## 5 Challenges and Future Research Directions  
Several challenges are required to be addressed in applying ML/DL techniques to detect COVID-19. Various kinds of diagnosis and detection strategies have been developed to control the spread of COVID-19 based pandemic, but the knowledge related to the applications of various ML/DL techniques to detect the virus is still insufficient. ML/DL approaches need a massive amount of data for computational models to discover and achieve information that is very limited in the case of COVID-19. The ML/DL techniques can only provide better results and valuable solutions to deal with the pandemic if a considerable amount of clinical data and medical images are available. To solve the issue, researchers need to work on appropriate ML/DL techniques that can provide better results using small datasets [78] such as L.S.T.M. [79].  
Moreover, Multi-Criteria Decision Analysis (M.C.D.A.) [80] can be considered a practical solution that enables decision-making to deal with the issues related to COVID-19. As [PERSON] et al. [26] specify, radiology images give optimal results in differentiating coronavirus from other kinds of viral pneumonia, but it still needs to be more efficient to recognize imaging features accurately. In some cases, the presence of noisy, unclear, and inaccurate datasets can lead to misclassification of radiological images (C.T. and X-ray), which can be a significant difficulty in diagnosing COVID. Many complications hinder the training of ML/DL algorithms, such as data redundancy and missing values. Henceforth, researchers utilized different kinds of datasets to improve the training of algorithms to detect the virus. It will enable the radiologists and doctors to diagnose the C.T. and X-ray images more efficiently and promote awareness of viruses. Therefore, a standard framework is needed for future research to assess and analyze these issues. ML can be applied for preparator and feedback activities to tackle worldwide critical situations. For example, ML applications can be used to develop the most efficient robotic and automatic setup for sanitation, distributing food, medicine, and taking care of patients in hospitals. In the computer and biomedical engineering domain, ML has been partially utilized to find out the novel drug compounds against coronavirus [81; 82].  
Moreover, the usage of data science applications like ultrasound and Magnetic Resource Imaging (M.R.I.) for the detection of COVID-19 was limited. The ultrasound scans are as useful as chest C.T. scans, but these are not reported to be utilized to detect COVID-19. Some researchers [83] employed M.R.I to detect COVID-19, but due to the shortage of data, the M.R.I. technique's feasibility and effectiveness are still unexplained. Generating well-defined datasets for the effective use of these methods to detect COVID-19 is another challenge. Moreover, upcoming research must be focused on the classification of COVID-19 by different symptoms of illness (such as fever, cough, congestion, sore throat, runny nose, diarrhea, and breathing difficulty) for precise and fast diagnosis in chronic patients (organ transplant). It is essential to utilize advanced ML/DL techniques to accurately evaluate infected patients and the corresponding death rate in the current scenario. One of the critical issues to be solved is the lack of interest by medical experts in examining medical images and clinical data. Furthermore, the manual classification of COVID-19 is a complicated and time-consuming process that demands the utilization of intelligent supervised ML/DL algorithms [84; 85].