Prediction of COVID-19 using Time-Sliding Window: The case of Piauí State - Brazil

Santos, Patrick Ryan Sales dos; Souza, Lucas B. M. de; Lelis, Samuel P. B. D.; Ribeiro, Hector B.; Borges, Fábbio A. S.; Silva, Romuere; Filho, Antonio Oseas Carvalho; Araújo, Flávio H. D.; Rabelo, Ricardo A. L.; Rodrigues, Joel J. P. C.

doi:10.1109/healthcom49281.2021.9399048

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…Also, limited genome sequences are available and considered in this model. Santos et al [153] also proposed a COVID-19 prediction model, for Brazil, using a time-sliding window algorithm.…”

Section: Applications Of Machine Learning Models In Pandemic Managementmentioning

confidence: 99%

Disaster and Pandemic Management Using Machine Learning: A Survey

Chamola

Hassija

Gupta

et al. 2021

IEEE Internet Things J.

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This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.

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“…Also, limited genome sequences are available and considered in this model. Santos et al [153] also proposed a COVID-19 prediction model, for Brazil, using a time-sliding window algorithm.…”

Section: Applications Of Machine Learning Models In Pandemic Managementmentioning

confidence: 99%

Disaster and Pandemic Management Using Machine Learning: A Survey

Chamola

Hassija

Gupta

et al. 2021

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…-19 systems can quickly diagnose COVID-19 pathogens and found different types of attacks [24] – [28] . In addition, DL Inference models were tested, including acoustic emission disturbances to the classifier, launching a black-box attack using the Clarifai REST API model, and using the back door attack to update the model [29] .…”

Section: Related Workmentioning

confidence: 99%

Trustworthy and Intelligent COVID-19 Diagnostic IoMT Through XR and Deep-Learning-Based Clinic Data Access

Tai

Gao

et al. 2021

IEEE Internet Things J.

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This article presents a novel extended reality (XR) and deep-learning-based Internet-of-Medical-Things (IoMT) solution for the COVID-19 telemedicine diagnostic, which systematically combines virtual reality/augmented reality (AR) remote surgical plan/rehearse hardware, customized 5G cloud computing and deep learning algorithms to provide real-time COVID-19 treatment scheme clues. Compared to existing perception therapy techniques, our new technique can significantly improve performance and security. The system collected 25 clinic data from the 347 positive and 2270 negative COVID-19 patients in the Red Zone by 5G transmission. After that, a novel auxiliary classifier generative adversarial network-based intelligent prediction algorithm is conducted to train the new COVID-19 prediction model. Furthermore, The Copycat network is employed for the model stealing and attack for the IoMT to improve the security performance. To simplify the user interface and achieve an excellent user experience, we combined the Red Zone’s guiding images with the Green Zone’s view through the AR navigate clue by using 5G. The XR surgical plan/rehearse framework is designed, including all COVID-19 surgical requisite details that were developed with a real-time response guaranteed. The accuracy, recall, F 1 -score, and area under the ROC curve (AUC) area of our new IoMT were 0.92, 0.98, 0.95, and 0.98, respectively, which outperforms the existing perception techniques with significantly higher accuracy performance. The model stealing also has excellent performance, with the AUC area of 0.90 in Copycat slightly lower than the original model. This study suggests a new framework in the COVID-19 diagnostic integration and opens the new research about the integration of XR and deep learning for IoMT implementation.

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“…It is possible making predictions based on data available to obtain an estimation of the number of contaminants over weeks. This type of study is done using linear regression techniques and artificial intelligence, as in [ 31 ]. It uses a sliding window method based on data obtained from the State Health Departments of Piauí, Rio de Janeiro, São Paulo, Santa Catarina, and Rio Grande do Sul states (in Brazil).…”

Section: Introductionmentioning

confidence: 99%

Air–Oxygen Blenders for Mechanical Ventilators: A Literature Review

Soares

Almeida

Menezes

et al. 2022

Sensors

Self Cite

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Respiratory diseases are one of the most common causes of death in the world and this recent COVID-19 pandemic is a key example. Problems such as infections, in general, affect many people and depending on the form of transmission they can spread throughout the world and weaken thousands of people. Two examples are severe acute respiratory syndrome and the recent coronavirus disease. These diseases have mild and severe forms, in which patients gravely affected need ventilatory support. The equipment that serves as a basis for operation of the mechanical ventilator is the air–oxygen blender, responsible for carrying out the air–oxygen mixture in the proper proportions ensuring constant supply. New blender models are described in the literature together with applications of control techniques, such as Proportional, Integrative and Derivative (PID); Fuzzy; and Adaptive. The results obtained from the literature show a significant improvement in patient care when using automatic controls instead of manual adjustment, increasing the safety and accuracy of the treatment. This study presents a deep review of the state of the art in air–oxygen benders, identifies the most relevant characteristics, performs a comparison study considering the most relevant available solutions, and identifies open research directions in the topic.

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Prediction of COVID-19 using Time-Sliding Window: The case of Piauí State - Brazil

Cited by 7 publications

References 19 publications

Disaster and Pandemic Management Using Machine Learning: A Survey

Disaster and Pandemic Management Using Machine Learning: A Survey

Trustworthy and Intelligent COVID-19 Diagnostic IoMT Through XR and Deep-Learning-Based Clinic Data Access

Air–Oxygen Blenders for Mechanical Ventilators: A Literature Review

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