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The present digital world’s challenging issue is COVID-19. This paper is related to the process of the COVID-19 treatment based on age, gender, symptoms and previous health issues. This paper gives the deep discussion about the prevention, symptoms, tests and treatment process. In this research work, the discussion is about vaccine invention and the side effects of the consumed medication.

This paper gives a clear explanation of the types of vaccine, which are lopinavir, ritonavir, remdesivir, hydroxychloroquine, chloroquine and plasma therapy. Thereafter, the discussion is prolonged to Indian vaccine for COVID-19.

This paper examines some of the COVID-19 treatment processes and difficulties, and finally, this paper aims to summarize and give an overview of the present preclinical research and clinical trials of potential candidates for COVID-19 treatments and vaccines.

The required information has been taken from online databases such as PubMed, Science, Nature, PNAS and Cell. Papers included were published between December 2019 and July 2020. The current results indicate the most promising outcomes for dexamethasone as a treatment and vaccine. Further research is needed to identify safe and effective treatments and vaccines for COVID-19.

A proper understanding of malware characteristics is necessary to protect massive data generated because of the advances in Internet of Things (IoT), big data and the cloud. Because of the encryption techniques used by the attackers, network security experts struggle to develop an efficient malware detection technique. Though few machine learning-based techniques are used by researchers for malware detection, large amounts of data must be processed and detection accuracy needs to be improved for efficient malware detection. Deep learning-based methods have gained significant momentum in recent years for the accurate detection of malware. The purpose of this paper is to create an efficient malware detection system for the IoT using Siamese deep neural networks.

In this work, a novel Siamese deep neural network system with an embedding vector is proposed. Siamese systems have generated significant interest because of their capacity to pick up a significant portion of the input. The proposed method is efficient in malware detection in the IoT because it learns from a few records to improve forecasts. The goal is to determine the evolution of malware similarity in emerging domains of technology.

The cloud platform is used to perform experiments on the Malimg data set. ResNet50 was pretrained as a component of the subsystem that established embedding. Each system reviews a set of input documents to determine whether they belong to the same family. The results of the experiments show that the proposed method outperforms existing techniques in terms of accuracy and efficiency.

The proposed work generates an embedding for each input. Each system examined a collection of data files to determine whether they belonged to the same family. Cosine proximity is also used to estimate the vector similarity in a high-dimensional area.

The purpose of this paper is to determine the role of service quality (SQ), trust and commitment to customer loyalty (CL) for telecom service users. Further, the moderating role of gender, marital status and connection type within the model was tested.

A measurement model was created based on valid 615 responses from Indian TSUs for SQ, trust, commitment and loyalty with the help of partial least squares structural equation modeling (PLS-SEM). Multi-group analysis (MGA) was conducted to understand the moderating effect of marital status, gender and connection type within the model.

The results suggest that, out of five dimensions of SQ, only responsiveness, assurance and empathy have a significant positive relationship with both commitment and trust. Tangibility has a significant positive relationship with trust only. Both commitment and trust have a significant impact on loyalty. It was noticed that both commitment and trust act as mediators between three SQ dimensions (assurance, empathy and responsiveness) and CL. MGA revealed that empathy and responsiveness positively induce trust in telecom users who are single. Whereas, assurance increases commitment toward telecom service providers in married users. Assurance and empathy significantly contribute toward commitment and trust, respectively, in male users as compared to females. Empathy was found important for postpaid users for trust-building, whereas trust was found to be more important for prepaid users to stay loyal to the service provider.

This article contributes toward understanding the role of SQ, trust and commitment to CL moderated by marital status, gender and connection type in an integrated model concerning telecom service.