Real time flood disaster monitoring based on energy efficient ensemble clustering mechanism in wireless sensor network

In this article, energy efficient ensemble clustering method (EECM) with black widow optimization (EECM-BWO) algorithm is proposed for effective data transmission with the help of real time flood disaster monitoring wireless sensor network (WSN). Initially, unified scalable ensemble clustering algorithm based on ensemble generation and consensus function is proposed for selecting the optimal routing path among the node using BWO algorithm. Then, biologically inspired routing black widow spiders optimization algorithm is proposed to trade off the nodes energy level, self-organization, and self-configuration in the WSN. The simulation is performed using NS2 simulator for validating the performance of the proposed EECM-BWO method. Here, in node, low delay achieves 24.07%, 72.58%, 51.36%, 81.75%, 77.74%, high packet delivery ratio achieves 70.83%, 53.93%, 90.23%, 43.58%, 24.58%, low packet drop attains 77.93%, 72.76%, 61.56%, 51.87%, 34.35%, low energy consumption attains 75.9%, 52.94%, 65.81%, 58%, 41.2% compared with existing energy-efficient clustering approach consolidated game theory as well as dual-cluster-head mode for WSNs energy-aware clustering by cuckoo optimization approach (EECM-COA), energy-aware clustering-based routing using multi-path reliable transmission with routing and control board (EECM-RCB-MRT), adaptive repair algorithm with temporally ordered routing algorithms for flood control strategy (EECM-AR-TORA-FCS), passive multi-hop clustering algorithm (EECM-PMC), dynamic source routing protocol based on genetic algorithm-bacterial foraging optimization (DSR-GA-BFO).     

Neural-network method applied to the stereo image correspondence problem in three-component particle image velocimetry

The successful application of a recurrent neural network of the Hopfield type to the solution of the stereo image-pair reconciliation problem in stereoscopic particle image velocimetry (PIV) in the tracking mode is described. The results of applying the network to both virtual-flow and physical-flow PIV data sets are presented, and the usefulness of this novel approach to PIV stereo image analysis is demonstrated. A partner-particle image-pair density (PPID) parameter is defined as the average number of potential particle image-pair candidates in the search window in the second view corresponding to a single image pair in the first view. A quantitative assessment of the performance of the method is then made from groups of 100 synthetic flow images at various values of the PPID. The successful pairing of complementary image points is shown to vary from 100% at a PPID of 1 and to remain greater than 97% successful for PPID's up to 5. The application of the method to a hydraulic flow is also described, with in-line stereo images presented, and the application of the neural-matching method is demonstrated for a typical data set.     

Randomized priority algorithms

Borodin, Nielsen and Rackoff [13] introduced the class of priority algorithms as a framework for modeling deterministic greedy-like algorithms. In this paper we address the effect of randomization in greedy-like algorithms. More specifically, we consider approximation ratios within the context of randomized priority algorithms. As case studies, we prove inapproximation results for two well-studied optimization problems, namely facility location and makespan scheduling.     

Disruption management in the airline industry—Concepts,models and methods

This paper provides a thorough review of the current state-of-the-art within airline disruption management of resources, including aircraft, crew, passenger and integrated recovery. An overview of model formulations of the aircraft and crew scheduling problems is presented in order to emphasize similarities between solution approaches applied to the planning and recovery problems. A brief overview of research within schedule robustness in airline scheduling is included in the review, since this proactive measure is a natural complement to disruption management.     

Elicitator: An expert elicitation tool for regression in ecology

Expert elicitation is the process of retrieving and quantifying expert knowledge in a particular domain. Such information is of particular value when the empirical data is expensive, limited or unreliable. This paper describes a new software tool, called Elicitator, which assists in quantifying expert knowledge in a form suitable for use as a prior model in Bayesian regression. Potential environmental domains for applying this elicitation tool include habitat modelling, assessing detectability or eradication, ecological condition assessments, risk analysis and quantifying inputs to complex models of ecological processes. The tool has been developed to be user-friendly, extensible and facilitate consistent and repeatable elicitation of expert knowledge across these various domains. We demonstrate its application to elicitation for logistic regression in a geographically based ecological context. The underlying statistical methodology is also novel, utilizing an indirect elicitation approach to target expert knowledge on a case-by-case basis. For several elicitation sites (or cases), experts are asked simply to quantify their estimated ecological response (e.g. probability of presence), and its range of plausible values, after inspecting (habitat) covariates via GIS.     

An application of the technology acceptance model to the level of Internet usage by older adults

In this cross-sectional study, the principles of a technology acceptance model were used to identify variables related to the level of Internet usage by older adults. Community-dwelling older adults aged 60–88 years completed a postal questionnaire survey that elicited responses on the use of the Internet. Out of a sample of 592 older adults (236 males and 356 females), 50.7% used the Internet. A multiple linear regression analysis was carried out on the Internet users sample using the self-reported number of hours of Internet usage per week as the dependent variable. The results indicated that attitude toward using the Internet and good health status were statistically significant predictors of the level of Internet usage. A second multiple regression analysis using Internet activity as the dependent variable showed that attitude, usefulness, good health, and gender (males) were significant predictor variables.     

Benchmarking Image Segmentation Algorithms

We present a thorough quantitative evaluation of four image segmentation algorithms on images from the Berkeley Segmentation Database. The algorithms are evaluated using an efficient algorithm for computing precision and recall with regard to human ground-truth boundaries. We test each segmentation method over a representative set of input parameters, and present tuning curves that fully characterize algorithm performance over the complete image database. We complement the evaluation on the BSD with segmentation results on synthetic images. The results reported here provide a useful benchmark for current and future research efforts in image segmentation.     

On the mapping of genotype to phenotype in evolutionary algorithms

Analogies with molecular biology are frequently used to guide the development of artificial evolutionary search. A number of assumptions are made in using such reasoning, chief among these is that evolution in natural systems is an optimal, or at least best available, search mechanism, and that a decoupling of search space from behaviour encourages effective search. In this paper, we explore these assumptions as they relate to evolutionary algorithms, and discuss philosophical foundations from which an effective evolutionary search can be constructed. This framework is used to examine grammatical evolution (GE), a popular search method that draws heavily upon concepts from molecular biology. We identify several properties in GE that are in direct conflict with those that promote effective evolutionary search. The paper concludes with some recommendations for designing representations for effective evolutionary search.