Table 11.
Techniques, attributes, and characteristics of hybrid apps-COVID-19 applications
| Authors | The basic objective | Pros | Limitations in study | Security method? | Simulation environments | Dataset and Size of Dataset | Using TL? | Mechanism | Application? |
|---|---|---|---|---|---|---|---|---|---|
| Cantini, Marozzo [119] | Proposing a model for suggesting a meaningful collection of COVID-19 hashtags for a given post |
-High F1-score -High scalability |
-Low robustness | No | Python | Online social networks (Large dataset) | No | MLP | COVID-19 discovery from hashtags and sentences from online social networks |
| Pahar, Klopper [120] | Demonstrating that TL may be utilized to increase the performance and robustness of DNN classifiers for COVID-19 identification |
-Cough with an AUC of 0.982, followed by breath with an AUC of 0.942, and speech with an AUC of 0.923 -Strong robustness |
-Low scalability | No | Python |
Coswara and ComParE dataset (Small dataset) |
Yes | CNN + LSTM + Resnet50 | Cough, breath, and speech detection |
| Hayawi, Shahriar [121] | Offering an ML-based COVID-19 vaccine misinformation detection framework |
-High accuracy -High scalability |
-High delay | No | NLTK library in Python | dataset from Twitter (Large size) | No | XGBoost, LSTM, and BERT transformer model | Detection of COVID-19 vaccination disinformation |
| Lee, Kim [122] | Using the LSTM approach to shorten the time required for RT-PCR in COVID-19 detection | -Moderate accuracy |
-High delay -Low security |
No | Python | The dataset from e 5810 patients (medium size) | No | LSTM | Reduce the time required for RT-PCR in COVID-19 |
| Szaszi, Hajdu [123] | Proposing an ML investigation of the association between demographics and social gathering attendance in 41 nations during the epidemic |
-High predictability -Low delay |
-Poor robustness | No | Not mentioned | The information was acquired from 112,136 people who participated in the survey from 175 countries | No | Random forests | An examination of the association between demography and social gathering participation |
| Hu, Heidari [124] | Using a strategy in conjunction with the KELM classifier to achieve the best results on blood samples |
-Low delay -High accuracy |
-High complexity | No | Python |
UCI dataset (small) |
No | Extreme learning machine | COVID-19 diagnostic assistance in blood specimens |
| Boussen, Cordier [125] | Recognizing intubation patterns with a Gaussian mixture model-clustering technique |
-High clustering ability -High robustness |
-Low scalability | No | Python | Local dataset | No | Gaussian mixture model | COVID-19 patient triage |
| Attallah [126] | Investigating the feasibility of using ECG trace pictures for COVID-19 diagnosis using CNN and TL | -High accuracy |
-Low scalability -High energy consumption |
No | Python | A total of 1937 ECG images from various classifications (Small size) | Yes | CNN | Analyzing ECG data to diagnose COVID-19 |
| Jeevan, Zacharias [127] | Using the CNN model to provide masked face recognition |
-Moderate accuracy -Low convergence time |
-Low security | No | TensorFlow | CASIA-WebFace dataset (Medium size) | Yes | CNN | Masks on face recognition |
| Soltanian and Borna [128] | Providing a simple CNN for differentiating between COVID and Non-COVID |
-High accuracy -Low complexity -High precision |
-Low robustness -Low flexibility |
No | Python sklearn library | Verify dataset (Small) | No | CNN | Recognition of COVID-19 based on cough sounds |