High utility item-sets mining(HUIM)is a special topic in frequent item-sets mining(FIM). It gives better insights for business growth by focusing on the utility of items in a transaction. HUIM is evolving as a powerful research area due to its vast applications in many fields. Data stream processing, meanwhile, is an interesting and challenging problem since, processing very fast generating a huge amount of data with limited resources strongly demands high-performance algorithms. This paper presents an innovative idea to extract the high utility item-sets (HUIs) from the dynamic data stream by applying sliding window control. Even though certain algorithms exist to solve the same problem, they allow redundant processing or reprocessing of data. To overcome this, the proposed algorithm used a trie like structure called Extended Global Utility Item-sets tree (EGUI-tree), which is flexible to store and retrieve the mined information instead of reprocessing. An experimental study on real-world datasets proved that EGUI-tree algorithm is faster than the state-of-the-art algorithms.

Texture classification and analysis are the most significant research topics in computer vision. Local binary pattern (LBP) derives distinctive features of textures. The robustness of LBP against gray-scale and monotonic variations and computational advantage have made it popular in various texture analysis applications. The histogram techniques based on LBP is complex task. Later uniform local binary pattern’s (ULBP) are derived on LBP based on bit wise transitions. The ULBP’s are rotationally invariant. The ULBP approach treated all non-uniform local binary pattern’s (NULBP) into one miscellaneous label. This paper presents a new texture classification method incorporating the properties of ULBP and grey-level co-occurrence matrix (GLCM). This paper derives ternary patterns on the ULBP and divides the 3 x 3 neighborhood in to dual neighborhood. The ternary pattern mitigates the noise problems particularly near uniform regions. The dual neighborhood reduces the range of texture unit from 0 to 6561 to 0 to 80. The GLCM features extracted from ULBP-dual texture matrix (ULBP-DTM) provide complete texture information about the image and reduce the texture unit range. Various machine learning classifiers are used for classification purpose. The performance of the proposed method is tested on Brodtaz, Outex and UIUC’s textures and compared with GLCM, texture spectrum (TS) and cross-diagonal texture matrix (CDTM) approaches.

The local binary pattern (LBP) and local ternary pattern (LTP) are basically gray scale invariant, and they encode the binary/ ternary relationship between the neighboring pixels and central pixel based on their grey level differences and derives a unique code. These traditional local patterns ignore the directional information. The proposed method encodes the relationship between the central pixel and two of its neighboring pixel located in different angles (α, β) with different directions. To estimate the directional patterns, the present paper derived variation in local direction patterns in between the two derivates of first order and derived a unique First order –Local Direction variation pattern (FO-LDVP) code. The FO-LDVP evaluated the possible direction variation pattern for central pixel by measuring the first order derivate relationship among the horizontal and vertical neighbors (0o Vs.90o; 90o Vs. 180o ; 180o Vs.270o ; 270o Vs. 0o) and derived a unique code. The performance of the proposed method is compared with LBP, LTP, LBPv, TS and CDTM using the benchmark texture databases viz. Brodtaz and MIT VisTex. The performance analysis shows the efficiency of the proposed method over the existing methods. 

Researchers have already shown the way, how to improve and compare the existing MANET routing protocols that help us to understand the basic feature and functionality of the various routing protocols. However, while these routing protocols have been proposed from different research groups in the literature, which shows the existing routing protocols, are not consistent to common framework to evaluate its performance. Moreover, these protocols are vulnerable to many collaborative attacks, due to its cooperative nature of routing algorithms. Hence, it is difficult for one to choose a proper routing protocol for a given application; therefore, we initially study and review to compare the different existing routing protocols in adversarial environment with varying traffic and mobility simulation scenarios. This paper addresses the comparison of various reactive routing protocols in adversarial environment. To achieve this, we had investigated with widely used NS-2 simulators for fair comparisons of different routing protocols. Furthermore, we also develop a collaborative adversary model for these existing routing protocols that can interfere with communications to subvert the normal operation of the network. Specifically, Our extensive simulation results shows the relative quantitative analysis for comparing the performance of reactive routing protocols such as AODV, DSR under adversarial environments with varying traffic and mobility simulation scenarios. Moreover, the performance of these protocols is measured with the various metrics such as throughput, end-to-end delay, packet delivery ratio, and routing overhead.

The present day users navigate more using electronic gadgets, interacting with social networking sites and retrieving the images of interest from the information groups or similar groups. Most of the retrievals techniques are not much effective due to the semantic gap. Many models have been discussed for effective retrievals of the images based on feature extraction, label based and semantic rules. However effective retrievals of images are still a challenging task, model based techniques together with semantic attributes provide alternatives for efficient retrievals. This article is developed with the concepts of Generalized Gaussian Mixture Models and Semantic attributes. Flicker dataset is considered to experiment the model and efficiency is measured using Precision and Recall.