Special lssues
Table of Content

Smart and Secure Solutions for Medical Industry

Submission Deadline: 31 August 2023 (closed)

Guest Editors

Dr. Thippa Reddy Gadekallu, School of Information Technology and Engineering, VIT, India
Dr. Abdul Rehman Javed (CO-GE), Air University, Pakistan

Summary

The Healthcare sector (a.k.a medical industry) offers opportunities to provide smart healthcare solutions like health monitoring, assessment, personal data security, and protection. Significant disruptive trends are reshaping the world and reforming the health industry today. Smart Healthcare systems are deeply connected with smartphones, wearable devices, and the internet of things (IoT). In this context, Ambient assisted living (AAL) has a prominent role in improving scalability in healthcare services, making them reachable to older people, and keeping the user safe in their home environments. Meanwhile, patient data and other critical clinical records are effortlessly transmitted over the network. This data not just give an insight to Healthcare specialists but also give the researcher the premise to provide an optimal solution. Organizations are adopting new approaches to turning data insights into valuable results. Many smart obtrusive devices and systems grow exponentially, resulting in a lack of security and privacy management, structuring medical data. A considerable measure of gathered information to battle the COVID-19 pandemic raises numerous security and protection concerns in this period. In this manner, appropriate medical data privacy and security are getting similarly significant in smart healthcare systems.


Considering these facts, this issue focuses on the researchers investigating and sharing groundbreaking ideas, approaches, hypotheses, and practices centered around smart health monitoring systems, analytics, data security, and privacy for healthcare industries.


This special issue will focus on (but is not limited to) the following topics:

• Connected healthcare, Ambient assisted living (AAL)

• Remote Patient Care Monitoring, Telemedicine system, Medical Management system

• Activity Recognition

• IoT based solutions for intelligent health monitoring

• Medical image analyses, Big data, and deep learning-based healthcare and predictive analysis for healthcare improvement

• Cybersecurity, privacy, and security approaches for healthcare Data (Blockchain, encryption, information hiding, biometrics, Application-level encryption, attack detection, and prevention)

• Security and privacy trends in the industrial applications



Published Papers


  • Open Access

    ARTICLE

    Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

    Zhuoqun Xia, Hangyu Hu, Wenjing Li, Qisheng Jiang, Lan Pu, Yicong Shu, Arun Kumar Sangaiah
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 409-430, 2024, DOI:10.32604/cmes.2024.030052
    (This article belongs to the Special Issue: Smart and Secure Solutions for Medical Industry)
    Abstract Early screening of diabetes retinopathy (DR) plays an important role in preventing irreversible blindness. Existing research has failed to fully explore effective DR lesion information in fundus maps. Besides, traditional attention schemes have not considered the impact of lesion type differences on grading, resulting in unreasonable extraction of important lesion features. Therefore, this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator (MPAG) and a lesion localization module (LLM). Firstly, MPAG is used to predict patches of different sizes and generate a weighted attention map based on the prediction score and… More >

  • Open Access

    ARTICLE

    IoT Task Offloading in Edge Computing Using Non-Cooperative Game Theory for Healthcare Systems

    Dinesh Mavaluru, Chettupally Anil Carie, Ahmed I. Alutaibi, Satish Anamalamudi, Bayapa Reddy Narapureddy, Murali Krishna Enduri, Md Ezaz Ahmed
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1487-1503, 2024, DOI:10.32604/cmes.2023.045277
    (This article belongs to the Special Issue: Smart and Secure Solutions for Medical Industry)
    Abstract In this paper, we present a comprehensive system model for Industrial Internet of Things (IIoT) networks empowered by Non-Orthogonal Multiple Access (NOMA) and Mobile Edge Computing (MEC) technologies. The network comprises essential components such as base stations, edge servers, and numerous IIoT devices characterized by limited energy and computing capacities. The central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy consumption. The system operates in discrete time slots and employs a quasi-static approach, with a specific focus on the complexities of… More >

