Algorithm

into 2-D search, the well-known subspace MUSIC algorithm is used for the examination of the received spatial information, and then it estimates each spatial spectrum in which the Azimuth Angle of Arrival (AOA) and Elevation Angle of Arrival (EOA) of all the paths at each URA WiFi access point is located. After that, because our system is considered under very low SNR, a set of spectra at some APs might be influenced, so, a fine-grained fusion algorithm has been added, it computes the minimum errors

intent of machine learning is that machine learns to observe data, extract important information from it and grasp on its own to predict, recommend or alter any action without any human mediation. This requires various algorithms over varied systems. For the ease of these algorithms, Apache has come up with frameworks Mahout and Spark, which with its different ways helps in implementing machine learning in a better way. Mahout and Spark both have their advantages and disadvantages. Let us have a

good results regarding the solution quality and success rate in finding optimal solution. Performances of algorithms are tested on mathematical benchmark functions with known global optimum. In order evaluate the optimization power of BSA various benchmark functions are taken into consideration. This dissertation presents the application of GSA on 10 benchmark functions and GOA on 8 benchmark functions. These benchmark functions are the classical functions utilized by many researchers. Despite the

comparison of mean square error using feed forward back propagation (FFBP) and radial basis function(RBF) neural network algorithms are given in table 5.2 for analysis of band pass FIR filter with hanning window. Table 5.2 Comparison of mean square error using FFBP and RBF neural network algorithms used for cut off frequency calculation of band pass FIR digital filter with hanning window Test input (Filter Coefficient) hanning Window (actual cut off Frequency) Output of Artificial Neural Network

Problem 1. (a). Here we have k number of routes from one source to one destination, which is a graph with the source and shrink. Suppose we have n files to transfer from k routes by minimizing the total time. We can apply simple greedy routing algorithm. This algorithm assigns the file to the route which has low load compared to other. Select route from graph which has capacity still greater or equal to the li. (b). The problem belongs to P Class (Polynomial Time). (c). Let number of routes be k Let n

–Learning Based Optimization: An overview Teaching –learning-based optimization (TLBO) is a newly introduced by R.V. Rao in NIT-surat. It is an evolutionary optimization algorithm that inspired from teaching –learning phenomenon of a classroom. It is a novel population based algorithm with faster convergence speed without any algorithm – specific parameters. In TLBO each potential problem is treated as a learner with certain knowledge. The TLBO consist of two phases Teacher phase and Learner phase, in

The first paper proposed a proactive push scheme based on the HTTP/2 server-push feature, the second paper, on the other hand, improved the push method from fixed numbers into adaptive numbers based on multiple factors. In this section, we will compare two outstanding papers with different parameters in the following four metrics: (a) Number of requests The definition of number of requests is the total number of HTTP requests sent by the client during the streaming of the video. This metric measures

K-parcel with locally ideal inside bunch total of squares by moving focuses called Euclidean separation starting with one bunch then onto the next [3]. It is a well-known bunch examination method for investigating a dataset. 2- Naïve Bayes Classifier Algorithm Characterization in information mining is an assignment of anticipating an estimation of all out factors. It should be possible by building models in view of a few factors or highlights for the expectation of a class of a protest on the premise of

Centralized-PSO Authors proposed brought together PSO algorithms, in which the nodes which have vitality better than expected vitality asset are chosen as the CHs. In this creators likewise contrast this calculation and LEACH protocol and with LEACH-C. Reproduction comes about demonstrate that PSO outperform to LEACH and LEACH-C in term of network life time and throughput and so on. It likewise beats GA and K-implies based clustering algorithms. 3.2.18 MST-PSO: Minimum Spanning Tree-PSO Authors proposed

General Approaches for Feature Selection There are 3 types of approaches for feature selection namely filter, wrapper, embedded method. Filter method: Filter method does not involve a learning algorithm for measuring feature subset [6]. It is fast and efficient for computation .filter method can fail to select the feature that are not beneficial by themselves but can be very beneficial when unite with others. Filter method evaluates the feature by giving ranks to their evaluation value. In filter