Melanoma is considered a fatal type of skin cancer. However, it is sometimes hard to distinguish it from nevus due to their identical visual appearance and symptoms. The mortality rate because of this disease is higher than all other skin-related consolidated malignancies. The number of cases is growing among young people, but if it is diagnosed at an earlier stage, then the survival rates become very high. The cost and time required for the doctors to diagnose all patients for melanoma are very high. In this paper, we propose an intelligent system to detect and distinguish melanoma from nevus by using the stateof-the-art image processing techniques. At first, the Gaussian filter is used for removing noise from the skin lesion of the acquired images followed by the use of improved K-mean clustering to segment out the lesion. A distinctive hybrid superfeature vector is formed by the extraction of textural and color features from the lesion. Support vector machine (SVM) is utilized for the classification of skin cancer into melanoma and nevus. Our aim is to test the effectiveness of the proposed segmentation technique, extract the most suitable features, and compare the classification results with the other techniques present in the literature. The proposed methodology is tested on the DERMIS dataset having a total number of 397 skin cancer images: 146 are melanoma and 251 are nevus skin lesions. Our proposed methodology archives encouraging results having 96% accuracy. INDEX TERMS Melanoma, nevus, feature, K-means clustering, and centroid selection.

With the evolution of technologies, the size of an image data has been significantly increased. However, traditional image encryption schemes cannot handle the emerging problems in big data such as noise toleration and compression. In order to meet today's challenges, we propose a new image encryption scheme based on chaotic maps and orthogonal matrices. The main core of the proposed scheme is based on the interesting properties of an orthogonal matrix. To obtain a random orthogonal matrix via the Gram Schmidt algorithm, a well-known nonlinear chaotic map is used in the proposed scheme to diffuse pixels values of a plaintext image. In the process of blockwise random permutation, the logistic map is employed followed by the diffusion process. The experimental results and security analyses such as key space, differential and statistical attacks show that the proposed scheme is secure enough and robust against channel noise and JPEG compression. In addition to complete encryption for higher security, it also supports partial encryption for faster processing as well.

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.Vehicular Ad Hoc Networks (VANETs) are autonomous and self-configurable wireless ad hoc networks and considered as a subset of Mobile Ad Hoc Networks (MANETs). MANET is composed of self-organizing mobile nodes which communicate through a wireless link without any network infrastructure. A VANET uses vehicles as mobile nodes for creating a network within a range of 100 to 1000 meters. VANET is developed for improving road safety and for providing the latest services of intelligent transport system (ITS). The development and designing of efficient, self-organizing, and reliable VANET are a challenge because the node's mobility is highly dynamic which results in frequent network disconnections and partitioning. VANET protocols reduce the power consumption, transmission overhead, and network partitioning successfully by using multicast routing schemes. In multicasting, the messages are sent to multiple specified nodes from a single source. The novel aspect of this paper is that it categorizes all VANET multicast routing protocols into geocast and cluster-based routing. Moreover, the performance of all protocols is analyzed by comparing their routing techniques and approaches.