This paper presents an overview of the technologies and the methodologies used in Intrusion Detection Systems (IDS). Intrusion Detection System (IDS) technologies are differentiated by types of events that IDSs can recognize, by types of devices that IDSs monitor and by activity. Intrusion Detection is the process of monitoring the information systems by sensors or agents and analyzing the collected information to detect and to attempt to stop the attacks in real time, identifying vulnerabilities, the violation of security policies or standard security practices.

Cognitive radio (CR) is emerging as a promising technology to improve the utilization of wireless spectrum resources. It is a novel technology that can potentially improve the utilization efficiency of the radio spectrum. The detection performance of spectrum sensing schemes is usually compromised by destructive channel conditions between the target-under-detection and the cognitive radios, since it is hard to distinguish between a white spectrum and a weak signal attenuated by deep fading. In this paper we presents the survey for the cognitive radio to improve the spectrum sensing and energy efficiency in radio networks.

In present scenario, content-based image classification has become research focus of multimedia Information retrieval. Usually, low-level visual features of images, such as color, texture, and shape, are used to describe images and to measure the content similarity between two images. In this paper we presents the survey for the image retrieval and classification for various number of application using the machine learning, data mining and other techniques.

Now a day’s healthcare organization faces major challenges in the provision of cost, quality of services, patient’s life decision, and detection of any diseases at early stages. These days many hospital generate huge amount of patient’s data for many diseases, which play a key role for the treatment of diseases and get recovered from its. In this paper we present the survey for the health diseases diagnosis using evolutionary and other classification algorithm.

In this paper we proposed a new model which is based on the classification methods such as support vector machine, neural network and optimization methods for the improving the classifier results in the terms of some performance parameters such as accuracy, precision, recall etc., here we measure the all performance parameters for the various dataset such as heart patients, liver patients and cancer patients and improve the rate of classification or results with compare than other existing techniques. In this paper our experimental results shows that the better detection rate of classification for performance parameters than other existing techniques.