Next-generation sequencing and its data analysis play a major role in solving biological problems and giving rise to new inventions or solutions. NGS performs sequencing of millions and millions of reads parallelly and provides a clear idea about the nucleotide organization of the genome. This rules out the presence of mutations, genetic disorders, cancer, etc. Next-generation sequencing data are high throughput, which becomes a challenge to handle as well as its need to be analyzed. The information about the sequenced genome provided by the sequencer is given to the user (Bioinformatician)in the form of binary format. Handling different file formats in NGS data analysis is a task. There are many file formats that store this sequenced information and make the workflow smooth. Numerous software tools are available that help the user carry out the workflow efficiently. Applications in this field are vast and are used for Human Microbiome studies, Novel pathogen by De Novo Techniques, It can sequence the whole genome rapidly and analyze many reads at a time, helps in the study of rare somatic variants, and mutations, analyze epigenetic factors, sequence target genomes deeply and so on. NGS data analysis not only analyses DNA-Seq it also performs RNA-Seq data analysis, detection of rare variants, and ChIP-Seq data are some of the data sets. Let us unravel the process of NGS data analysis and its applications.

Cardiovascular disease has recently emerged as a significant health risk for people. The term "cardiovascular disease" refers to a broad variety of illnesses that affect the heart and blood arteries. It is been rising daily as a result of inherited and lifestyle factors. Cardiovascular disease is been caused by physical inactivity, unhealthy diet, tobacco use and harmful use of alcohol. One of the largest cardiovascular disease burdens is seen in India. The age-standardized cardiovascular disease death rate of 272 per 100000 population in India is higher than the global average of 235per 100000 population.As the reason due to the patient’s unhealthy symptom’s doctors will ask to go through the basic tests as ECG, X-ray and some other tests and then on the basis of the reports, conclusions about the diseases will be made. At some sort of time patients will be at very weak condition of their health and wait for the treatments to be done. To avoid all such process through the help of machine learning application a lot of time and treatments can also be done as early as possible. Due to a number of risk factors, such as high blood pressure, excessive cholesterol, and an irregular heart rate, it is difficult to diagnose. By using machine learning it collects the required data from the patient and measures whether that patient is having the disease related to cardiovascular or some other diseases. So that the patient can save up the time, test and expenses.

Intelligent face recognition has become a basic technology in work and life. This technology has been immensely used in various products and is well known by people in all sectors. However, the intelligent Face recognition systems developed at present lacks a universal method, and the designed system cannot be applied to all types of products. In order to improve face recognition systems and get better accuracy the main aim is to study and understand face recognition based on different algorithms to find out which is the best suitable method for the same that can be used universally. The study is based on the two widely known algorithms called PCA- Principal Component Analysis, LDA-Linear Discriminant Analysis, comparing the two in terms of face recognition accuracy on a popular dataset called ORL.

Twitter Sentiment Analysis applies sentiment analysis to data in tweets on social media platforms to identify user sentiment. The scope of research in this field has expanded steadily in recent decades. The reason is that the format of tweets is complex and difficult to process. The extremely small Tweet format introduces a whole new set of problems: B. Use of Slang and Acronyms. The COVID-19 coronavirus pandemic continues to impact the health and well-being of people around the world. Our worldview has changed as a result of the pandemic. A vaccination campaign should be carried out among the population to stop the outbreak of the pandemic. However, people are unsure about vaccination, which is cause for concern. This study used machine learning to evaluate public Twitter tweets related to COVID-19 vaccination to determine how users feel about vaccination (ML). Twitter data was analyzed into positive or negative tweets using his three different machine learning (ML) techniques: Support Vector Machine (SVM) and Logistic Regression (LR). The accuracy of the analysis results was 85.3% for the LR algorithm and 87.34% for the SVM.