AN EFFICIENT FUZZY NEURAL NETWORK TRAINING MODEL FOR SUPERVISED PATTERN CLASSIFICATION SYSTEM

Among the existing NN architectures, Multilayer Feedforward Neural Network (MFNN) with single hidden layer architecture has been scrutinized thoroughly as best for solving nonlinear classification problem. The training time is consumed more for very huge training datasets in the MFNN training phase. In order to reduce the training time, a simple and fast training algorithm called Exponential Adaptive Skipping Training (EAST) Algorithm was presented that improves the training speed by significantly reducing the total number of training input samples consumed by MFNN for training at every single epoch. Although the training performance of EAST achieves faster, it still lacks in the accuracy rate due to high skipping factor. In order to improve the accuracy rate of the training algorithm, Hybrid system has been suggested in which the neural network is trained with the fuzzified data. In this paper, a z-Score Fuzzy Exponential Adaptive Skipping Training (z-FEAST) algorithm is proposed which is based on the fuzzification of EAST. The evaluation of the proposed z-FEAST algorithm is demonstrated effectively using the benchmark datasets - Iris, Waveform, Heart Disease and Breast Cancer for different learning rate. Simulation study proved that z-FEAST training algorithm improves the accuracy rate

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

A DevOps implementation framework for large agile-based financial organizations

Modern large-scale financial organizations show an interest in embracing a DevOps way of working in addition to Agile adoption. Implementing DevOps next to Agile enhances certain Agile practices while extending other practices. Although there are quite some DevOps maturity models available in the literature, they are either not specific to large-scale financial organizations or do not include the Agile aspects within the desired scope. This study has been performed to identify why such organizations are interested in implementing DevOps and how this implementation can be guided by a conceptual framework. As a result, a list of drivers, a generic DevOps implementation framework and driver-dependent variations are presented. The development of these artifacts has been realized through a design science research method and they have been validated by practitioners from financial organizations in the Netherlands. The practitioners have identified the developed artifacts as useful, mainly to educate people within their organizations. Moreover, the artifacts have been applied to real organizational goals to demonstrate how they can be of help to identify the useful measurement units, which in turn can help to measure and achieve their DevOps transformation goals. Thus, the developed artifacts are not only serving as a baseline for future research but are also useful for existing financial organizations to commence and get ahead with their DevOps implementations

A DevOps implementation framework for large agile-based financial organizations

Modern large-scale financial organizations show an interest in embracing a DevOps way of working in addition to Agile adoption. Implementing DevOps next to Agile enhances certain Agile practices while extending other practices. Although there are quite some DevOps maturity models available in the literature, they are either not specific to large-scale financial organizations or do not include the Agile aspects within the desired scope. This study has been performed to identify why such organizations are interested in implementing DevOps and how this implementation can be guided by a conceptual framework. As a result, a list of drivers, a generic DevOps implementation framework and driver-dependent variations are presented. The development of these artifacts has been realized through a design science research method and they have been validated by practitioners from financial organizations in the Netherlands. The practitioners have identified the developed artifacts as useful, mainly to educate people within their organizations. Moreover, the artifacts have been applied to real organizational goals to demonstrate how they can be of help to identify the useful measurement units, which in turn can help to measure and achieve their DevOps transformation goals. Thus, the developed artifacts are not only serving as a baseline for future research but are also useful for existing financial organizations to commence and get ahead with their DevOps implementations

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