Novel Scheme for Minimal Iterative PSO Algorithm for Extending Network Lifetime of Wireless Sensor Network

Clustering is one of the operations in the wireless sensor network that offers both streamlined data routing services as well as energy efficiency. In this viewpoint, Particle Swarm Optimization (PSO) has already proved its effectiveness in enhancing clustering operation, energy efficiency, etc. However, PSO also suffers from a higher degree of iteration and computational complexity when it comes to solving complex problems, e.g., allocating transmittance energy to the cluster head in a dynamic network. Therefore, we present a novel, simple, and yet a cost-effective method that performs enhancement of the conventional PSO approach for minimizing the iterative steps and maximizing the probability of selecting a better clustered. A significant research contribution of the proposed system is its assurance towards minimizing the transmittance energy as well as receiving energy of a cluster head. The study outcome proved proposed a system to be better than conventional system in the form of energy efficiency

Novel Bacteria Foraging Optimization for Energy-efficient Communication in Wireless Sensor Network

Optimization techniques based on Swarm-intelligence has been reported to have significant benefits towards addressing communication issues in Wireless Sensor Network (WSN). We reviewed the most dominant swarm intelligence technique called as Bacteria Foraging Optimization (BFO) to find that there are very less significant model towards addressing the problems in WSN. Therefore, the proposed paper introduced a novel BFO algorithm which maintains a very good balance between the computational and communication demands of a sensor node unlike the conventional BFO algorithms. The significant contribution of the proposed study is to minimize the iterative steps and inclusion of minimization of both receiving / transmittance power in entire data aggregation process. The study outcome when compared with standard energy-efficient algorithm was found to offer superior network lifetime in terms of higher residual energy as well as data transmission performance

A Modified Neutral Point Method for Kernel-Based Fusion of Pattern-Recognition Modalities with Incomplete Data Sets

It is commonly the case in multi-modal pattern recognition that certain modality-specific object features are missing in the training set. We address here the missing data problem for kernel-based Support Vector Machines, in which each modality is represented by the respective kernel matrix over the set of training objects, such that the omission of a modality for some object manifests itself as a blank in the modality-specific kernel matrix at the relevant position. We propose to fill the blank positions in the collection of training kernel matrices via a variant of the Neutral Point Substitution (NPS) method, where the term ”neutral point” stands for the locus of points defined by the ”neutral hyperplane” in the hypothetical linear space produced by the respective kernel. The current method crucially differs from the previously developed neutral point approach in that it is capable of treating missing data in the training set on the same basis as missing data in the test set. It is therefore of potentially much wider applicability. We evaluate the method on the Biosecure DS2 data set

Salinity changes in the estuary and the coastal sea adjacent to the portmouth at Cochin

The article deals with the details of salinity changes in the Cochin estuary and its influence and interrelations with the Vembanad lake

Consultancy services in marine fisheries- A profile of technologies and experts

The ICAR system currently needs very effective partnership between the researchers and the user groups. The Central Marine Fisheries Research Institute, a premier Institute under the ICAR, has taken effective steps to introduce the services and technologies in the marine fisheries sector, achieved over the last 50 years R & D activities. With a viev/ to institutionalising transfer of technologies, the institute has constituted a Consultancy Processing Cell (CPC) in 1997 for effectively serving the needs of our clients, through the short term and long term trainings, consultancies, contract services and contract research

A STUDY ON EDUCATIONAL DATA MINING THROUGH QUESTIONNAIRE SURVEY

Educational Data Mining (EDM) is a recent area yet with many fields to be researched. Applying Data Mining techniques to education data help us dealing with issues that would be hard without them. With its techniques and methods we try to discover behaviours and strategies, both for students and teachers. This information will take us towards the discovery of which strategies must be avoided, which teaching strategies can be adapted to each kind of student or to anticipate which students will fail so they can be helped since an early stage. In This Paper we have conducted a Questionnaire Survey on 500 Software Engineers to understand the present scenario of EDM

Altered expression of cytokines in mice infected intranasally with two syncytial variants of Herpes simplex virus type 1

