Currently, in most virus detection techniques the signature recognition process of unknown viruses are challenging task. The process has become more difficulties after the complexities of the viruses' structure have developed, as in the Polymorphic computer viruses. This paper reviews and then explains some used techniques to detect the known polymorphic viruses. Besides, the paper contains the review of some biological models for virus detection. Finally, a new biological model for detecting polymorphic viruses is suggested

Abstract-recently, the diseases of diabetes mellitus have grown into extremely feared problems that can have damaging effects on the health condition of their sufferers globally. In this regard, several machine learning models have been used to predict and classify diabetes types. Nevertheless, most of these models attempted to solve two problems; categorizing patients in terms of diabetic types and forecasting blood surge rate of patients. This paper presents an automatic decision support system for diabetes mellitus through machine learning techniques by taking into account the above problems, plus, reflecting the skills of medical specialists who believe that there is a great relationship between patient's symptoms with some chronic diseases and the blood sugar rate. Data sets are collected from Layla Qasim Clinical Center in Kurdistan Region, then, the data is cleaned and proposed using feature selection techniques such as Sequential Forward Selection and the Correlation Coefficient, finally, the refined data is fed into machine learning models for prediction, classification, and description purposes. This system enables physicians and doctors to provide diabetes mellitus (DM) patients good health treatments and recommendations.