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There is huge amount of information accessible within the healthcare systems. But there do not exist enough analysis tools to mine uncovered, unusual but useful patterns in data. Data mining has been used successfully in various fields to discover hidden patterns and trends, alerting about the hidden anomalies in the data or simply helping in the decision making process. This paper how classification techniques in data mining can be applied for heart disease prediction. To predict and alert about any future coronary ailment in the patients techniques like Naïve Bayes, and Decision tree are applied and efficiency of these algorithms is compared. The dataset taken is Cleveland dataset with 14 attributes.

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With the expeditious improvement of the internet and information technology, malicious codes and attacks pose a critical threat to internet and computer security. These attacks and malicious codes are increased exponentially and caused huge amounts of financial damage. The potential to recognize several malicious code variants and threats is crucial for preservation across unauthorized access to computer data, information, data theft, security breaches, etc. Recent methods for identifying malicious codes and threats have indicated less precision and deficient speeds. This paper aims to conduct a brief and systematic survey on the malware detection methods based on the soft computing model. This work is expected to help researchers to understand the malware detection technology and the direction of its research development.