Towards an ML-based semantic IoT for pandemic management: A survey of enabling technologies for COVID-19

Chahbandarian, Ghazar; Kamalov, Firuz; Messiry, Haythem El; Al-Gindy, Ahmed

doi:10.1016/j.neucom.2023.01.007

Cited by 16 publications

(6 citation statements)

References 142 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rita Zgheib et al reviewed the importance of artificial intelligence with machine learning and semantic reasoning in the current scenario of digital healthcare systems. They illustrated and analyzed the relevance of AI and ML technologies in handling the COVID-19 pandemic [ 8 ]. Yash Jain et al developed an ML-based healthcare management system which can act as a virtual doctor to provide a preliminary diagnosis based on the information provided by the subject.…”

Section: Related Workmentioning

confidence: 99%

Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

2023

View full text Add to dashboard Cite

Emotion recognition becomes an important aspect in the development of human-machine interaction (HMI) systems. Positive emotions impact our lives positively, whereas negative emotions may cause a reduction in productivity. Emotionally intelligent systems such as chatbots and artificially intelligent assistant modules help make our daily life routines effortless. Moreover, a system which is capable of assessing the human emotional state would be very helpful to assess the mental state of a person. Hence, preventive care could be offered before it becomes a mental illness or slides into a state of depression. Researchers have always been curious to find out if a machine could assess human emotions precisely. In this work, a unimodal emotion classifier system in which one of the physiological signals, an electrocardiogram (ECG) signal, has been used is proposed to classify human emotions. The ECG signal was acquired using a capacitive sensor-based non-contact ECG belt system. The machine-learning-based classifiers developed in this work are SVM and random forest with 10-fold cross-validation on three different sets of ECG data acquired for 45 subjects (15 subjects in each age group). The minimum classification accuracies achieved with SVM and RF emotion classifier models are 86.6% and 98.2%, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

2023

View full text Add to dashboard Cite

show abstract

“…In urban areas, the difference between these two measures is rather minimal, while it is substantial for rural regions. This finding could suggest the presence of varying patterns of internet usage within rural areas, which warrants a deeper understanding of the contributing factors and potential implications (Greenfield et al, 2021;Zgheib et al, 2023).…”

Section: Resultsmentioning

confidence: 91%

Comparative Study of Internet Accessing Households in Urban and Rural Areas Before the Pandemic During the Pandemic

Nayahita

2023

IIRJET

View full text Add to dashboard Cite

This research aims to review household internet use, especially before and during the pandemic. In addition, this research tests and provides evidence that internet use in urban and rural areas still experiences differences in users. Data was taken from the Central Statistics Agency in 2019-2021 for all provinces in Indonesia except DKI. The analysis tools in this research are the Kruskal-Wallis and Wilcoxon Signed Ranks tests. The test results show differences in users between urban and rural areas. Apart from that, there are also differences in internet users based on changes in time over three years. The conclusion is that internet use in Indonesia is increasing daily, but there are still differences in users in urban and rural areas.

show abstract

“…In the healthcare sector, Machine Learning (ML) finds application across diverse areas, encompassing disease diagnosis [4][5][6][7], patient management [8][9][10][11], and administrative tasks [12][13][14][15]. In the realm of diagnosis, ML contributes to the analysis of medical images, including X-rays [16,17], MRIs [18][19][20], and CT scans [21,22], facilitating early disease detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive Analysis of Endoscope Demand in Otolaryngology Outpatient Settings

Lanier,

Roush,

Young

et al. 2024

BioMedInformatics

View full text Add to dashboard Cite

Background: There has been a trend to transit reprocessing of flexible endoscopes from a high-level disinfectant (HLD) centralized manner to sterilization performed by nursing staff in some Ear, Nose, and Throat (ENT) clinics. In doing so, the clinic nursing staff are responsible for predicting and managing clinical demand for flexible endoscopes. The HLD disinfection process is time-consuming and requires specialized training and competency to be performed safely. Solely depending on human expertise for predicting the flexible endoscope demands is unreliable and produced a concern of an inadequate supply of devices available for diagnostic purposes. Method: The demand for flexible endoscopes for future patient visits has not been well studied but can be modeled based on patients’ historical information, provider, and other visit-related factors. Such factors are available to the clinic before the visit. Binary classifiers can be used to help inform the sterile processing department of reprocessing needs days or weeks earlier for each patient. Results: Among all our trained models, Logistic Regression reports an average AUC ROC score of 89% and accuracy of 80%. Conclusion: The proposed framework not only significantly reduces the reprocessing efforts in terms of time spent on communication, cleaning, scheduling, and transferring scopes, but also helps to improve patient safety by reducing the exposure risk to potential infections.

show abstract

Towards an ML-based semantic IoT for pandemic management: A survey of enabling technologies for COVID-19

Cited by 16 publications

References 142 publications

Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

Comparative Study of Internet Accessing Households in Urban and Rural Areas Before the Pandemic During the Pandemic

Predictive Analysis of Endoscope Demand in Otolaryngology Outpatient Settings

Contact Info

Product

Resources

About