As per the information provided by WHO, most of the people die from the cardiovascular disease. In 2019, almost 32% of the global deaths were due to cardiovascular diseases, out of which 85% were because of heart attacks and strokes. Hence, it is very important to predict the chances and risk of cardiovascular disease event, to prevent any damage in future. Cardiovascular diseases are the disorders of blood vessels supplying blood to heart, brain and different parts of the body. There are different causes of cardiovascular diseases, which can be quantified with the help of different features and with supporting attributes like age of the person, any diseases like diabetes, blood pressure etc., the risk of the cardiovascular disease can be assessed to prevent the further losses. Machine learning approach is very useful in these circumstances, where quantified data values are available in terms of data set. Machine learning techniques can be used to find the risk of cardiovascular disease. Here, we are proposing to use the two machine learning classifiers such as kNN and decision tree. kNN helps us to find the possibility of cardiovascular disease and decision tree helps us to classify the type of the cardiovascular disease with the risk involved. This approach is very useful, as decision tree is one of the most accurate classifiers, which also helps us to identify the specific cardiovascular disease that can be the future event based on feature values. This proposed methodology is justified with proper research gap stating the important of the proposed architecture and implementation results, which gives effective way for assessing the risk of cardiovascular disease.

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There is an explosive growth of information on Internet that makes extraction of relevant data from various sources, a difficult task for its users. Therefore, to transform the Web pages into databases, Information Extraction (IE) systems are needed. Relevant information in Web documents can be extracted using information extraction and presented in a structured format. By applying information extraction techniques, information can be extracted from structured, semi-structured, and unstructured data. This paper presents some of the major information extraction tools. Here, advantages and limitations of the tools are discussed from a user's perspective.

The interactions with web systems is huge because of COVID-19, where every system is run through online interactions. The world wide web is continuously expanding, and users' interactions with websites generate a vast quantity of data. Web usage mining is the use of data mining techniques to extract important and hidden information about users. It allows you to see the most frequently visited sites, imagine user navigation, and track the progress of your website's structure, among other things. The web mining techniques help us to analyze the user's behavior and accordingly create the required web designs, which will appear in the relevant searches of the users. In this scenario, one of the important processes is web document preprocessing, which will help us to extract the particular quality data inputs for analyzing the behaviors which helps in effective web design. Here, the authors discuss preprocessing of web documents. From the four different phases of the web mining, web document pre-processing is a very important phase.