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  • Open Access

    ARTICLE

    Developed Fall Detection of Elderly Patients in Internet of Healthcare Things

    Omar Reyad1,2, Hazem Ibrahim Shehata1,3, Mohamed Esmail Karar1,4,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1689-1700, 2023, DOI:10.32604/cmc.2023.039084

    Abstract Falling is among the most harmful events older adults may encounter. With the continuous growth of the aging population in many societies, developing effective fall detection mechanisms empowered by machine learning technologies and easily integrable with existing healthcare systems becomes essential. This paper presents a new healthcare Internet of Health Things (IoHT) architecture built around an ensemble machine learning-based fall detection system (FDS) for older people. Compared to deep neural networks, the ensemble multi-stage random forest model allows the extraction of an optimal subset of fall detection features with minimal hyperparameters. The number of cascaded random forest stages is automatically… More >

  • Open Access

    ARTICLE

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

    Mahmood Hussain Mir1,*, Sanjay Jamwal1, Ummer Iqbal2, Abolfazl Mehbodniya3, Julian Webber3, Umar Hafiz Khan4

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2529-2565, 2023, DOI:10.32604/cmes.2023.027173

    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 is based on edge computing… More >

  • Open Access

    ARTICLE

    Privacy Preserved Brain Disorder Diagnosis Using Federated Learning

    Ali Altalbe1,2,*, Abdul Rehman Javed3

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2187-2200, 2023, DOI:10.32604/csse.2023.040624

    Abstract Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence (AI) algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy. Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s. Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients. The healthcare industry faces two significant challenges: security and privacy issues and the personalization of cloud-trained AI models. This paper proposes a Deep Neural Network (DNN) based approach embedded in a federated… More >

  • Open Access

    ARTICLE

    CD-FL: Cataract Images Based Disease Detection Using Federated Learning

    Arfat Ahmad Khan1, Shtwai Alsubai2, Chitapong Wechtaisong3,*, Ahmad Almadhor4, Natalia Kryvinska5,*, Abdullah Al Hejaili6, Uzma Ghulam Mohammad7

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1733-1750, 2023, DOI:10.32604/csse.2023.039296

    Abstract A cataract is one of the most significant eye problems worldwide that does not immediately impair vision and progressively worsens over time. Automatic cataract prediction based on various imaging technologies has been addressed recently, such as smartphone apps used for remote health monitoring and eye treatment. In recent years, advances in diagnosis, prediction, and clinical decision support using Artificial Intelligence (AI) in medicine and ophthalmology have been exponential. Due to privacy concerns, a lack of data makes applying artificial intelligence models in the medical field challenging. To address this issue, a federated learning framework named CD-FL based on a VGG16… More >

  • Open Access

    ARTICLE

    Chest Radiographs Based Pneumothorax Detection Using Federated Learning

    Ahmad Almadhor1,*, Arfat Ahmad Khan2, Chitapong Wechtaisong3,*, Iqra Yousaf4, Natalia Kryvinska5, Usman Tariq6, Haithem Ben Chikha1

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1775-1791, 2023, DOI:10.32604/csse.2023.039007

    Abstract Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse, causing air to enter the pleural cavity, the area close to the lungs and chest wall. The most persistent disease, as well as one that necessitates particular patient care and the privacy of their health records. The radiologists find it challenging to diagnose pneumothorax due to the variations in images. Deep learning-based techniques are commonly employed to solve image categorization and segmentation problems. However, it is challenging to employ it in the medical field due to privacy issues and a lack of data. To address this issue, a… More >

  • Open Access

    ARTICLE

    Securing Healthcare Data in IoMT Network Using Enhanced Chaos Based Substitution and Diffusion

    Musheer Ahmad1, Reem Ibrahim Alkanhel2,*, Naglaa F. Soliman2, Abeer D. Algarni2, Fathi E. Abd El-Samie3, Walid El-Shafai3,4

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2361-2380, 2023, DOI:10.32604/csse.2023.038439

    Abstract Patient privacy and data protection have been crucial concerns in E-healthcare systems for many years. In modern-day applications, patient data usually holds clinical imagery, records, and other medical details. Lately, the Internet of Medical Things (IoMT), equipped with cloud computing, has come out to be a beneficial paradigm in the healthcare field. However, the openness of networks and systems leads to security threats and illegal access. Therefore, reliable, fast, and robust security methods need to be developed to ensure the safe exchange of healthcare data generated from various image sensing and other IoMT-driven devices in the IoMT network. This paper… More >

  • Open Access

    REVIEW

    A Systematic Review on the Internet of Medical Things: Techniques, Open Issues, and Future Directions

    Apurva Sonavane1, Aditya Khamparia2,*, Deepak Gupta3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1525-1550, 2023, DOI:10.32604/cmes.2023.028203

    Abstract IoT usage in healthcare is one of the fastest growing domains all over the world which applies to every age group. Internet of Medical Things (IoMT) bridges the gap between the medical and IoT field where medical devices communicate with each other through a wireless communication network. Advancement in IoMT makes human lives easy and better. This paper provides a comprehensive detailed literature survey to investigate different IoMT-driven applications, methodologies, and techniques to ensure the sustainability of IoMT-driven systems. The limitations of existing IoMT frameworks are also analyzed concerning their applicability in real-time driven systems or applications. In addition to… More >

  • Open Access

    ARTICLE

    Computing and Implementation of a Controlled Telepresence Robot

    Ali A. Altalbe1,2,*, Aamir Shahzad3, Muhammad Nasir Khan4

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1569-1585, 2023, DOI:10.32604/iasc.2023.039124

    Abstract The development of human-robot interaction has been continuously increasing for the last decades. Through this development, it has become simpler and safe interactions using a remotely controlled telepresence robot in an insecure and hazardous environment. The audio-video communication connection or data transmission stability has already been well handled by fast-growing technologies such as 5G and 6G. However, the design of the physical parameters, e.g., maneuverability, controllability, and stability, still needs attention. Therefore, the paper aims to present a systematic, controlled design and implementation of a telepresence mobile robot. The primary focus of this paper is to perform the computational analysis… More >

  • Open Access

    ARTICLE

    DeepGan-Privacy Preserving of HealthCare System Using DL

    Sultan Mesfer Aldossary*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2199-2212, 2023, DOI:10.32604/iasc.2023.038243

    Abstract The challenge of encrypting sensitive information of a medical image in a healthcare system is still one that requires a high level of computing complexity, despite the ongoing development of cryptography. After looking through the previous research, it has become clear that the security issues still need to be looked into further because there is room for expansion in the research field. Recently, neural networks have emerged as a cost-effective and effective optimization strategy in terms of providing security for images. This revelation came about as a result of current developments. Nevertheless, such an implementation is a technique that is… More >

  • Open Access

    ARTICLE

    A Detailed Study on IoT Platform for ECG Monitoring Using Transfer Learning

    Md Saidul Islam*

    Journal on Internet of Things, Vol.4, No.3, pp. 127-140, 2022, DOI:10.32604/jiot.2022.037489

    Abstract Internet of Things (IoT) technologies used in health have the potential to address systemic difficulties by offering tools for cost reduction while improving diagnostic and treatment efficiency. Numerous works on this subject focus on clarifying the constructs and interfaces between various components of an IoT platform, such as knowledge generation via smart sensors collecting biosignals from the human body and processing them via data mining and, in recent times, deep neural networks offered to host on cloud computing architecture. These approaches are intended to assist healthcare professionals in their daily activities. In this comparative research, we discuss the construction of… More >

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