Exploring Temporal Analytics in Fog-Cloud Architecture for Smart Office HealthCare

Bhatia, Munish; Sood, Sandeep K.

doi:10.1007/s11036-018-0991-5

Cited by 68 publications

(38 citation statements)

References 47 publications

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“…SOM strategy empowers dynamic introduction dependent on the shaded coding plan. The proposed T-RNN model for forecasting is fused with U-network for executing SOM method [ 20 ]. Figure 6 represents the U-network details.…”

Section: Proposed Modelmentioning

confidence: 99%

RETRACTED ARTICLE: A novel IoT–fog–cloud-based healthcare system for monitoring and predicting COVID-19 outspread

et al. 2021

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Rapid communication of viral sicknesses is an arising public medical issue across the globe. Out of these, COVID-19 is viewed as the most critical and novel infection nowadays. The current investigation gives an effective framework for the monitoring and prediction of COVID-19 virus infection (C-19VI). To the best of our knowledge, no research work is focused on incorporating IoT technology for C-19 outspread over spatial–temporal patterns. Moreover, limited work has been done in the direction of prediction of C-19 in humans for controlling the spread of COVID-19. The proposed framework includes a four-level architecture for the expectation and avoidance of COVID-19 contamination. The presented model comprises COVID-19 Data Collection (C-19DC) level, COVID-19 Information Classification (C-19IC) level, COVID-19-Mining and Extraction (C-19ME) level, and COVID-19 Prediction and Decision Modeling (C-19PDM) level. Specifically, the presented model is used to empower a person/community to intermittently screen COVID-19 Fever Measure (C-19FM) and forecast it so that proactive measures are taken in advance. Additionally, for prescient purposes, the probabilistic examination of C-19VI is quantified as degree of membership, which is cumulatively characterized as a COVID-19 Fever Measure (C-19FM). Moreover, the prediction is realized utilizing the temporal recurrent neural network. Additionally, based on the self-organized mapping technique, the presence of C-19VI is determined over a geographical area. Simulation is performed over four challenging datasets. In contrast to other strategies, altogether improved outcomes in terms of classification efficiency, prediction viability, and reliability were registered for the introduced model.

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Section: Proposed Modelmentioning

confidence: 99%

RETRACTED ARTICLE: A novel IoT–fog–cloud-based healthcare system for monitoring and predicting COVID-19 outspread

et al. 2021

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“…It plays important in global communication among millions of devices with wired/wireless sensors, electronic appliances like Television, Refrigerator, etc., connected to the Internet [5]. Then it plays an amazing role in many fields such as Agriculture [6,7], healthcare [8,9], entertainments [10], automobiles [11], sports [12], home [13,14], industrial appliances [15], business [16], etc. The reasons…”

Section: Introductionmentioning

confidence: 99%

A study on medical Internet of Things and Big Data in personalized healthcare system

Jagadeeswari

Subramaniyaswamy

Ravi

et al. 2018

Health Inf Sci Syst

253

118

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Personalized healthcare systems deliver e-health services to fulfill the medical and assistive needs of the aging population. Internet of Things (IoT) is a significant advancement in the Big Data era, which supports many real-time engineering applications through enhanced services. Analytics over data streams from IoT has become a source of user data for the healthcare systems to discover new information, predict early detection, and makes decision over the critical situation for the improvement of the quality of life. In this paper, we have made a detailed study on the recent emerging technologies in the personalized healthcare systems with the focus towards cloud computing, fog computing, Big Data analytics, IoT and mobile based applications. We have analyzed the challenges in designing a better healthcare system to make early detection and diagnosis of diseases and discussed the possible solutions while providing e-health services in secure manner. This paper poses a light on the rapidly growing needs of the better healthcare systems in real-time and provides possible future work guidelines.

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“…Within the same perspective, Alghamlas and Alabduljabbar (2019) proposed a web-based application that assesses the suitability of IT students' skills for being recruited in the Saudi labor market. Further application examples are given in (Bhatia et al 2018), where the proposed system mine large volume of students' data collected from their various usage of applications in the cloud. Students' academic skills are inferred and used to predict their employability at an early stage.…”

Section: Associating (Skills Mapping)mentioning

confidence: 99%

Employability prediction: a survey of current approaches, research challenges and applications

Mezhoudi

Alghamdi

Aljunaid

et al. 2021

J Ambient Intell Human Comput

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Student employability is crucial for educational institutions as it is often used as a metric for their success. The job market landscape, however, more than ever dynamic, is evolving due to the globalization, automation, and recent advances in Artificial Intelligence. Identifying the significant factors affecting employability, as well as the requirements of the new job market can tremendously help all stakeholders. Knowing their weaknesses and strengths, students might better plan their career. Instructors can focus on more appropriate skill sets to meet the requirements of rapidly evolving labor markets. Program managers can anticipate and improve their curriculum to build new competencies, both for educating, training and reskilling current and future workers. All these combined efforts certainly can contribute to increasing employability. Data driven and machine learning techniques have been extensively used in various fields of educational data mining. More and more studies are investigating data mining techniques for the prediction of employability. Yet, these studies show a lot of variation, for instance, with respect to the data used, the methods adopted, or even the research questions posed. In this paper, we aim to depict a clear picture of the art, clarifying for each standard step of data mining process, the differences, and similarities of these studies, along with further suggestions. Thus, this survey provides a comprehensive roadmap, enabling the application of data mining for employability.

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Exploring Temporal Analytics in Fog-Cloud Architecture for Smart Office HealthCare

Cited by 68 publications

References 47 publications

RETRACTED ARTICLE: A novel IoT–fog–cloud-based healthcare system for monitoring and predicting COVID-19 outspread

RETRACTED ARTICLE: A novel IoT–fog–cloud-based healthcare system for monitoring and predicting COVID-19 outspread

A study on medical Internet of Things and Big Data in personalized healthcare system

Employability prediction: a survey of current approaches, research challenges and applications

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