When the usages of electronic mail continue, unsolicited bulk email also continues to grow. These unsolicited bulk emails occupies server storage space and consumes large amount of network bandwidth. To overcome this serious problem, Anti-spam filters become a common component of internet security. Recently, Image spamming is a new kind of method of email spamming in which the text is embedded in image or picture files. Identifying and preventing spam is one of the top challenges in the internet world. Many approaches for identifying image spam have been established in literature. The artificial neural network is an effective classification method for solving feature extraction problems. In this paper we present an experimental system for the classification of image spam by considering statistical image feature histogram and mean value of an block of image. A comparative study of image classification based on color histogram and mean value is presented in this paper. The experimental result shows the performance of the proposed system and it achieves best results with minimum false positive.

No abstract

No abstract

No abstract

Impulse noise (IN) affects the digital image, during transmission, digital storage, and image acquisition. IN removal from an image is necessary as it retains the quality of the image. This work concentrates on the IN. A neuro‐fuzzy (NF) system based on a fuzzy technique which is trained by a learning algorithm derived from neural network theory was implemented for the removal of noise. A NF network for noise filtering in grayscale images that combines two NF filters with a postprocessor to produce the output was presented. However, Sugeno‐type is not intuitive technique and it also less accurate. To overcome these problems, a hybrid NF filter with optimized intelligent water drop (IWD) technique is introduced, where hybridized Sugeno–Mamdani‐based fuzzy interference system is implemented in both the NF filters to obtain more efficient noise removal system. To improve the accuracy of the assignment of membership values to each input pixels, the optimized IWD technique is utilized, as the choice of membership function decides the efficiency of the noise removal in the images. Here, Fuzzy rules have been used to obtain the filtered output. The Hybrid method maintains the accuracy of the Sugeno model and also the interpretable capability of the Mamdani model. This method is robust against the IN and it is flexible, efficient, and accurate than existing filtering method in both noise attenuation and detail preservation and it has a great scope for better real‐time applications.