IFCnCov: An IoT‐based smart diagnostic architecture for COVID‐19

Pati, Abhilash; Parhi, Manoranjan; Pattanayak, Binod Kumar; Pati, Bibudhendu

doi:10.1002/spe.3247

Cited by 3 publications

(1 citation statement)

References 54 publications

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“…In this study, the WBBN model undergoes thorough training and testing using a distinct distribution of the PIDD dataset comprising 768 records. The primary evaluative parameter employed in this study is accuracy, which is the correctness of predictions made by a predictive model for diabetes diagnosis or classification (Nayak et al, 2023 ; Panigrahi et al, 2023 ; Pati et al, 2023 ).…”

Section: Resultsmentioning

confidence: 99%

Weighted Bayesian Belief Network for diabetics: a predictive model

Kharya,

Soni,

Pati

et al. 2024

Front. Artif. Intell.

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Diabetes is an enduring metabolic condition identified by heightened blood sugar levels stemming from insufficient production of insulin or ineffective utilization of insulin within the body. India is commonly labeled as the “diabetes capital of the world” owing to the widespread prevalence of this condition. To the best of the authors' last knowledge updated on September 2021, approximately 77 million adults in India were reported to be affected by diabetes, reported by the International Diabetes Federation. Owing to the concealed early symptoms, numerous diabetic patients go undiagnosed, leading to delayed treatment. While Computational Intelligence approaches have been utilized to improve the prediction rate, a significant portion of these methods lacks interpretability, primarily due to their inherent black box nature. Rule extraction is frequently utilized to elucidate the opaque nature inherent in machine learning algorithms. Moreover, to resolve the black box nature, a method for extracting strong rules based on Weighted Bayesian Association Rule Mining is used so that the extracted rules to diagnose any disease such as diabetes can be very transparent and easily analyzed by the clinical experts, enhancing the interpretability. The WBBN model is constructed utilizing the UCI machine learning repository, demonstrating a performance accuracy of 95.8%.

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Section: Resultsmentioning

confidence: 99%

Weighted Bayesian Belief Network for diabetics: a predictive model

Kharya,

Soni,

Pati

et al. 2024

Front. Artif. Intell.

Self Cite

View full text Add to dashboard Cite

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Accurate detection of coronavirus cases using deep learning with attention mechanism and genetic algorithm

Kara

2024

Multimed Tools Appl

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The novel coronavirus disease has caused severe threats to the daily life and health of people all over the world. Hence, early detection and timely treatment of this disease are significant to prevent the coronavirus's spread and ensure more effective patient care. This work adopted an integrated framework comprising deep learning and attention mechanism to provide a more effective and reliable diagnosis. This framework consists of two convolution neural network (CNN), a bidirectional LSTM, two fully-connected layers (FCL), and an attention mechanism. The main aim of the proposed framework is to reveal a promising approach based on deep learning for early and timely detection of coronavirus disease. For greater accuracy, the framework's hyperparameters are tuned by means of a genetic algorithm. The effectiveness of the proposed framework has been examined utilizing a public dataset including 18 different blood findings from Albert Einstein Israelita Hospital in Sao Paulo, Brazil. Additionally, within the experimental studies, the proposed framework is subjected to comparison with the state-of-the-art techniques, evaluated across various metrics. Based on the derived consequences, the proposed framework has yielded enhancements in accuracy, recall, precision, and F1-score, registering approximate improvements of 1.27%, 4.07%, 3.20%, and 2.88%, respectively, as measured against the second-best rates.

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Internet of Things with Deep Learning Techniques for Pandemic Detection: A Comprehensive Review of Current Trends and Open Issues

Ajagbe,

Mudali,

Adigun

2024

Electronics

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Technological advancements for diverse aspects of life have been made possible by the swift development and application of Internet of Things (IoT) based technologies. IoT technologies are primarily intended to streamline various processes, guarantee system (technology or process) efficiency, and ultimately enhance the quality of life. An effective method for pandemic detection is the combination of deep learning (DL) techniques with the IoT. IoT proved beneficial in many healthcare domains, especially during the last worldwide health crisis: the COVID-19 pandemic. Using studies published between 2019 and 2024, this review seeks to examine the various ways that IoT-DL models contribute to pandemic detection. We obtained the titles, keywords, and abstracts of the chosen papers by using the Scopus and Web of Science (WoS) databases. This study offers a comprehensive review of the literature and unresolved problems in applying IoT and DL to pandemic detection in 19 papers that were eligible to be read from start to finish out of 2878 papers that were initially accessed. To provide practitioners, policymakers, and researchers with useful information, we examine a range of previous study goals, approaches used, and the contributions made in those studies. Furthermore, by considering the numerous contributions of IoT technologies and DL as they help in pandemic preparedness and control, we provide a structured overview of the current scientific trends and open issues in this field. This review provides a thorough overview of the state-of-the-art routing approaches currently in use, as well as their limits and potential future developments, making it an invaluable resource for DL researchers and practitioners and it is a useful tool for multidisciplinary research.

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IFCnCov: An IoT‐based smart diagnostic architecture for COVID‐19

Cited by 3 publications

References 54 publications

Weighted Bayesian Belief Network for diabetics: a predictive model

Weighted Bayesian Belief Network for diabetics: a predictive model

Accurate detection of coronavirus cases using deep learning with attention mechanism and genetic algorithm

Internet of Things with Deep Learning Techniques for Pandemic Detection: A Comprehensive Review of Current Trends and Open Issues

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