who have trouble communicating verbally are often dependent on sign language, which can be difficult for most people to understand, making interaction with them a difficult endeavor. The Sign Language Recognition (SLR) system takes an input expression from a hearing or speaking-impaired person and outputs it in the form of text or voice to a normal person. The existing study related to the Sign Language Recognition system has some drawbacks, such as a lack of large datasets and datasets with a range of backgrounds, skin tones, and ages. This research efficiently focuses on Sign Language Recognition to overcome previous limitations. Most importantly, we use our proposed Convolutional Neural Network (CNN) model, “ConvNeural”, in order to train our dataset. Additionally, we develop our own datasets, “BdSL_OPSA22_STATIC1” and “BdSL_OPSA22_STATIC2”, both of which have ambiguous backgrounds. “BdSL_OPSA22_STATIC1” and “BdSL_OPSA22_STATIC2” both include images of Bangla characters and numerals, a total of 24,615 and 8437 images, respectively. The “ConvNeural” model outperforms the pre-trained models with accuracy of 98.38% for “BdSL_OPSA22_STATIC1” and 92.78% for “BdSL_OPSA22_STATIC2”. For “BdSL_OPSA22_STATIC1” dataset, we get precision, recall, F1-score, sensitivity and specificity of 96%, 95%, 95%, 99.31% , and 95.78% respectively. Moreover, in case of “BdSL_OPSA22_STATIC2” dataset, we achieve precision, recall, F1-score, sensitivity and specificity of 90%, 88%, 88%, 100%, and 100% respectively.
Keywords	ConvNeural ; Sign language ; CNN ; Static ; Feature extraction ; Convolution2D ; Electrical engineering. Electronics. Nuclear engineering ; TK1-9971 ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006 ; 410
Language	English
Publishing date	2024-03-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities.

Rahman, Anichur / Debnath, Tanoy / Kundu, Dipanjali / Khan, Md Saikat Islam / Aishi, Airin Afroj / Sazzad, Sadia / Sayduzzaman, Mohammad / Band, Shahab S

AIMS public health

2024 Volume 11, Issue 1, Page(s) 58–109

Abstract: In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, ... ...

Abstract	In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our observations.
Language	English
Publishing date	2024-01-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	2777115-5
ISSN	2327-8994 ; 2327-8994
ISSN (online)	2327-8994
ISSN	2327-8994
DOI	10.3934/publichealth.2024004
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Article ; Online: Internet of medical things and blockchain-enabled patient-centric agent through SDN for remote patient monitoring in 5G network.

Rahman, Anichur / Wadud, Md Anwar Hussen / Islam, Md Jahidul / Kundu, Dipanjali / Bhuiyan, T M Amir-Ul-Haque / Muhammad, Ghulam / Ali, Zulfiqar

Scientific reports

2024 Volume 14, Issue 1, Page(s) 5297

Abstract: During the COVID-19 pandemic, there has been a significant increase in the use of internet resources for accessing medical care, resulting in the development and advancement of the Internet of Medical Things (IoMT). This technology utilizes a range of ... ...

Abstract	During the COVID-19 pandemic, there has been a significant increase in the use of internet resources for accessing medical care, resulting in the development and advancement of the Internet of Medical Things (IoMT). This technology utilizes a range of medical equipment and testing software to broadcast patient results over the internet, hence enabling the provision of remote healthcare services. Nevertheless, the preservation of privacy and security in the realm of online communication continues to provide a significant and pressing obstacle. Blockchain technology has shown the potential to mitigate security apprehensions across several sectors, such as the healthcare industry. Recent advancements in research have included intelligent agents in patient monitoring systems by integrating blockchain technology. However, the conventional network configuration of the agent and blockchain introduces a level of complexity. In order to address this disparity, we present a proposed architectural framework that combines software defined networking (SDN) with Blockchain technology. This framework is specially tailored for the purpose of facilitating remote patient monitoring systems within the context of a 5G environment. The architectural design contains a patient-centric agent (PCA) inside the SDN control plane for the purpose of managing user data on behalf of the patients. The appropriate handling of patient data is ensured by the PCA via the provision of essential instructions to the forwarding devices. The suggested model is assessed using hyperledger fabric on docker-engine, and its performance is compared to that of current models in fifth generation (5G) networks. The performance of our suggested model surpasses current methodologies, as shown by our extensive study including factors such as throughput, dependability, communication overhead, and packet error rate.
MeSH term(s)	Humans ; Blockchain ; Pandemics ; Internet ; Monitoring, Physiologic ; Software ; Patient-Centered Care
Language	English
Publishing date	2024-03-04
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-55662-w
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Article ; Online: 4SQR-Code: A 4-state QR code generation model for increasing data storing capacity in the Digital Twin framework.

