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  • 1. Multi-Controller Model for Improving the Performance of IoT Networks

    Descrizione:
    Internet of Things (IoT), a strong integration of radio frequency identifier (RFID), wireless devices, and sensors, has provided a difficult yet strong chance to shape existing systems into intelligent ones. Many new applications have been created in the last few years. As many as a million objects are anticipated to be linked together to form a network that can infer meaningful conclusions based on raw data. This means any IoT system is heterogeneous when it comes to the types of devices that are used in the system and how they communicate with each other. In most cases, an IoT network can be described as a layered network, with multiple tiers stacked on top of each other. IoT network performance improvement typically focuses on a single layer. As a result, effectiveness in one layer may rise while that of another may fall. Ultimately, the achievement issue must be addressed by considering improvements in all layers of an IoT network, or at the very least, by considering contiguous hierarchical levels. Using a parallel and clustered architecture in the device layer, this paper examines how to improve the performance of an IoT network’s controller layer. A particular clustered architecture at the device level has been shown to increase the performance of an IoT network by 16% percent. Using a clustered architecture at the device layer in conjunction with a parallel architecture at the controller layer boosts performance by 24% overall.
    Parola chiave:
    Layered Networking, Parallel Architectures, Device Level Clustering, Internet of Things, Topology Binding, Performance Optimization, and Radio-frequency Identification
    Soggetto:
    Artificial Intelligence and Data Science
    Creatore:
    Singh, Ninni , Roy, Sudipta , Saad, Ismail , Rahebi, Javad , Farzamnia, Ali , Gunjan, Vinit Kumar , Davanam, Ganesh , and Kallam, Suresh
    Owner:
    n.sakthivel@jioinstitute.edu.in
    Editore:
    MDPI
    luogo:
    Switzerland
    Lingua:
    English
    Data caricata:
    11-02-2023
    Data modificata:
    16-02-2023
    data di creazione:
    01-11-2022
    Rights Statement Tesim:
    In Copyright
    License Tesim:
    All rights reserved
    Resource Type:
    Article
    Identifier:
    10.3390/en15228738

  • 2. Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

    Descrizione:
    The global healthcare sector continues to grow rapidly and is reflected as one of the fastestgrowing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.
    Parola chiave:
    Healthcare, Precision Medicine, Artificial Intelligence, Computer Vision, Deep Learning, Medical Imaging, XAI, and Supervised Learning
    Soggetto:
    Artificial Intelligence and Data Science
    Creatore:
    Roy, Sudipta , Lim, Se-Jung , and Meena, Tanushree
    Collaboratore:
    Jio Institute, CVMI-Computer Vision in Medical Imaging Project
    Owner:
    n.sakthivel@jioinstitute.edu.in
    Editore:
    MDPI
    luogo:
    Switzerland and India
    Lingua:
    English
    Data caricata:
    10-02-2023
    Data modificata:
    16-02-2023
    data di creazione:
    01-10-2022
    Rights Statement Tesim:
    In Copyright
    License Tesim:
    All rights reserved
    Resource Type:
    Article
    Identifier:
    10.3390/diagnostics12102549