Mustafa and Masood [1] obtained group invariant solutions of a nonlinear elastic wave equation using continuous symmetry transformations. In the present\npaper, some new group invariant solutions by using a discrete symmetry group,\nwhich lead to many new exact solutions of the same nonlinear elastic wave\nequation are obtained.

An efficient, computationally secure, verifiable (t, n) multi secret sharing scheme, based on YCH is proposed for multiple 3D models. The (t, n) scheme shares the 3D secrets among n participants, such that shares less than t cannot reveal the secret. The Experimental results provide sufficient protection to 3D models. The feasibility and the security of the proposed system are demonstrated on various 3D models. The Simulation results show that the secrets are retrieved from the shares without any loss.

MRI image which are three hundred and fifty in number obtained from the KMCH hospital database is used to design the proposed diagnosing system. Initially, the X-ray labels and film artifacts are eliminated from the MRI images. The Center Weighted median filter is applied to remove the high frequency components from the image. Then the MRI images are normalized. The suspicious region or tumors is segmented using Markov Random Field (MRF) hybrid with Modified PSO with FCM algorithm for magnetic resonance images. Initially, a unique label is assigned to similar patterns in the magnetic resonance images. A kernel of 3×3 matrix is selected randomly from the enhanced image. The MRF is used to compute the MAP values of each kernel. The metaheuristic algorithm called Modified PSO and Modified PSO with FCM is implemented to obtain the optimum labels by minimizing the MAP values.

MRI and Mammogram is one of the best technologies currently being used for diagnosing breast cancer and brain tumour. Breast cancer and brain tumour is diagnosed at advanced stages with the help of the mammogram and MRI image. In this thesis an intelligent system is designed to diagnose tumour through mammograms, using image processing techniques along with intelligent optimization tools, such as Fire Fly Algorithm (FFA), Enhanced BEE Colony Optimization (EBCO) and Artificial Neural Network. The detection of tumour is performed in two phases: preprocessing and segmentation in the first phase and feature extraction, selection and classification in the second phase.\n\n350 MRI images obtained from KMCH Hospital Coimbatore and 161 pairs of digitized mammograms obtained from the Mammography Image Analysis Society (MIAS) database is used to design the proposed diagnosing system. Initially, the film artifacts and X-ray labels are removed from the images and median filter is applied to remove the high frequency components from the image. The suspicious region is segmented using Markov Random Field (MRF) hybrid with EBCO and FFA algorithm for MRI and mammogram images. \n\nThe MRF and EABCO and FFA algorithm based image segmentation method is a process seeking the optimal labeling of the pixels. The optimum label is that which minimizes the Maximizing a Posterior (MAP) estimate. EBCO and FFA metaheuristic algorithm is implemented to compute the optimum label, which is to be treated as an optimum threshold for segmentation.

The very many optimization techniques like GA, PSO and ABC aid in crystallizing and addressing the static shortest path in the realm of wireless network routing. The motion of MANET is dynamic and hence the shortest path routing problem in MANET manifests into a dynamic optimization problem. The nodes are instilled with an awareness of the environmental conditions by making them operational through intelligence routing becomes a key concern as it has a significant impact towards network performance. The paper attempts to exploit and utilize Artificial Bee Colony to solve MANET because shortest Path problem turns out to be a dynamic optimization problem in MANETs. MANETs are kept and considered target systems because they do represent the next generation wireless network. The results of experiment explicate that Artificial Bee Colony is steadfast to adapt to the gradations in the environment.

In this work, new proposed Artificial Bear Optimization (ABO) algorithm is implemented in the application and for forecasting Back Propagation Algorithm is implemented. Methodical overview of the existing techniques for Business intelligence in optimization techniques is summarized in and in particular, existing papers are discussed elaborately with their limitations. Tracking the optimized customer from the huge set of customer selected using GA, ACO, ABC algorithms. ROC analysis is drafted to evaluate the optimal customer for GA, ACO, ABC and ABO algorithms

Viral diseases are one of the important factor in reduction the area and production of potato. Among the viral diseases Potato leaf roll virus (PLRV) are most destructive caused up to 70% yield losses. Transmissions of PLRV virus occur by an aphid (Myzus persicae) through persistent manner. Environmental conditions play a very important role in the transmission of aphid population and PLRV disease development. Relationship of environmental conditions with vector population and PLRV disease development would help to forecast a disease predictive model in order to take timely disease management decisions. Fifteen varieties/ lines were sown in the research area of Plant Virology section, Plant Pathology Research Institute, Faisalabad during 2009-10 and 2010-11 potato growing seasons. Natural inoculum of insect and virus was relied upon for disease development; however, PLRV was confirmed through ELISA. None of the varieties/lines was resistant or moderately resistant. All the varieties/lines were moderately susceptible to susceptible. Potato leaf roll virus disease incidence, aphid population and environmental data recorded on weekly basis were analyzed through stepwise regression, only three environmental variables i.e., maximum temperature, relative humidity and wind speed exerted significant influence on aphid population and PLRV disease incidence. When these data were split by years; all the environmental variables exerted significant influence on aphid population and PLRV disease incidence during 2009-10. However, during 2010-11 only relative humidity and wind speed were exhibiting significant influence for aphid population and PLRV disease incidence. Stepwise regression gave different models for aphid and PLRV disease prediction. Based on two years combined data a three environmental variable model consisting of maximum air temperature, relative humidity and wind speed explained 31 % variability in aphid population and 60% variability in PLRV disease development.

This paper presents a comparison of single-term Haar wavelet series (STHW) method and the classical fourth order Runge-Kutta (RK) method to solve the second order linear system with singular-A. The results obtained using RK method and the STHW methods are compared with the exact solutions of the second order linear system with singular-A. It is observed that the result obtained using STHW is closer to the true solutions of the problems. Error graphs for the numerical results and exact solutions are presented in a graphical form to highlight the efficiency of this STHW.