Udoy, Ababil Islam / Rahaman, Muhammad Aminur / Islam, Md Jahidul / Rahman, Anichur / Ali, Zulfiqar / Muhammad, Ghulam

Journal of advanced research

2023

Abstract: Introduction: The usage of Quick Response (QR) Codes has become widely popular in recent years, primarily for immense electronic transactions and industry uses. The structural flexibility of QR Code architecture opens many more possibilities for ... ...

Abstract	Introduction: The usage of Quick Response (QR) Codes has become widely popular in recent years, primarily for immense electronic transactions and industry uses. The structural flexibility of QR Code architecture opens many more possibilities for researchers in the domain of the Industrial Internet of Things (IIoT). However, the limited storage capacity of the traditional QR Codes still fails to stretch the data capacity limits. The researchers of this domain have already introduced different kinds of techniques, including data hiding, multiplexing, data compression, color QR Codes, and so on. However, the research on increasing the data storage capacity of the QR Codes is very limited and still operational. Objectives: The main objective of this work is to increase the data storage capacity of QR Codes in the IIoT domain. Methods: In the first part, we have introduced a 4-State-Pattern-based encoding technique to generate the proposed 4-State QR (4SQR) Code where actual data are encoded into a 4SQR Code image which increases the data storage capacity more than the traditional 2-State QR Code. The proposed 4SQR Code consists of four types of patterns, including Black Square Box (BSB), White Square Box (WSB), Triangle, and Circle, whereas the traditional 2-State QR Codes consist of BSB and WSB. In the second part, the 4SQR Code decoding module has been introduced using the adaptive YOLO V5 algorithm where the proposed 4SQR Code image is decoded into the actual data. Results: The proposed model is tested in a Digital Twin (DT) framework using randomly generated 3000 testing samples for the encoding module that converts into 4SQR Code images successfully and similarly for the decoding module that decodes the 4SQR Code images into the actual data. Conclusion: Experimental results show that this proposed technique offers increased data storage capacity two times than traditional 2-State QR Codes.
Language	English
Publishing date	2023-10-18
Publishing country	Egypt
Document type	Journal Article
ZDB-ID	2541849-X
ISSN	2090-1224 ; 2090-1224
ISSN (online)	2090-1224
ISSN	2090-1224
DOI	10.1016/j.jare.2023.10.006
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Article ; Online: MultiNet

Saikat Islam Khan / Ashef Shahrior / Razaul Karim / Mahmodul Hasan / Anichur Rahman

Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 8, Pp 6217-

A deep neural network approach for detecting breast cancer through multi-scale feature fusion

2022 Volume 6228

Abstract: Breast cancer diagnosis from biopsy tissue images conducted manually by pathologists is costly, time-consuming, and disagreements among specialists. Nowadays, the advancement of the Computer-Aided Diagnosis (CAD) system allows pathologists to identify ... ...

Abstract	Breast cancer diagnosis from biopsy tissue images conducted manually by pathologists is costly, time-consuming, and disagreements among specialists. Nowadays, the advancement of the Computer-Aided Diagnosis (CAD) system allows pathologists to identify breast cancer more reliably and quickly.For this reason, interest in CAD-based deep learning models has been increased significantly. In this study, we propose a “MultiNet” framework based on the transfer learning concept to classify different breast cancer types using two publicly available datasets that include 7909 and 400 microscopic breast images, respectively. The proposed “MultiNet” framework is designed to provide fast and accurate diagnostics for breast cancer with binary classification (benign and malignant) and multi-class classification (benign, in situ, invasive, and normal). In the proposed framework, features from microscopy images are extracted using three well-known pre-trained models, including DenseNet-201, NasNetMobile, and VGG16. The extracted features are then fed into the concatenate layer, making a robust hybrid model. The proposed framework yields an overall classification accuracy of 99% in classifying two classes. It also achieves 98% classification accuracy in classifying four classes. Such promising results will provide the opportunity to use “MultiNet” framework as a diagnostic model in clinics and health care.
Keywords	Breast cancer ; CAD ; DenseNet-201 ; NasNetMobile ; VGG16 ; Feature fusion ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006
Language	English
Publishing date	2022-09-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Four-layer ConvNet to facial emotion recognition with minimal epochs and the significance of data diversity.

