The proposed work considers patient's health data which can be used for decision making or prediction using various calculation, in this work, developing a heart condition prediction system mainly concentrating on artificial neural network, which uses the multilayer perceptron algorithm for the execution...
All feature extractions LDAs and Multilayer perceptron (MLP), are methods for identifying erosion defects are described and employed in this paper. Great accuracy rate in compare between results of related approaches suggests that this Method can be used as an algorithm of MFL data interpretation ...
You can count on the tool to test networked applications for reliability and performance. You can also leverage the tool to assess the impact of network conditions on IoT devices. The tool also does a good job of assessing the functionality of cloud-based services in different environments. The...
In this context, this paper presents an improved algorithm to detect liner surface defects that may compromise the bonding between the solid propellant and the insulation. The use of Local Binary Patterns (LBP) provides a structural and statistical approach to texture analysis of liner sample images...
The main idea is to optimize, simultaneously, the weights and activation function used in a Multilayer Perceptron (MLP), through an approach that combines the advantages of simulated annealing, tabu search and a local learning algorithm. We have chosen two local learning algorithms: the back...
The prediction is carried out with the help of a classification algorithm. This approach should help to lower the number of mutants which have to be executed. An experimental validation of this approach is also presented in this paper. An example of a program used in experiments is described ...
The gap in the data may cause problems for algorithms that try to fit distributions to the data. These problems could lead to bad performance, which would not be detected by our smoke tests, but also to crashes in case no distribution at all can be fit, e.g., because the algorithm ...
The algorithm that outputs the classification label is represented by a model that has to be trained. Several inference methods have been proposed in ML and computational statistics, as listed in Table 5. In supervised ML algorithms, a function is inferred from a set of ground truth-labeled ...
The training algorithm for RFs applies the general technique of bootstrap aggregating, or bagging, to tree learners. The number of trees in RFs is a free parameter that should be determined carefully. Typically, a few hundred to several thousands of trees are used, depending on the size and ...
Based on Westfall and Young (1993, Algorithm 2.5), the MC version of this adjusted p-value is computed as follows: 1. Choose B so that αB is an integer, where α∈(0,1) is the desired FWER. 2. For b=1,…,B−1, repeat the following steps: (a) Using draws from the ...