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S. M. Riazul Islam

Bio: S. M. Riazul Islam is an academic researcher from Sejong University. The author has contributed to research in topics: Communication channel & Noma. The author has an hindex of 24, co-authored 125 publications receiving 5501 citations. Previous affiliations of S. M. Riazul Islam include University of Dhaka & Inha University.


Papers
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Journal ArticleDOI
TL;DR: The main objective of the proposed framework is to bridge the current gap between current technologies and healthcare systems and propose a convolutional neural network-based deep learning model for COVID-19 detection based on patient’s X-ray scan images and transfer learning.
Abstract: Coronavirus (COVID-19) is a new virus of viral pneumonia. It can outbreak in the world through person-to-person transmission. Although several medical companies provide cooperative monitoring healthcare systems, these solutions lack offering of the end-to-end management of the disease. The main objective of the proposed framework is to bridge the current gap between current technologies and healthcare systems. The wireless body area network, cloud computing, fog computing, and clinical decision support system are integrated to provide a comprehensive and complete model for disease detection and monitoring. By monitoring a person with COVID-19 in real time, physicians can guide patients with the right decisions. The proposed framework has three main layers (i.e., a patient layer, cloud layer, and hospital layer). In the patient layer, the patient is tracked through a set of wearable sensors and a mobile app. In the cloud layer, a fog network architecture is proposed to solve the issues of storage and data transmission. In the hospital layer, we propose a convolutional neural network-based deep learning model for COVID-19 detection based on patient’s X-ray scan images and transfer learning. The proposed model achieved promising results compared to the state-of-the art (i.e., accuracy of 97.95% and specificity of 98.85%). Our framework is a useful application, through which we expect significant effects on COVID-19 proliferation and considerable lowering in healthcare expenses.

72 citations

Journal ArticleDOI
TL;DR: In this paper, a D-shaped photonic crystal fiber (PCF) was used for temperature measurement in the near infrared region (900-1900 nm) for the first time.
Abstract: Simple structure and high sensitivity with a broad detection range are highly desirable for temperature sensor. This work presents a highly sensitive plasmonic sensor based on D-shaped photonic crystal fiber (PCF) in the near infrared region (900-1900 nm) for temperature measurement. The proposed sensor is designed by finite element method (FEM) based simulation tool and sensing properties are investigated by means of wavelength interrogation method (WIM). To support the surface plasmon oscillation, 45 nm gold film is deposited on the flat portion of the D-shaped PCF which consists of pure silica. Benzene is used as the temperature sensitive material that offers large propagation loss (PL) peak shift. Simulation outcome shows that the maximum possible sensitivity of 110 nm/°C in the temperature range from 10 °C to 70 °C. To our knowledge, the achieved sensitivity is the highest for temperature sensing in the existing literature. In addition, the proposed sensor exhibits the maximum figure of merit (FOM) of 5.5 /°C, resolution of 9.09 × 10–4 °C, and excellent fitting characteristics of PL peak wavelengths. Moreover, low PL of the proposed sensor helps to extend the sensor length up to few centimeters. Such excellent results and wider temperature range make sure that the proposed sensor can be an appropriate choice for temperature measurement even in the remote sensing application.

70 citations

Journal ArticleDOI
TL;DR: In this paper, a two-layer model with random forest (RF) as classifier algorithm is proposed to diagnose and progression detection of Alzheimer's disease (AD) in patients.
Abstract: Alzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.

70 citations

Journal ArticleDOI
TL;DR: In this paper, the authors outline the WBAN requirements that are important for the design of a low-power MAC protocol and study low power MAC protocols proposed/investigated for WBAN with emphasis on their strengths and weaknesses.
Abstract: The seamless integration of low-power, miniaturised, invasive/non-invasive lightweight sensor nodes have contributed to the development of a proactive and unobtrusive Wireless Body Area Network (WBAN). A WBAN provides long-term health monitoring of a patient without any constraint on his/her normal dailylife activities. This monitoring requires low-power operation of invasive/non-invasive sensor nodes. In other words, a power-efficient Medium Access Control (MAC) protocol is required to satisfy the stringent WBAN requirements including low-power consumption. In this paper, we first outline the WBAN requirements that are important for the design of a low-power MAC protocol. Then we study low-power MAC protocols proposed/investigated for WBAN with emphasis on their strengths and weaknesses. We also review different power-efficient mechanisms for WBAN. In addition, useful suggestions are given to help the MAC designers to develop a low-power MAC protocol that will satisfy the stringent WBAN requirements.