Debnath, Tanoy / Reza, Md Mahfuz / Rahman, Anichur / Beheshti, Amin / Band, Shahab S / Alinejad-Rokny, Hamid

Scientific reports

2022 Volume 12, Issue 1, Page(s) 6991

Abstract: Emotion recognition is defined as identifying human emotion and is directly related to different fields such as human-computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human-robot ... ...

Abstract	Emotion recognition is defined as identifying human emotion and is directly related to different fields such as human-computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human-robot communication, and many more. This paper proposes a new facial emotional recognition model using a convolutional neural network. Our proposed model, "ConvNet", detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise. The features extracted by the Local Binary Pattern (LBP), region based Oriented FAST and rotated BRIEF (ORB) and Convolutional Neural network (CNN) from facial expressions images were fused to develop the classification model through training by our proposed CNN model (ConvNet). Our method can converge quickly and achieves good performance which the authors can develop a real-time schema that can easily fit the model and sense emotions. Furthermore, this study focuses on the mental or emotional stuff of a man or woman using the behavioral aspects. To complete the training of the CNN network model, we use the FER2013 databases at first, and then apply the generalization techniques to the JAFFE and CK+ datasets respectively in the testing stage to evaluate the performance of the model. In the generalization approach on the JAFFE dataset, we get a 92.05% accuracy, while on the CK+ dataset, we acquire a 98.13% accuracy which achieve the best performance among existing methods. We also test the system's success by identifying facial expressions in real-time. ConvNet consists of four layers of convolution together with two fully connected layers. The experimental results show that the ConvNet is able to achieve 96% training accuracy which is much better than current existing models. However, when compared to other validation methods, the suggested technique was more accurate. ConvNet also achieved validation accuracy of 91.01% for the FER2013 dataset. We also made all the materials publicly accessible for the research community at: https://github.com/Tanoy004/Emotion-recognition-through-CNN .
MeSH term(s)	Anger ; Emotions ; Facial Expression ; Facial Recognition ; Female ; Humans ; Male ; Neural Networks, Computer
Language	English
Publishing date	2022-04-28
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-11173-0
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Article ; Online: Study on IoT for SARS-CoV-2 with healthcare: present and future perspective.

Rahman, Anichur / Rahman, Muaz / Kundu, Dipanjali / Karim, Md Razaul / Band, Shahab S / Sookhak, Mehdi

Mathematical biosciences and engineering : MBE

2021 Volume 18, Issue 6, Page(s) 9697–9726

Abstract: The ever-evolving and contagious nature of the Coronavirus (COVID-19) has immobilized the world around us. As the daily number of infected cases increases, the containment of the spread of this virus is proving to be an overwhelming task. Healthcare ... ...

Abstract	The ever-evolving and contagious nature of the Coronavirus (COVID-19) has immobilized the world around us. As the daily number of infected cases increases, the containment of the spread of this virus is proving to be an overwhelming task. Healthcare facilities around the world are overburdened with an ominous responsibility to combat an ever-worsening scenario. To aid the healthcare system, Internet of Things (IoT) technology provides a better solution-tracing, testing of COVID patients efficiently is gaining rapid pace. This study discusses the role of IoT technology in healthcare during the SARS-CoV-2 pandemics. The study overviews different research, platforms, services, products where IoT is used to combat the COVID-19 pandemic. Further, we intelligently integrate IoT and healthcare for COVID-19 related applications. Again, we focus on a wide range of IoT applications in regards to SARS-CoV-2 tracing, testing, and treatment. Finally, we effectively consider further challenges, issues, and some direction regarding IoT in order to uplift the healthcare system during COVID-19 and future pandemics.
MeSH term(s)	COVID-19 ; Delivery of Health Care ; Humans ; Internet of Things ; Pandemics ; SARS-CoV-2
Language	English
Publishing date	2021-11-20
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	2265126-3
ISSN	1551-0018 ; 1551-0018
ISSN (online)	1551-0018
ISSN	1551-0018
DOI	10.3934/mbe.2021475
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Article: Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues.

Rahman, Anichur / Hossain, Md Sazzad / Muhammad, Ghulam / Kundu, Dipanjali / Debnath, Tanoy / Rahman, Muaz / Khan, Md Saikat Islam / Tiwari, Prayag / Band, Shahab S

Cluster computing

2022 , Page(s) 1–41

Abstract: Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents ...

