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Convolutional Neural Networks (CNNs) and its variants are increasingly used across wide domain of applications achieving high performance measures. For high performance, application specific CNN architecture is required, hence the need for network architecture search (NAS) becomes essential. This paper proposes a hybrid evolutionary approach for network architecture search (HyENAS), and targets convolution...
This paper presents the application of Interval Type-2 Subsethood Neural Fuzzy Inference System (IT2SuNFIS) [1] in the area of control of a chemical plant and function approximation. In this model, a subsethood method between the inputs and hidden rule layer neurons determines the similarity between interval type-2 fuzzy set (IT2 FS) inputs and IT2 FS antecedents. The inputs to the system are fuzzified...
This paper proposes heterogeneous modular deep neural network (DNN) to address a complex problem of detection of diabetic retinopathy and simultaneously the five types of abnormalities. The modular approach gives the advantage to extract class specific features for the classifier, which helps to outperform the classical convolutional neural networks. Moreover, the heterogeneous nature of modular DNN...
Neuro-fuzzy models are being increasingly employed in the domains like weather forecasting, stock market prediction, computational finance, control, planning, physics, economics and management, to name a few. These models enable one to predict system behavior in a more human-like manner than their crisp counterparts. In the present work, an interval type-2 neuro-fuzzy evolutionary subsethood based...
Over the years conventional neural networks has shown state-of-art performance on many problems. However, their performance on recognition system is still not widely accepted in the machine learning community because these networks are unable to handle selectivity-invariance dilemma and also suffer from the problem of vanishing gradients. Some of these issues have been addressed by deep learning....
The applications requiring massive computations may get benefit from the Graphics Processing Units (GPUs) with Compute Unified Device Architecture (CUDA) by reducing the execution time. Since the introduction of CUDA, applications from different areas have been benefited. Evolutionary algorithms are one such potential area where CUDA implementation proves to be beneficial not only in terms of the...
The automatic simultaneous selection of structure and parameters of an artificial neural networks is an important area of research. Although many variants of evolutionary algorithms (EA) have been successfully applied to this problem, their demanding memory requirements have restricted their application to real world problems, especially embedded applications with memory constraints. In this paper,...
This paper introduces an island model approach for differential evolution (DE) learning in asymmetric subsethood product fuzzy neural inference system (ASuPFuNIS). In the island model, each island executes an independent DE and maintains its own sub-population for search. The migration model scheme has been implemented here to parallelize ASuPFuNIS. The parallelization strategy presented here is compared...
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