HealthCloud: A system for monitoring health status of heart patients using machine learning and cloud computing

Desai, Forum; Chowdhury, Deepraj; Kaur, Rupinder; Peeters, Marloes; Arya, Rajesh; Wander, Gurpreet Singh; Gill, Sukhpal Singh; Buyya, Rajkumar

doi:10.1016/j.iot.2021.100485

Cited by 46 publications

(18 citation statements)

References 20 publications

(28 reference statements)

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“…The accuracy of the model will improve in classifying COVID‐19 and other X‐ray images as more people use it. In the future, the paper can be expanded upon and integrated into various IoT architectures, such as IoTpi (Shao et al, 2022 ), as well as various fog and cloud computing architectures, such as FogDLearner (Iftikhar et al, 2022 ) and HealthCloud (Desai et al, 2022 ). Additionally, a smart embedded device like a smartwatch can be incorporated with this work (Saleem et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Federated learning based Covid‐19 detection

et al. 2022

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The world is affected by COVID‐19, an infectious disease caused by the SARS‐CoV‐2 virus. Tests are necessary for everyone as the number of COVID‐19 affected individual's increases. So, the authors developed a basic sequential CNN model based on deep and federated learning that focuses on user data security while simultaneously enhancing test accuracy. The proposed model helps users detect COVID‐19 in a few seconds by uploading a single chest X‐ray image. A deep learning‐aided architecture that can handle client and server sides efficiently has been proposed in this work. The front‐end part has been developed using StreamLit, and the back‐end uses a Flower framework. The proposed model has achieved a global accuracy of 99.59% after being trained for three federated communication rounds. The detailed analysis of this paper provides the robustness of this work. In addition, the Internet of Medical Things (IoMT) will improve the ease of access to the aforementioned health services. IoMT tools and services are rapidly changing healthcare operations for the better. Hopefully, it will continue to do so in this difficult time of the COVID‐19 pandemic and will help to push the envelope of this work to a different extent.

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Section: Discussionmentioning

confidence: 99%

Federated learning based Covid‐19 detection

et al. 2022

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“…Gateway devices will be used by IoT/edge devices and end users to communicate with computer systems, abstracting away the interactions with sensors and actuators/effectors located on the edge [38]. The system will communicate with various and multiple instances of IoT applications (such as healthcare, smart city, farming, and weather monitoring) or their digital twins to efficiently provide AI and other autonomic services [39].…”

Section: Iot Applicationsmentioning

confidence: 99%

AI for Next Generation Computing: Emerging Trends and Future Directions

Gill,

Xu,

Ottaviani

et al. 2022

Preprint

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Autonomic computing investigates how systems can achieve (user) specified "control" outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.

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“…En la investigación propuesta por Forum et al [15] se desarrolló una aplicación que pueda monitorear el estado de salud de pacientes con problemas cardíacos teniendo en cuenta que los datos deben poder ser actualizados en tiempo real, los investigadores llegaron a la conclusión que Firebase era la plataforma más adecuada para manejar la data en la nube y como algunos puntos mencionados esta que esta herramienta provee de varios servicios como lo es Analytics que proporcionan datos y gráficos de las interacciones del usuario. Como punto final los autores destacan la precisión de la aplicación al ofrecer consejos y predicciones en servicios tan delicados como lo es el campo de la salud.…”

Section: Revisión De La Literaturaunclassified

Development of a mobile application for the registration and attendance control of university students based on Machine Learning

Silva¹,

Samayani²,

Alejandro³

et al. 2022

Proceedings of the 2nd LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD 202

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This research aims to develop an Android mobile application that allows registration and control of attendance in educational institutions based on Machine Learning and Cloud Computing technology. For this, the project management methodology XP (EXtreme Programming) is used, in whose phases the entire process of implementation of the application and the launch are detailed; likewise, Firebase is used as a service for database management. The application tests were carried out on the students of the seventh semester of the Professional School of Systems Engineering of the University of Peru. The attendance control system was connected to a database that stores student information and attendance records. To implement the QR code attendance record functionality, the Firebase Machine Learning kit was used to Validate the student attendance record. In addition, the user interface displays the attendance records from an attractive, intuitive approach that is easy for teachers and students to manage. The results of the research show that the use of the application by teachers and students reduces and optimize the time invested in the attendance registration process compared to traditional methods, according to the satisfaction and acceptance criteria of the mobile application called "ASYS."

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HealthCloud: A system for monitoring health status of heart patients using machine learning and cloud computing

Cited by 46 publications

References 20 publications

Federated learning based Covid‐19 detection

Federated learning based Covid‐19 detection

AI for Next Generation Computing: Emerging Trends and Future Directions

Development of a mobile application for the registration and attendance control of university students based on Machine Learning

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