The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused coronavirus diseases (COVID-19) in late 2019 in China created a devastating economical loss and loss of human lives. To date, 11 variants have been identified with minimum to maximum severity of infection and surges in cases. Bacterial co-infection/secondary infection is identified during viral respiratory infection, which is a vital reason for morbidity and mortality. The occurrence of secondary infections is an additional burden to the healthcare system; therefore, the quick diagnosis of both COVID-19 and secondary infections will reduce work pressure on healthcare workers. Therefore, well-established support from Artificial Intelligence (AI) could reduce the stress in healthcare and even help in creating novel products to defend against the coronavirus. AI is one of the rapidly growing fields with numerous applications for the healthcare sector. The present review aims to access the recent literature on the role of AI and how its subfamily machine learning (ML) and deep learning (DL) are used to curb the pandemic’s effects. We discuss the role of AI in COVID-19 infections, the detection of secondary infections, technology-assisted protection from COVID-19, global laws and regulations on AI, and the impact of the pandemic on public life.

Data mining is a growing domain. Wherever we are going, data mining Techniques are available to collect important information from the warehouse or from databases. In every sector, they need to keep data secure. Maintaining a balanced state of data is very crucial. Whenever a class tends to classify, the specimens in the class can be grouped as the majority group and the minority group. The majority group consists of higher number of data when compared with the data distributed in the minority group. This paper proposes the Randomized-Ensemble with Smote (RESMOTE) methodology to handle the class imbalance problem. This paper addresses imbalance issues and proposes a class imbalance solution using a sampling technique combined with certain classification metrics.

Class imbalance is a critical challenge in heart disease prediction datasets, often leading to biased models with poor performance on the minority class. This paper proposes a novel algorithm, the Adaptive Synthetic Ensemble Balancer (ASEB), designed to address this issue through a combination of advanced synthetic data generation, adaptive class weighting, and ensemble learning techniques. ASEB integrates Adaptive Synthetic Sampling (ADASYN) and Generative Adversarial Networks (GANs) for synthetic sample generation, dynamic class weighting, and ensemble learning strategies. Experimental results demonstrate ASEB's effectiveness in improving predictive accuracy and robustness in imbalanced heart disease datasets.

Data mining is a concept of getting relevant data from massive databases. Nowadays, there is a lack of Prediction of disease is increased predominantly. Hence, we need to remove the inaccurate data that has been spread over in the dataset, which leads to inaccurately predicting disease. Outliers are more prone to prediction mechanisms. This paper includes the detection of outliers in disease prediction mechanisms. The goal of this paper is to bring out exact data instead of getting invalid data by finding outliers and handling them with care. Outliers can be detected using a KNN based peak-LOF based approach with manhattan distance metrics. In this paper, a proper study on outlier detection mechanisms has been done.