Prediction and diagnosis of vertebral tumors on the Internet of Medical Things Platform using geometric rough propagation neural network

Fouad, H.; Soliman, Ahmed M.; Hassanein, Azza S.; Al-Feel, Haytham

doi:10.1007/s00521-020-04935-2

Cited by 8 publications

(4 citation statements)

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“…The introduced IGPLONN approach has an effective objective function, and each feature has a specific penalty value that effectively predicts the relationship between each feature. The successful analysis process improves the overall recognition rate [48,49], and the obtained result is shown in figure12. As inferred from the Eq(2), where is denoted as particular input from the list, = { 1 … . }…”

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confidence: 89%

Predicting Neurological Disorders Linked to Oral Cavity Manifestations Using an IoMT-Based Optimized Neural Networks

et al. 2020

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Anatomically, oral cavity and central nervous system have a close relationship; the mouth and face are the location for 30-40% of the body's sensory and motor nerves. The identification of orofacial manifestations of neurological disorders is usually in direct relation with the responsibilities of a dental surgeon. Therefore, familiarizing dental surgeons with theses manifestations is essential to have better recognition, diagnosis, and correct decisions upon treating their associated Neurological Disorders. These manifestations should be efficiently analyzed using novel effective techniques since their related neurological disorders need to be early identified to avoid serious consequences. Furthermore, preventive dental care for patients with neurological disorders and all kind of rehabilitative treatments necessitates well-planned and effective novel approaches. The Internet of Medical Thing (IoMT) is a relatively new technology that has revolutionized health care sector drastically since last decade. It's a combination of medical sensors and wearable devices that can connect to health care storage systems via networking technologies allowing the transfer of medical data over a secure network. The data transferred are of utmost importance in diseases diagnosis and treatment. In this paper, an IoMT-based Intelligent Guided Particle Local Search with Optimized Neural Networks (IGPLONN) approach is proposed. Firstly, dental data are collected from the International Collaboration on Cancer Reporting (ICCR) oral cavity and central nervous system. Secondly, features are extracted from data and IGPLONN algorithm is utilized to select the effective features by minimizing the feature dimension that helps improve the overall prediction rate. Finally, the obtained features are transferred to the central health application through the IoMT platform where they can be analyzed by dental practitioners for neurological disorders prediction. The hybrid optimized technique improves the overall oral-linked neurological diseases detection rate. Moreover, it efficiently manages the forecast parameters that are used to predict the dental metastasis with minimum computational complexity. The performance of the proposed system has been experimentally evaluated on MATLAB to verify its excellence. The results revealed that proposed IoMT-based IGPLONN method attains the maximum accuracy of 98.3% compared to other methods. INDEX TERMS Oral cavity, neurological disorder, central nervous system, intelligent guided particle local search algorithm, neural network, Internet of Medical Things (IoMT).

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confidence: 89%

Predicting Neurological Disorders Linked to Oral Cavity Manifestations Using an IoMT-Based Optimized Neural Networks

et al. 2020

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“…Today, ANNs find applications in a wide array of scientific and technological domains, especially within the industrial sector. They serve as pivotal tools for diverse applications such as process monitoring and control [109], power systems [110], medical diagnosis [111], stock market prediction [112], nuclear plant control [113], robotics [114], communication systems [115], data mining [116], pattern recognition [117], and decision fusion [118].…”

Section: Machine Learningmentioning

confidence: 99%

A Review on Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing

Lee

2023

Processes

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In the evolving landscape of manufacturing, the integration of intelligent control theory stands as a pivotal advancement, driving both process optimization and the paradigm of smart manufacturing. This review delves into the multifaceted applications of intelligent control theory, emphasizing its role in equipment, operations, and controls optimization. With a focus on three primary methodologies—fuzzy logic, neural networks, and genetic algorithms—the paper elucidates their biological parallels and their significance in simulation, modeling, and optimization. The transformative potential of smart manufacturing, synonymous with Industry 4.0, is also explored, highlighting its foundation in data, automation, and artificial intelligence. Drawing from a comprehensive analysis of recent literature, the review underscores the growing interest in this domain, as evidenced by the surge in publications and citations over the past decade. The overarching aim is to provide contemporary discourse on the applications and implications of intelligent control theory in the realms of process optimization and smart manufacturing.

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“…The growth of information technology and internet facility are used to understand the services and tasks which is accepted by the concerned individuals who are a part of the network. The internet of things (IoT) is support the information technology by transmitting, processing, and storing the data [7,8]. Telemedicine insures to make use of innovative technology in the medical field is available in remote communities and provides patients with general care without travel long distances to meet medical professionals for consultation.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Telemedicine Framework for a Smart Healthcare Ecosystem

2020

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The healthcare sector is one of the most rapidly growing sectors globally. With the evergrowing technology, patient care, regulatory compliance, and digital transformation, there is an increased need for healthcare sectors to collaborate with all stakeholders-both within the healthcare ecosystem and in concurring industries. In recent times, telemedicine has proven to provide high quality, affordable, and predominantly adapted healthcare services. However, telemedicine suffers from several risks in implementation, such as data breach, restricted access across medical fraternity, incorrect diagnosis and prescription, fraud, and abuse. In this work, introduce blockchain-based framework that would unlock the future of the healthcare sector and improved services. Our proposed solution utilizing Ethereum smart contracts to develop a transparent, tamper-proof telemedicine healthcare framework, and ensure the integrity of sensitive patient data eliminating a central administrator. Moreover, the smart contract regulates the interaction between all the parties involved in the network and keeps the patient meticulously informed about the transactions in the network.

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Prediction and diagnosis of vertebral tumors on the Internet of Medical Things Platform using geometric rough propagation neural network

Cited by 8 publications

References 30 publications

Predicting Neurological Disorders Linked to Oral Cavity Manifestations Using an IoMT-Based Optimized Neural Networks

Predicting Neurological Disorders Linked to Oral Cavity Manifestations Using an IoMT-Based Optimized Neural Networks

A Review on Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing

Decentralized Telemedicine Framework for a Smart Healthcare Ecosystem

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