69 citations

Journal ArticleDOI
TL;DR: A new MAC protocol for BAN is proposed using out of band (on-demand) wakeup radio through a centralized and coordinated external wakeup mechanism and is found to be efficient in terms of power consumption and delay.
Abstract: Applications of wearable and implanted wireless sensor devices are hot research area. A specialized field called the body area networks (BAN) has emerged to support this area. Managing and controlling such a network is a challenging task. An efficient media access control (MAC) protocol to handle proper management of media access can considerably improve the performance of such a network. Power consumption and delay are major concerns for MAC protocols in a BAN. Low cost wakeup radio module attached with sensor devices can help reduce power consumption and prolong the network lifetime by reducing idle state power consumption and increasing sleep time of a BAN node. In this article, we propose a new MAC protocol for BAN using out of band (on-demand) wakeup radio through a centralized and coordinated external wakeup mechanism. We have compared our method against some existing MAC protocols. Our method is found to be efficient in terms of power consumption and delay.

63 citations


Cited by
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Journal ArticleDOI
TL;DR: The current state-of-art of WBANs is surveyed based on the latest standards and publications, and open issues and challenges within each area are explored as a source of inspiration towards future developments inWBANs.
Abstract: Recent developments and technological advancements in wireless communication, MicroElectroMechanical Systems (MEMS) technology and integrated circuits has enabled low-power, intelligent, miniaturized, invasive/non-invasive micro and nano-technology sensor nodes strategically placed in or around the human body to be used in various applications, such as personal health monitoring. This exciting new area of research is called Wireless Body Area Networks (WBANs) and leverages the emerging IEEE 802.15.6 and IEEE 802.15.4j standards, specifically standardized for medical WBANs. The aim of WBANs is to simplify and improve speed, accuracy, and reliability of communication of sensors/actuators within, on, and in the immediate proximity of a human body. The vast scope of challenges associated with WBANs has led to numerous publications. In this paper, we survey the current state-of-art of WBANs based on the latest standards and publications. Open issues and challenges within each area are also explored as a source of inspiration towards future developments in WBANs.

1,359 citations

Journal ArticleDOI
01 Dec 2017
TL;DR: This work provides a comprehensive overview of the state of the art in power-domain multiplexing-aided NOMA, with a focus on the theoretical N OMA principles, multiple-antenna- aided NomA design, and on the interplay between NOMa and cooperative transmission.
Abstract: Driven by the rapid escalation of the wireless capacity requirements imposed by advanced multimedia applications (e.g., ultrahigh-definition video, virtual reality, etc.), as well as the dramatically increasing demand for user access required for the Internet of Things (IoT), the fifth-generation (5G) networks face challenges in terms of supporting large-scale heterogeneous data traffic. Nonorthogonal multiple access (NOMA), which has been recently proposed for the third-generation partnership projects long-term evolution advanced (3GPP-LTE-A), constitutes a promising technology of addressing the aforementioned challenges in 5G networks by accommodating several users within the same orthogonal resource block. By doing so, significant bandwidth efficiency enhancement can be attained over conventional orthogonal multiple-access (OMA) techniques. This motivated numerous researchers to dedicate substantial research contributions to this field. In this context, we provide a comprehensive overview of the state of the art in power-domain multiplexing-aided NOMA, with a focus on the theoretical NOMA principles, multiple-antenna-aided NOMA design, on the interplay between NOMA and cooperative transmission, on the resource control of NOMA, on the coexistence of NOMA with other emerging potential 5G techniques and on the comparison with other NOMA variants. We highlight the main advantages of power-domain multiplexing NOMA compared to other existing NOMA techniques. We summarize the challenges of existing research contributions of NOMA and provide potential solutions. Finally, we offer some design guidelines for NOMA systems and identify promising research opportunities for the future.

1,008 citations

Journal ArticleDOI
05 Dec 1980-JAMA
TL;DR: This third edition of what has now become a well-established textbook in cardiovascular medicine is again edited by Dr Eugene Braunwald with the assistance of 65 other authors who read like a Who's Who of American Cardiology.
Abstract: This third edition of what has now become a well-established textbook in cardiovascular medicine is again edited by Dr Eugene Braunwald with the assistance of 65 other authors who read like a Who's Who of American Cardiology. Since the second edition, 12 new chapters have been added or substituted and others have been significantly revised. The first volume includes Part I on "Examination of the Patient" and Part II on "Normal and Abnormal Circulatory Function." The second volume deals with specific diseases. Part III, "Diseases of the Heart, Pericardium and Vascular System," includes new sections on "Risk Factors for Coronary Artery Disease," "The Pathogenesis of Atherosclerosis," and "Interventional Catheterization Techniques." Part IV, "Broader Perspectives on Heart Disease and Cardiologic Practice," includes new chapters on "Genetics and Cardiovascular Disease," "Aging in Cardiac Disease," and "Cost Effective Strategies in Cardiology." The last 200 pages of the book (Part V) are devoted to

927 citations