IRAbMC: Image Recommendation with Absorbing Markov Chain

Image Recommendation is an important feature for search engine as tremendous amount images are available online. It is necessary to retrieve relevant images to meet user's requirement. In this paper, we present an algorithm Image Recommendation with Absorbing Markov Chain (IRAbMC) to retrieve relevant images for user input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Absorbing Markov chain is used to calculate keyword relevance. Experiments results show that the IRAbMC algorithm outperforms Markovian Semantic Indexing (MSI) method with improved relevance score of retrieved ranked images

Query Click and Text Similarity Graph for Query Suggestions

Query suggestion is an important feature of the search engine with the explosive and diverse growth of web contents. Different kind of suggestions like query, image, movies, music and book etc. are used every day. Various types of data sources are used for the suggestions. If we model the data into various kinds of graphs then we can build a general method for any suggestions. In this paper, we have proposed a general method for query suggestion by combining two graphs: (1) query click graph which captures the relationship between queries frequently clicked on common URLs and (2) query text similarity graph which finds the similarity between two queries using Jaccard similarity. The proposed method provides literally as well as semantically relevant queries for users’ need. Simulation results show that the proposed algorithm outperforms heat diffusion method by providing more number of relevant queries. It can be used for recommendation tasks like query, image, and product suggestion

IRHDF: Iris Recognition using Hybrid Domain Features

Iris Biometric is a unique physiological noninvasive trait of human beings that remains stable over a person's life. In this paper, we propose an Iris Recognition using Hybrid Domain Features (IRHDF) as Dual Tree Complex Wavelet Transform (DTCWT) and Over Lapping Local Binary Pattern (OLBP). An eye is preprocessed to extract the complex wavelet features to obtain the Region of Interest (ROI) area from an iris. OLBP is further applied on ROI to generate features of magnitude coefficients. Resultant features are generated by fusion of DTCWT and OLBP using arithmetic addition. Euclidean Distance (ED) is used to match the test iris image with database iris features to recognize a person. We observe that the values of Equal Error Rate (EER) and Total Success Rate (TSR) are better than in [7]

SALR: Secure adaptive load-balancing routing in service oriented wireless sensor networks

Congestion control and secure data transfer are the major factors that enhance the efficiency of Service Oriented Wireless Sensor Networks. It is desirable to modify the routing and security schemes adaptively in order to respond effectively to the rapidly changing Network State. Adding more complexities to the routing and security schemes increases the end-to-end delay which is not acceptable in Service Oriented WSNs which are mostly in real time. We propose an algorithm Secure Adaptive Load-Balancing Routing (SALR) protocol, in which the routing decision is taken at every hop considering the unforeseen changes in the network. Multipath selection based on Node Strength is done at every hop to decide the most secure and least congested route. The system predicts the best route rather than running the congestion detection and security schemes repeatedly. Simulation results show that security and latency performance is better than reported protocols

WCTFR : WRAPPING CURVELET TRANSFORM BASED FACE RECOGNITION

The recognition of a person based on biological features are efficient compared with traditional knowledge based recognition system. In this paper we propose Wrapping Curvelet Transform based Face Recognition (WCTFR). The Wrapping Curvelet Transform (WCT) is applied on face images of database and test images to derive coefficients. The obtained coefficient matrix is rearranged to form WCT features of each image. The test image WCT features are compared with database images using Euclidean Distance (ED) to compute Equal Error Rate (EER) and True Success Rate (TSR). The proposed algorithm with WCT performs better than Curvelet Transform algorithms used in [1], [10] and [11]

IRDO: Iris Recognition by Fusion of DTCWT and OLBP

Iris Biometric is a physiological trait of human beings. In this paper, we propose Iris an Recognition using Fusion of Dual Tree Complex Wavelet Transform (DTCWT) and Over Lapping Local Binary Pattern (OLBP) Features. An eye is preprocessed to extract the iris part and obtain the Region of Interest (ROI) area from an iris. The complex wavelet features are extracted for region from the Iris DTCWT. OLBP is further applied on ROI to generate features of magnitude coefficients. The resultant features are generated by fusing DTCWT and OLBP using arithmetic addition. The Euclidean Distance (ED) is used to compare test iris with database iris features to identify a person. It is observed that the values of Total Success Rate (TSR) and Equal Error Rate (EER) are better in the case of proposed IRDO compared to the state-of-the art technique

Novel synthetic coumarins that targets NF-κB in Hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the fifth most common malignant tumor worldwide, and is the third most common cause of cancer related death. Constitutive activation of NF-κB is the underlying mechanism behind tumorigenesis and this protein regulates the expression of genes involved in proliferation, survival, drug resistance, angiogenesis and metastasis. The design of inhibitors which suppress NF-κB activation is therefore of great therapeutic importance in the treatment of HCC. In this study, we investigated the effect of newly synthesized coumarin derivatives against HCC cells, and identified (7-Carbethoxyamino-2-oxo-2. H-chromen-4-yl)methylpyrrolidine-1 carbodithioate (CPP) as lead compound. Further, we evaluated the effect of CPP on the DNA binding ability of NF-κB, CXCL12-induced cell migration and invasion, and the regulated gene products in HCC cells. We found that CPP induced cytotoxicity in three HCC cells in a time and dose dependent manner, and suppressed the DNA binding ability of NF-κB. CPP significantly decreased the CXCL12-induced cell migration and invasion. More evidently, CPP inhibits the expression of NF-κB targeted genes such as cyclin D1, Bcl-2, survivin, MMP12 and C-Myc. Furthermore, the molecular docking analysis suggested that CPP interacts with the p50 binding domain of the p65 subunit, scoring best among the 26 docked coumarin derivatives of this study. Thus, we are reporting CPP as a potent inhibitor of the pro-inflammatory pathway in Hepatocellular carcinoma. © 2014 Elsevier Ltd

Synthesis, biological evaluation and in silico and in vitro mode-of-action analysis of novel dihydropyrimidones targeting PPAR-gamma

Hepatocellular carcinoma, a fatal liver cancer, affects 600 000 people annually and ranks third in cancer-related lethality. In this work we report the synthesis and related biological activity of novel dihydropyrimidones. Among the tested compounds, 5-acetyl-4-(1H-indol- 3-yl)-6-methyl-3,4-dihydropyrimidin-2(1H)-one (4g) was found to be most active towards the HepG2 cell line (IC50 = 17.9 mu M), being at the same time 7.6-fold selective over normal (LO2) liver cells (IC50 = 136.9 mu M). Subsequently, we identified peroxisome proliferator-activated receptor gamma as a target of compound 4g using an in silico approach, and confirmed this mode-of-action experimentally

OSPCV: Off-line Signature Verification using Principal Component Variances

Signature verification system is always the most sought after biometric verification system. Being a behavioral biometric feature which can always be imitated, the researcher faces a challenge in designing such a system, which has to counter intrapersonal and interpersonal variations. The paper presents a comprehensive way of off-line signature verification based on two features namely, the pixel density and the centre of gravity distance. The data processing consists of two parallel processes namely Signature training and Test signature analysis. Signature training involves extraction of features from the samples of database and Test signature analysis involves extraction of features from test signature and it’s comparison with those of trained values from database. The features are analyzed using Principal Component Analysis (PCA). The proposed work provides a feasible result and a notable improvement over the existing systems