Abstract	Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.
Language	English
Publishing date	2022-08-17
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2012757-1
ISSN	1573-7543 ; 1386-7857
ISSN (online)	1573-7543
ISSN	1386-7857
DOI	10.1007/s10586-022-03658-4
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Article ; Online: Four-layer ConvNet to facial emotion recognition with minimal epochs and the significance of data diversity

Tanoy Debnath / Md. Mahfuz Reza / Anichur Rahman / Amin Beheshti / Shahab S. Band / Hamid Alinejad-Rokny

Scientific Reports, Vol 12, Iss 1, Pp 1-

2022 Volume 18

Abstract: Abstract Emotion recognition is defined as identifying human emotion and is directly related to different fields such as human–computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human–robot ... ...

Abstract	Abstract Emotion recognition is defined as identifying human emotion and is directly related to different fields such as human–computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human–robot communication, and many more. This paper proposes a new facial emotional recognition model using a convolutional neural network. Our proposed model, “ConvNet”, detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise. The features extracted by the Local Binary Pattern (LBP), region based Oriented FAST and rotated BRIEF (ORB) and Convolutional Neural network (CNN) from facial expressions images were fused to develop the classification model through training by our proposed CNN model (ConvNet). Our method can converge quickly and achieves good performance which the authors can develop a real-time schema that can easily fit the model and sense emotions. Furthermore, this study focuses on the mental or emotional stuff of a man or woman using the behavioral aspects. To complete the training of the CNN network model, we use the FER2013 databases at first, and then apply the generalization techniques to the JAFFE and CK+ datasets respectively in the testing stage to evaluate the performance of the model. In the generalization approach on the JAFFE dataset, we get a 92.05% accuracy, while on the CK+ dataset, we acquire a 98.13% accuracy which achieve the best performance among existing methods. We also test the system’s success by identifying facial expressions in real-time. ConvNet consists of four layers of convolution together with two fully connected layers. The experimental results show that the ConvNet is able to achieve 96% training accuracy which is much better than current existing models. However, when compared to other validation methods, the suggested technique was more accurate. ConvNet also achieved validation accuracy of 91.01% for the FER2013 dataset. We also made all the materials publicly accessible for the research community at: https://github.com/Tanoy004/Emotion-recognition-through-CNN .
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2022-04-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
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Article: Accurate brain tumor detection using deep convolutional neural network.

Khan, Md Saikat Islam / Rahman, Anichur / Debnath, Tanoy / Karim, Md Razaul / Nasir, Mostofa Kamal / Band, Shahab S / Mosavi, Amir / Dehzangi, Iman

Computational and structural biotechnology journal

2022 Volume 20, Page(s) 4733–4745

Abstract: Detection and Classification of a brain tumor is an important step to better understanding its mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging technique that helps the radiologist find the tumor region. However, it is a ... ...

Abstract	Detection and Classification of a brain tumor is an important step to better understanding its mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging technique that helps the radiologist find the tumor region. However, it is a time taking process and requires expertise to test the MRI images, manually. Nowadays, the advancement of Computer-assisted Diagnosis (CAD), machine learning, and deep learning in specific allow the radiologist to more reliably identify brain tumors. The traditional machine learning methods used to tackle this problem require a handcrafted feature for classification purposes. Whereas deep learning methods can be designed in a way to not require any handcrafted feature extraction while achieving accurate classification results. This paper proposes two deep learning models to identify both binary (normal and abnormal) and multiclass (meningioma, glioma, and pituitary) brain tumors. We use two publicly available datasets that include 3064 and 152 MRI images, respectively. To build our models, we first apply a 23-layers convolution neural network (CNN) to the first dataset since there is a large number of MRI images for the training purpose. However, when dealing with limited volumes of data, which is the case in the second dataset, our proposed "23-layers CNN" architecture faces overfitting problem. To address this issue, we use transfer learning and combine VGG16 architecture along with the reflection of our proposed "23 layers CNN" architecture. Finally, we compare our proposed models with those reported in the literature. Our experimental results indicate that our models achieve up to 97.8% and 100% classification accuracy for our employed datasets, respectively, exceeding all other state-of-the-art models. Our proposed models, employed datasets, and all the source codes are publicly available at: (https://github.com/saikat15010/Brain-Tumor-Detection).
Language	English
Publishing date	2022-08-27
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2694435-2
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2022.08.039
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