There are many sounds all around us and our brain can easily clearly identify them. Furthermore, processes the received sound signals continuously provides with relevant environmental knowledge. Although not up to level of accuracy brain, there some smart devices which extract necessary information from an audio signal help different algorithms. Over years, various models like Convolutional Neu...

This paper proposes an approach to automatic music genre classification using deep belief networks. Based on the restricted Boltzmann machines, the deep belief networks is constructed and takes the acoustic features extracted through content-based analysis of music signals as input. The model parameters are initially determined after the deep belief network is trained by greedy layer-wise learn...

20min 684 Single trial P300 identification for an auditory BCI: implementation of a 3D input for convolutional neural networks Eduardo Carabez*, Miho Sugi, Isao Nambu, Yasuhiro Wada (Japan) 20min 1007 Deep Learning-Based Classification for Brain-Computer Interfaces John Thomas*, Tomasz Maszczyk, Sinha Nishant, Kluge Tilmann, Justin Dauwels 20min 691 Driver’s Fatigue Prediction by Deep Covarianc...

Deep convolutional neural networks (DCNNs) are an influential tool for solving various problems in the machine learning and computer vision fields. In this paper, we introduce a new deep learning model called an InceptionRecurrent Convolutional Neural Network (IRCNN), which utilizes the power of an inception network combined with recurrent layers in DCNN architecture. We have empirically evalua...

Neural networks are one of the most popular approaches for many natural language processing tasks such as sentiment analysis. They often outperform traditional machine learning models and achieve the state-of-art results on most tasks. However, many existing deep learning models are complex, difficult to train, and provide limited improvement over simpler methods. We propose a simple, robust an...

The porosity within a reservoir rock is a basic parameter for the reservoir characterization. The present paper introduces two intelligent models for identification of the porosity types using image analysis. For this aim, firstly, thirteen geometrical parameters of pores of each image were extracted using the image analysis techniques. The extracted features and their corresponding pore types ...

Automatic and accurate estimation of disease severity is essential for food security, disease management, and yield loss prediction. Deep learning, the latest breakthrough in computer vision, is promising for fine-grained disease severity classification, as the method avoids the labor-intensive feature engineering and threshold-based segmentation. Using the apple black rot images in the PlantVi...

While deep learning has led to remarkable advances across diverse applications, it struggles in domains where the data distribution changes over the course of learning. In stark contrast, biological neural networks continually adapt to changing domains, possibly by leveraging complex molecular machinery to solve many tasks simultaneously. In this study, we introduce intelligent synapses that br...

Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task due to two main issues: lack of sufficient training data for every class and difficulty in learning discriminative features for representation. In this paper, to address the two issues, we propose a two-phase framework for recognizing images from unseen fine-grained classes, i.e. ...

In this chapter, we focus on two important areas in neural computation, i.e., deep and modular neural networks, given the fact that both deep and modular neural networks have been among the most powerful machine learning and pattern recognition techniques for complex AI problem solving. We begin by providing a general overview of deep and modular neural networks to describe the general motivati...