Deep neural networks (DNN) have remarkably progressed in applications involving large
and complex datasets but have been criticized as a black-box. This downside has recently …

Deep neuro-fuzzy systems (DNFSs) have been successfully applied to real-world problems
using the efficient learning process of deep neural networks (DNNs) and reasoning aptitude …

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem.
However, CNNs depend on deeper network architectures to improve performance of image …

This paper introduces a novel pooling method namely fuzzy based pooling for image
classification. Herein, a pooling method for bolstering the performance of conventional …

Abstract Convolutional Neural Networks (CNN) are being widely practised in computer
vision applications, where pooling indicates as an integral part. Pooling significantly reduces …

Affective systems are getting increasingly more attention from researchers and high-tech
companies in order to enable the acknowledgment or adaptation to a user's mood. Emotion …

In this study, we developed a new fuzzy logic-based convolution layer on a two-dimensional
Convolutional Neural Network (2D-CNN). This innovation aims to enhance the ability of …

Computer vision functions like object detection, image segmentation, and image
classification were recently getting advance due to Convolutional Neural Networks (CNNs) …

Even with the topical developments of numerous image Super Resolution (SR) algorithms,
how to quantify the visual quality scores of a super resolved image is still an open research …

Dalam upaya mendukung program Desa Mandiri Energi (DME), Kelompok Tani dan Ternak
Bangun Rejo dan Andini Jaya Kabupaten Semarang telah memiliki beberapa digester …