![]() ![]() Kusyk, J., Şahin, C.Ş., Zou, J., Gundry, S., Uyar, M., Urrea, E.: Game theoretic and bio-inspired optimization approach for autonomous movement of manet nodes. 422 of Studies in Computational Intelligence, pp. In: Advances in Intelligent Modelling and Simulation, Vol. Gundry, S., Zou, J., Urrea, E., Şahin, C.Ş., Kusyk, J., Uyar, M.: Analysis of emergent behavior for ga-based topology control mechanism for self-spreading nodes in manets. In: Proceedings of the International Conference on Military Communications (MILCOM), pp. Urrea, E., Şahin, C.Ş., Uyar, M.U., Conner, M., Bertoli, G., Pizzo, C.: Estimating behavior of a ga-based topology control for self-spreading nodes in manets. Şahin, C.Ş., Uyar, M.Ü., Gundry, S., Urrea, E.: Self organization for area coverage maximization and energy conservation in mobile ad hoc networks. Formal analysis and experimental results with respect to average protection space, total underwater movement, average network connectivity and fault tolerance demonstrate that 3d- pso is an efficient tool to guide uuvs for these three classes of applications in uwsns. The third class involves spherical distribution of uuvs such that they are uniformly distributed and maintain connectivity. In planar distribution class of applications, uuvs form a plane to cover a given dimension in 3d space. In 3d encapsulation class of applications, uuvs uniformly cover the underside of a maritime vessel. Three classes of applications for uwsn configurations are presented and analyzed. Using only a limited information collected from a uuv’s neighborhood, 3d- pso guides uuvs to make movement decisions over unknown 3d spaces. 3d- pso provides a user-defined level of protection density around an asset and fault tolerant connectivity within the uwsn by utilizing Yao-graph inspired metrics in fitness calculations. We present a topology control mechanism based on particle swarm optimization ( pso), called 3d- pso, allowing uuvs to cooperatively protect valued assets in unknown 3d underwater spaces. uuvs can autonomously run intelligent topology control algorithms to adjust their positions such that they can achieve desired underwater wireless sensor network ( uwsn) configurations. The promising results indicate that bio-inspired computation techniques may be useful to construct mathematical models with patient specific growth parameters in clinical systems.Unmanned underwater vehicles ( uuvs) are increasingly used in maritime applications to acquire information in harsh and inaccessible underwater environments. Using leave-one-out-cross-validation method, we showed that, for a set of H&E slides from kidney cancer patient derived xenograft models, PReP-SR generates personalized model parameters with an average error rate of 13.58%. Differential evolution algorithms are developed for tumor growth parameter computations, where a candidate vector in a population consists of size selection indices for spatial evaluation and weight coefficients for spatial features and their correlations. These features are then used as inputs to PReP-SR to compute tumor growth parameters for exponential-linear model. Applying spatial pattern analysis techniques of quadrat counts, kernel estimation and nearest neighbor functions to the images of the H&E samples, slide-specific features are extracted to examine the hypothesis that existence of dependency of nuclei positions possesses information of individual tumor characteristics. In this paper, we analyze digitized images of Hematoxylin-Eosin (H&E) slides equipped with tumorous tissues from patient derived xenograft models to build our bio-inspired computation method, namely Personalized Relevance Parameterization of Spatial Randomness (PReP-SR).
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