COVID-19 assessment using HMM cough recognition system

Hamidi, Mohamed; Zealouk, Ouissam; Satori, Hassan; Laaidi, Naouar; Salek, Amine

doi:10.1007/s41870-022-01120-7

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…Another study used a cough dataset for the assessment of COVID-19 [36]. This study aimed to employ an automatic speech identification system based on hidden Markov models (HMMs) to analyze cough signals and determine whether they originated from individuals who were healthy or COVID-19 patients.…”

Section: Related Workmentioning

confidence: 99%

IoT-Based Framework for COVID-19 Detection Using Machine Learning Techniques

Al-Khaleefa,

Al-Musawi,

Saeed

2023

Sci

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Current advancements in the technology of the Internet of Things (IoT) have led to the proliferation of various applications in the healthcare sector that use IoT. Recently, it has been shown that voice signal data of the respiratory system (i.e., breathing, coughing, and speech) can be processed through machine learning techniques to detect different diseases of this system such as COVID-19, considered an ongoing global pandemic. Therefore, this paper presents a new IoT framework for the identification of COVID-19 based on breathing voice samples. Using IoT devices, voice samples were captured and transmitted to the cloud, where they were analyzed and processed using machine learning techniques such as the naïve Bayes (NB) algorithm. In addition, the performance of the NB algorithm was assessed based on accuracy, sensitivity, specificity, precision, F-Measure, and G-Mean. The experimental findings showed that the proposed NB algorithm achieved 82.97% accuracy, 75.86% sensitivity, 94.44% specificity, 95.65% precision, 84.61% F-Measure, and 84.64% G-Mean.

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Section: Related Workmentioning

confidence: 99%

IoT-Based Framework for COVID-19 Detection Using Machine Learning Techniques

Al-Khaleefa,

Al-Musawi,

Saeed

2023

Sci

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“…When predicting the genes coding for proteins, the HMM shows the researchers with the greatest accuracy and foresight, and it has broken the stereotype [4]. Apart from predicting the internal part of human beings, the HMM is also a good tool to check the outer production of people, including speech recognition [1] and face identification [3]. In the post-pandemic period, it is also a highlighted issue which is COVID-19 detection.…”

Section: Literature Reviewmentioning

confidence: 99%

The Stock Prices Prediction Performance of Hidden Markov Models in the Luxury Category

2023

ferm

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The stock market is the place where issued stocks are transferred, traded and circulated, including exchange market and over-the-counter market. Because it is based on the issuance market, it is also called the secondary market. The structure and trading activities of the stock market are more complex than the issuance market (the primary market), and its role and influence are also greater. It is precisely because of its complex systems and processes that achieving accurate predictions is very difficult and challenging. The Hidden Markov Model is not a commonly used model in predicting the next day's stock price. Hence, I will focus on the Hidden Markov Model with four luxury giants to prove whether the HMM is suitable for that industry, and which company fitted most.

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