  • Open Access

    ARTICLE

    Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

    Mustufa Haider Abidi, Hisham Alkhalefah, Mohamed K. Aboudaif
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 977-997, 2024, DOI:10.32604/cmes.2023.044169
    (This article belongs to the Special Issue: Smart and Secure Solutions for Medical Industry)
    Abstract The healthcare data requires accurate disease detection analysis, real-time monitoring, and advancements to ensure proper treatment for patients. Consequently, Machine Learning methods are widely utilized in Smart Healthcare Systems (SHS) to extract valuable features from heterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities. These methods are employed across different domains that are susceptible to adversarial attacks, necessitating careful consideration. Hence, this paper proposes a crossover-based Multilayer Perceptron (CMLP) model. The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on the medical… More >

  • Open Access

    ARTICLE

    Threshold-Based Software-Defined Networking (SDN) Solution for Healthcare Systems against Intrusion Attacks

    Laila M. Halman, Mohammed J. F. Alenazi
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1469-1483, 2024, DOI:10.32604/cmes.2023.028077
    (This article belongs to the Special Issue: Smart and Secure Solutions for Medical Industry)
    Abstract The healthcare sector holds valuable and sensitive data. The amount of this data and the need to handle, exchange, and protect it, has been increasing at a fast pace. Due to their nature, software-defined networks (SDNs) are widely used in healthcare systems, as they ensure effective resource utilization, safety, great network management, and monitoring. In this sector, due to the value of the data, SDNs face a major challenge posed by a wide range of attacks, such as distributed denial of service (DDoS) and probe attacks. These attacks reduce network performance, causing the degradation of… More >

    Graphic Abstract

    Threshold-Based Software-Defined Networking (SDN) Solution for Healthcare Systems against Intrusion Attacks

  • Open Access

    ARTICLE

    Multi Head Deep Neural Network Prediction Methodology for High-Risk Cardiovascular Disease on Diabetes Mellitus

    B. Ramesh, Kuruva Lakshmanna
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2513-2528, 2023, DOI:10.32604/cmes.2023.028944
    (This article belongs to the Special Issue: Smart and Secure Solutions for Medical Industry)
    Abstract Major chronic diseases such as Cardiovascular Disease (CVD), diabetes, and cancer impose a significant burden on people and healthcare systems around the globe. Recently, Deep Learning (DL) has shown great potential for the development of intelligent mobile Health (mHealth) interventions for chronic diseases that could revolutionize the delivery of health care anytime, anywhere. The aim of this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases and advance our understanding of the progress made in this… More >

    Graphic Abstract

    Multi Head Deep Neural Network Prediction Methodology for High-Risk Cardiovascular Disease on Diabetes Mellitus

  • Open Access

    ARTICLE

    A Novel Edge-Assisted IoT-ML-Based Smart Healthcare Framework for COVID-19

    Mahmood Hussain Mir, Sanjay Jamwal, Ummer Iqbal, Abolfazl Mehbodniya, Julian Webber, Umar Hafiz Khan
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2529-2565, 2023, DOI:10.32604/cmes.2023.027173
    (This article belongs to the Special Issue: Smart and Secure Solutions for Medical Industry)
    Abstract The lack of modern technology in healthcare has led to the death of thousands of lives worldwide due to COVID- 19 since its outbreak. The Internet of Things (IoT) along with other technologies like Machine Learning can revolutionize the traditional healthcare system. Instead of reactive healthcare systems, IoT technology combined with machine learning and edge computing can deliver proactive and preventive healthcare services. In this study, a novel healthcare edge-assisted framework has been proposed to detect and prognosticate the COVID-19 suspects in the initial phases to stop the transmission of coronavirus infection. The proposed framework… More >

  • Open Access

    ARTICLE

    Analysis and Design of Surgical Instrument Localization Algorithm

    Siyu Lu, Jun Yang, Bo Yang, Zhengtong Yin, Mingzhe Liu, Lirong Yin, Wenfeng Zheng
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 669-685, 2023, DOI:10.32604/cmes.2023.027417
    (This article belongs to the Special Issue: Smart and Secure Solutions for Medical Industry)
    Abstract With the help of surgical navigation system, doctors can operate on patients more intuitively and accurately. The positioning accuracy and real-time performance of surgical instruments are very important to the whole system. In this paper, we analyze and design the detection algorithm of surgical instrument location mark, and estimate the posture of surgical instrument. In addition, we optimized the pose by remapping. Finally, the algorithm of location mark detection proposed in this paper and the posture analysis data of surgical instruments are verified and analyzed through experiments. The final result shows a high accuracy. More >

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