Abstract: The mobile nodes are generally focused for path selection, but path is not perfectly predicted by sender node, it transmitting the data packets continuously. Genuine time routing is difficult for normal path selection scheme, because unfortunately the nodes behavior is changed, it block data packets, and blocks the time, when transmitting data packets. It obtains the inefficient communication. It increase packet loss rate, and reduces network lifetime. So, proposed Efficient routing for unpredictable path (ERUP) method is used to achieve effective communication in mobile network, the current path status is analyzed and after that assign the routing path. Routing path is judged easily by this methods. This does not block data flow and time. Multirate leveraging situation analyzer algorithm is constructed, it analyze the leveraging condition of routing path nodes in various rates, such that rates are resources utilization of nodes. It increase network lifetime and minimize packet loss rate

Abstract:\n\n\n Generally, a very critical kind of brain cancer has been defined as Glia Blastemal Multiform (GBM) and is a highly less median survival. So, researches have much attention to classify various glioma cancer cell subtypes in order to envisage the patient’s diagnosis. But, due to various individual basis the survival period of various subtypes of GBM is frequently various. In gene testing, classic subtypes rules has been evolved based on single bimolecular features. This type of classification attained better performance compared than traditional rules, but still the massive data has not been covered in GBM diagnosis prediction. So, here, an Improved Extreme Learning Machine (IELM) with sparse representation based classification combined diagnosis model based on more than one data type could accomplish better in GBM and it will used additional to clinical treatment. In this work, the features are selected based on the Linear Discriminant Analysis (LDA). Then, the long and short term survivors are classified by using IELM with sparse representation. In this IELM, the effectual features are selected by using sparse representation to increase the prediction diagnosis accuracy. This system is evaluated by using the Cancer Genome Atlas (TCGA) database. Simulation outcomes demonstrates that the anticipated IELM acquires superior performance with respect to accuracy rate of 92.35%, sensitivity rate of 93.35%, specificity rate of 92.35%, precision rate of 76.77%, recall rate of 60.25% and f-measure rate of 69.25%compared than exiting Minimum Redundancy — Maximum Relevance (MRMR) with Multiple Kernel Machine (MKL) learning method and Support Vector Machine (SVM).

In the modern world, the digital signal processing embeds more in real time applications. Several researchers focused on filtering process to identify the limitation in traditional methods. In this article, the meta-heuristic algorithm is deployed for optimizing the design of infinite impulse response (IIR) filter. The traditional IIR filter results create computational complexity and its performance is worse in the case of a noisy environment. In signal processing, IIR plays several roles in filtering and monitoring the signal amplitude. The African Buffalo Optimization (ABO) is quite easy for implementation and its performance outcomes solved many problems in various domains. Hence, it is selected for solving the IIR filter problems to obtain the optimal filter coefficients. Initially, the IIR filter is designed for different orders under ABO concept. The performance of the ABO based IIR filter is superior to those obtained by Genetic Algorithm and the cuckoo search algorithm. The performance result of the proposed method proves that it has a smaller magnitude error and phase error with fast convergence rate.

Abstract: Nowadays, breast cancer is one of the major issues among women in developed nations. It occurs based on their lifestyles and eating habits. As per many researchers, cancer leads to death. Likewise, breast cancer is also one of the leading dangerous cancer types that may result in critical issues over a body. Hence to avoid such activities it is necessary to detect the cancer spot and provide some clinical experiments, it is termed as early detection. The detection of this type of cancer is difficult at initial stage because the cancerous tumors are rooted in the common breast tissue structures. The main objective of this research is to model a breast cancer prediction system with a novel machine learning approach based on wavelets is proposed. The prediction of breast cancer for diagnosis process is made by the proposed algorithm named as Optimally Pruned Hybrid Wavelet Kernel-based Extreme Learning Machine (HOP-WKELM). Initially, the input is pre-processed for noise reduction using kuan filter. After that, Quantum Evolutionary Algorithm (QEA) is applied to segment the cancer part in mammogram image and feature extraction using Grey-level co-occurrence matrix (GLCM), Gabor filter and Local Binary Pattern (LBP) features. The extracted features are classified using HWKELM classifier. In this HWKELW, WKELM learning algorithm utilized the Dragonfly Swarm Behavior-based Optimization (DSBO) approach to optimize the parameters of kernel functions. The proposed strategies achieved a maximum accuracy of 98.8% and a maximum precision of 98.1% when compared with existing Adaboost systems.

Abstract: Nowadays, utilization of power system with management and monitoring of smart grid system is essential in global wise. Therefore, Phasor Measurement Units (PMUs) device is used for advanced monitoring of real time system, analysis and control in smart power grid system. An Optimal PMU Placement (OPP) is quite important during planning studies for both existing and future power networks. So, the problems of minimizing the number of PMUs and their optimal location for system are mainly considered. In this work, to achieve better optimal location and reduce the number of PMUsand to increase the measurement redundancy, an Enhanced Bat Algorithm (EBA) is proposed for optimization with phasing module. Phasing is mainly considered for OPP to reduce the cost. In phasing unit, PMUs are separated into three phases and those are concentrated in each phase separately. Finally, IEEE-14 bus standard systems and the Tamil Nadu state power grid (83 bus system) are utilized to test the proposed model. The simulation results are evaluated and compared with existing algorithm to show the efficient process of OPP with better optimization, less cost and minimum redundancy than the existing optimization schemes.

ABSTRACT\nIn the applications of Wireless Sensor Network, sensor nodes broadcast the data towards the destination. Localization is considered as the most important and interesting area in WSN as identifying the location of sensor nodes is needful for many situations. The proposed work, points on discovering the sensor node location and promotes cross layer effective routing path searching for the reliable data transmission in the network. The proposed work consists of three main phases. The initial phase includes the cross layer link establishment. The second phase includes, identifying the fuzziness of wireless sensors. It tracks the location of sensor nodes and analyzes the behavior. This can be done with the algorithm namely Fuzziness based Contiguous nodes Refining Algorithm. Since the characteristics of nodes may vary often, the algorithm finds the unsuitable communication node in the routing path and removes it. Source node controls the activities of the intermediate nodes in the routing path. After identifying and removing the unsuitable nodes, in final phase, an effective routing path is established by using the scheme called Efficient Fuzziness based Contiguous node refining with cross-layer routing Scheme (EFCRS) is proposed. This selects the routing nodes from starting node to ending node with the lesser energy usage. At last, destination node acquires the enhanced throughput and reduced consumption of energy. It also obtains minimum packet latency. Simulation is carried out and found it achieves better performance than existing algorithms in comparing metrics end to end delay, overhead, connectivity ratio, throughput and energy consumption.