Heart disease (HD) is a major cause of morbidity and mortality in the modern society. Medical diagnosis is extremely important but complicated task that should be performed accurately and efficiently. This study analyzes the Behavioral Risk Factor Surveillance System, survey to test whether self-reported cardiovascular disease rates are higher in Singareni coal mining regions in Andhra Pradesh state, India, compared to other regions after control for other risks. Dependent variables include self-reported measures of being diagnosed with cardiovascular disease (CVD) or with a specific form of CVD including (1) chest pain (2) stroke and (3) heart attack. Heart care study specifies 15 attributes to predict the morbidity. Beside regular attributes other general attributes BMI (Body Mass Index), physician supply, age, ethnicity, education, income, and others are used for prediction. An automated system for medical diagnosis would enhance medical care and reduce costs. In this paper popular data mining techniques namely, Decision Trees, Naïve Bayes and Neural Network are used for prediction of heart disease.

The drastic development of the World Wide Web in the recent times has made the concept of Web crawling receive remarkable significance. The voluminous amounts of Web documents swarming the Web have posed huge challenges to the Web search engines making their results less relevant to the users. The presence of duplicate and near duplicate Web documents in abundance has created additional overheads for the search engines critically affecting their performance and quality. The detection of duplicate and near duplicate Web pages has long been recognized in Web crawling research community. It is an important requirement for search engines to provide users with the relevant results for their queries in the first page without duplicate and redundant results. In this paper, we have presented a novel and efficient approach for the detection of near duplicate Web pages in Web crawling. Detection of near duplicate Web pages is carried out ahead of storing the crawled Web pages in to repositories. At first, the keywords are extracted from the crawled pages and the similarity score between two pages is calculated based on the extracted keywords. The documents having similarity scores greater than a threshold value are considered as near duplicates. The detection has resulted in reduced memory for repositories and improved search engine quality.

Application layer Distributed Denial of Service (DDoS) attacks are among the deadliest kinds of attacks that have significant impact on destination servers and networks due to their ability to be launched with minimal computational resources to cause an effect of high magnitude. Commercial and government Web servers have become the primary target of these kinds of attacks, with the recent mitigation efforts struggling to deaden the problem efficiently. Most application layer DDoS attacks can successfully mimic legitimate traffic without being detected by Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS). IDSs and IPSs can also mistake a normal and legitimate activity for a malicious one, producing a False Positive (FP) that affects Web users if it is ignored or dropped. False positives in a large and complex network topology can potentially be dangerous as they may cause IDS/IPS to block the user's benign traffic. Our focus and contributions in this paper are first, to mitigate the undetected malicious traffic mimicking legitimate traffic and developing a special anti-DDoS module for general and specific DDoS tools attacks by using a trained classifier in a random tree machine-learning algorithm. We use labeled datasets to generate rules to incorporate and fine-tune existing IDS/IPS such as Snort. Secondly, we further assist IDS/IPS by processing traffic that is classified as malicious by the IDS/IPS in order to identify FPs and route them to their intended destinations. To achieve this, our approach uses active authentication of traffic source of both legitimate and malicious traffic at the Bait and Decoy server respectively before destined to the Web server.