Immune evasion strategies are important for the onset and the maintenance of viral infections. Many viruses have evolved mechanisms to counteract or suppress the host immune response. We have previously characterized two syncytial (syn) variants of Herpes simplex 1 (HSV-1) strain F, syn14-1 and syn17-2, obtained by selective pressure with a natural carrageenan. These variants showed a differential pathology in vaginal and respiratory mucosa infection in comparison with parental strain. In this paper, we evaluated the modulation of immune response in respiratory mucosa by these HSV-1 variants. We observed altered levels of Tumor Necrosis Factor-α and Interleukin-6 in lungs of animals infected with the syn14-1 and syn17-2 variants compared with the parental strain. Also, we detected differences in the recruitment of immune cells to the lung in syn variants infected mice. Both variants exhibit one point mutation in the sequence of the gene of glycoprotein D detected in the ectodomain of syn14-1 and the cytoplasmic tail of syn17-2. Results obtained in the present study contribute to the characterization of HSV-1 syn variants and the participation of the cellular inflammatory response in viral pathogenesis.Fil: Artuso, María Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Linero, Florencia Natalia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Biológica. Laboratorio de Virología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gazzaniga, Silvina Noemí. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Scolaro, Luis Alberto. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Biológica. Laboratorio de Virología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pujol, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Wainstok, Rosa. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Carlucci, Maria Josefina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentin

Score Fusion by Maximizing the Area under the ROC Curve

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-02172-5_61Information fusion is currently a very active research topic aimed at improving the performance of biometric systems. This paper proposes a novel method for optimizing the parameters of a score fusion model based on maximizing an index related to the Area Under the ROC Curve. This approach has the convenience that the fusion parameters are learned without having to specify the client and impostor priors or the costs for the different errors. Empirical results on several datasets show the effectiveness of the proposed approach.Work supported by the Spanish projects DPI2006-15542-C04 and TIN2008-04571 and the Generalitat Valenciana - Consellería d’Educació under an FPI scholarship.Villegas Santamaría, M.; Paredes Palacios, R. (2009). Score Fusion by Maximizing the Area under the ROC Curve. En Pattern Recognition and Image Analysis: 4th Iberian Conference, IbPRIA 2009 Póvoa de Varzim, Portugal, June 10-12, 2009 Proceedings. Springer Verlag (Germany). 473-480. https://doi.org/10.1007/978-3-642-02172-5_61S473480Toh, K.A., Kim, J., Lee, S.: Biometric scores fusion based on total error rate minimization. Pattern Recognition 41(3), 1066–1082 (2008)Jain, A., Nandakumar, K., Ross, A.: Score normalization in multimodal biometric systems. Pattern Recognition 38(12), 2270–2285 (2005)Gutschoven, B., Verlinde, P.: Multi-modal identity verification using support vector machines (svm). In: Proceedings of the Third International Conference on Information Fusion. FUSION 2000, vol. 2, pp. THB3/3–THB3/8 (July 2000)Ma, Y., Cukic, B., Singh, H.: A classification approach to multi-biometric score fusion. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 484–493. Springer, Heidelberg (2005)Maurer, D.E., Baker, J.P.: Fusing multimodal biometrics with quality estimates via a bayesian belief network. Pattern Recogn. 41(3), 821–832 (2008)Ling, C.X., Huang, J., Zhang, H.: Auc: a statistically consistent and more discriminating measure than accuracy. In: Proc. of IJCAI 2003, pp. 519–524 (2003)Yan, L., Dodier, R.H., Mozer, M., Wolniewicz, R.H.: Optimizing classifier performance via an approximation to the wilcoxon-mann-whitney statistic. In: Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), Washington, DC, USA, pp. 848–855. AAAI Press, Menlo Park (2003)Marrocco, C., Molinara, M., Tortorella, F.: Exploiting auc for optimal linear combinations of dichotomizers. Pattern Recogn. Lett. 27(8), 900–907 (2006)Marrocco, C., Duin, R.P.W., Tortorella, F.: Maximizing the area under the roc curve by pairwise feature combination. Pattern Recogn. 41(6), 1961–1974 (2008)Paredes, R., Vidal, E.: Learning prototypes and distances: a prototype reduction technique based on nearest neighbor error minimization. Pattern Recognition 39(2), 180–188 (2006)Villegas, M., Paredes, R.: Simultaneous learning of a discriminative projection and prototypes for nearest-neighbor classification. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2008, pp. 1–8 (2008)Nandakumar, K., Chen, Y., Dass, S.C., Jain, A.: Likelihood ratio-based biometric score fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(2), 342–347 (2008)Poh, N., Bengio, S.: A score-level fusion benchmark database for biometric authentication. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 1059–1070. Springer, Heidelberg (2005)National Institute of Standards and Technology: NIST Biometric Scores Set - Release 1 (BSSR1) (2004), http://www.itl.nist.gov/iad/894.03/biometricscores/Bengio, S., Mariéthoz, J., Keller, M.: The expected performance curve. In: Proceedings of the Second Workshop on ROC Analysis in ML, pp. 9–16 (2005