S# Reference Description Techniques
1 Otoom, M., Otoum, N., An IoT-based framework for early (1) Support Vector Machine, (2)
Alzubaidi, M. A., Etoom, identification and monitoring of Neural Network, (3) Naïve Bayes,
Y., & Banihani, R. COVID-19 cases (4) K-Nearest Neighbor (K- NN),
(2020). An IoT-based (5) Decision Table, (6) Decision
framework for early Stump, (7) OneR, and (8) ZeroR.
identification and
monitoring of COVID-19
cases. Biomedical
Signal Processing and
Control, 62, 102149.
2 Ahmed, I., Ahmad, A., & Propose an IoT-Based Deep Regional-based convolutional
Jeon, G. (2020). An IoT- Learning Framework for Early neural networks (RCNNs), i.e.,
based deep learning Assessment of Covid-19 Faster-RCNN and ResNet-101,
framework for early are utilized for Covid-19
assessment of COVID- detection.
19. IEEE Internet of
Things Journal, 8(21),
15855-15862.
3 Nalavade, Jagannath E. Design automated digital health monitoring system
"Digital Screening Tool screening tool that detect the (combination of IoT features and
to detect Covid-19 infected people in ML
infected People." 2021 early stage. algorithms)
International
Conference on
Computer
Communication and
Informatics (ICCCI).
IEEE, 2021.
4 Rahman, M. A., & An Internet-of-Medical-Things- deep learning technique, CNN
Hossain, M. S. (2021). Enabled Edge Computing convolutional neural network
An Internet-of-Medical- Framework for Tackling COVID- architecture.
Things-Enabled Edge 19 is proposed,
Computing Framework
for Tackling COVID-
19. IEEE Internet of
Things Journal, 8(21),
15847-15854.
5
IoT device Features of Data Case Study
wearable sensors, temperature-based Fever, Cough, Fatigue, Sore COVID-19
sensors, audio-based sensors, Motion- Throat, and Shortness of Breath. detection and
based and heart-rate sensors, image- monitoring
based classification, oxygen-based
sensors
anchor selection, sensitivity, Covid-19 detection
Specificity
Wireless Body Area Network day to day symptoms,first day of detection of covid
technology,Blue tooth and ZigBee,medical covid-19, second day covid-19, 19 to check
sensors are available which works on upto fourteenth day of covid- whether person is
Bluetooth,"low-power sensing devices, 19 ,Like first day he had aches Healthy or
management electronics and wireless and pains, nasal congestion, infected
transceivers",accelerometers which runny nose, sore throat or
can sense blood pressure, heartbeat rate, diarrhea etc., related any
movement or symptoms.
even muscular activity.
tiredness and drowsiness detection via cough, emotions, detection of covid
camera sensor, ECG sensors, sound fever,Drowsiness, heartbeat, 19
sensor, wearable sensors, thermal camera face mask.
Covid Variants Dataset Efficiency Accuracy
COVID-19 COVID-19 Open Support Vector
Research Dataset Machine (SVM)
(CORD-19) 92.95 %, Neural
repository Network 92.89 %, K-
Nearest Neighbor (K-
NN)
92.89 % , Decision
Table 92.95 %, Decision
Stump 70.73 %, OneR
68.36 %, ZeroR 57.86 %
Covid-19 X-Ray images detection accuracy of
dataset, 4000 98%
samples of Covid-19
(positive) and 7000
samples of Non-
Covid-19 (negative)
are obtained,
COVID-19 created by studying detection accuracy of
covid-19 patients per 98%
day history(dataset
of 5000 patients.)
COVID-19 Google’s audio data precision and recall
set, MIT-BIH results of every dataset
Arrhythmia ECG data are shown
set, 8541 images
consisting of 4250
images with a face
mask, and the rest
without a face mask
