No abstract

Mobile Phone manufacturers are continuously working to take move on with rapid pace on their new models and to match with the need of customer, they need to customize their system. However the security scenarios of such practice are not that known, due to this various malware and viruses are increasing day by day and causing harm to the devices. Due to the substantial damage caused by malware in last few years certain significant efforts on developing detection and defense mechanism against malwares. For detecting such malicious applications and malwares a security system should be developed which will target such anomaly or outliers in system. In data mining anomaly detection system plays a major role by monitoring the behavior of an application and categorizing them in to normal and abnormal to detect malwares present in the system.

Machine learning enables computers to help humans in analysing knowledge from large, complex data sets. One of the complex data is genetics and genomic data which needs to analyse various set of functions automatically by the computers. Hope this machine learning methods can provide more useful for making these data for further usage like gene prediction, gene expression, gene ontology, gene finding, gene editing and etc. The purpose of this study is to explore some machine learning applications and algorithms to genetic and genomic data. At the end of this study we conclude the following topics classifications of machine learning problems: supervised, unsupervised and semi supervised, which type of method is suitable for various problems in genomics, applications of machine learning and future views of machine learning in genomics.