<p><strong>Abstract—</strong> Clustering is a major exploratory data mining activity, and a popular statistical data analysis technique used in many fields. Cluster analysis generally speaking isn't just an automated function, but rather reiterated   information exploration procedure or multipurpose dynamic optimisation Comprising trial and error. Parameters for pre-processing and modeling data frequently need to be modified until the output hits the desired properties. -Data points in fuzzy clustering may probably belong to several clusters. Each Data Point is assigned membership grades. Such grades of membership reflect the degree to which data points belong to each cluster. The Fuzzy C-means clustering (FCM) algorithm is among the most widely used fuzzy clustering algorithms.  In this paper We use this method to find typological analysis for dynamic Ad Hoc network nodes movement and demonstrate that we can achieve good performance of fuzziness on a simulated data set of dynamic ad hoc network nodes (DANET) and How to use this principle to formulate node clustering as a partitioning problem. Cluster analysis aims at grouping a collection of nodes into clusters in such a way that nodes seeing a high degree of correlation within the same cluster, whereas nodes members of various clusters are extremely dissimilar in nature.  The FCM algorithm is used for implementation and evaluation the simulated data set using NS2 simulator with optimized AODV protocol. The results from the algorithm 's application show the technique achieved the maximum values of stability for both cluster centers and nodes (98.41 %, 99.99 %) respectively.<strong></strong></p>

With the exponential growth of digital data exchange over the computer network in recent times, protection of information content is becoming a major concern. There are many security risks, which can easily compromise the data transmitted over the network. Cryptography plays an important role in ensuring the security of digital data transmission over these unsafe networks,so in order to ensure secure and fast data transmitted over the network, an enhanced modification for Advanced Encryption Standard (AES) algorithm is proposed and implemented using additional key which generated using linear feedback shift register(LFSR), which provide an efficient technique to pseudo random number generation,also rounds number are decreased . The proposed method give promised result comparing with original AES algorithm result for different data type text, image, and video.

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The Wireless Sensor networks (WSN) consider an emerging technology that have been greatly employed in critical situations like battlefields and commercial applications such as traffic surveillance, building habitat, smart homes and monitoring and many more scenarios. Security is one of the main challenges that face wireless sensor networks nowadays. While an unattended environment makes the deployment of sensor nodes in the networks more vulnerable to vary of potential attacks, the limitations of inherent power and memory for the sensor nodes makes conventional solutions of the security is unfeasible. The sensing technology combined with processing power and wireless communication makes it profitable for being exploited in great quantity in future. The wireless communication technology also acquires various types of security threats. This paper discusses a wide variety of attacks in WSN and their classification mechanisms and different securities available to handle them including the challenges faced.

The aim of this research is to produce a critical survey on detecting and extracting human face with the features over the past 10-15 years. Face Detection is one of the most common techniques in various future visual applications, such as teleconferencing, facial recognition systems, biometrics and human computer interface , not only because of the difficult nature of the face as an object, but also because of the myriad applications that require face detection application as a first step . Finally, we offer a facial detection technology based on skin color segmentation as well as the facial features inside it. The determination of the human face as elliptical area was achieved. Meanwhile, several techniques were used such as enhancement, thresholding, edge detections and binarization techniques to achieve the aim of the suggested method. The facial features are detected and extracted inside the elliptical area; these features can be categorized in three parts: Nose, Mouth and lips localization. The features are detected and extracted in the human face based on image processing techniques.