Search results for "NEURAL NETWORK"

CDRX modelling in friction stir welding of aluminium alloys: a neural network based approach

2007

Coarse scales are sufficient for efficient categorization of emotional facial expressions: Evidence from neural computation

2010

The human perceptual system performs rapid processing within the early visual system: low spatial frequency information is processed rapidly through magnocellular layers, whereas the parvocellular layers process all the spatial frequencies more slowly. The purpose of the present paper is to test the usefulness of low spatial frequency (LSF) information compared to high spatial frequency (HSF) and broad spatial frequency (BSF) visual stimuli in a classification task of emotional facial expressions (EFE) by artificial neural networks. The connectionist modeling results show that an LSF information provided by the frequency domain is sufficient for a distributed neural network to correctly cla…

Fair Pairwise Learning to Rank

2020

Ranking algorithms based on Neural Networks have been a topic of recent research. Ranking is employed in everyday applications like product recommendations, search results, or even in finding good candidates for hiring. However, Neural Networks are mostly opaque tools, and it is hard to evaluate why a specific candidate, for instance, was not considered. Therefore, for neural-based ranking methods to be trustworthy, it is crucial to guarantee that the outcome is fair and that the decisions are not discriminating people according to sensitive attributes such as gender, sexual orientation, or ethnicity.In this work we present a family of fair pairwise learning to rank approaches based on Neur…

Adaptive Distance-Based Pooling in Convolutional Neural Networks for Audio Event Classification

2020

In the last years, deep convolutional neural networks have become a standard for the development of state-of-the-art audio classification systems, taking the lead over traditional approaches based on feature engineering. While they are capable of achieving human performance under certain scenarios, it has been shown that their accuracy is severely degraded when the systems are tested over noisy or weakly segmented events. Although better generalization could be obtained by increasing the size of the training dataset, e.g. by applying data augmentation techniques, this also leads to longer and more complex training procedures. In this article, we propose a new type of pooling layer aimed at …

Classification of Heart Sounds Using Convolutional Neural Network

2020

Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connected layers that are usually used before the classification layer were replaced with a global averag…

Toward Optimal LSTM Neural Networks for Detecting Algorithmically Generated Domain Names

2021

Malware detection is a problem that has become particularly challenging over the last decade. A common strategy for detecting malware is to scan network traffic for malicious connections between infected devices and their command and control (C&C) servers. However, malware developers are aware of this detection method and begin to incorporate new strategies to go unnoticed. In particular, they generate domain names instead of using static Internet Protocol addresses or regular domain names pointing to their C&C servers. By using a domain generation algorithm, the effectiveness of the blacklisting of domains is reduced, as the large number of domain names that must be blocked g…

Automatic Content Analysis of Computer-Supported Collaborative Inquiry-Based Learning Using Deep Networks and Attention Mechanisms

2020

Computer-supported collaborative inquiry-based learning (CSCIL) represents a form of active learning in which students jointly pose questions and investigate them in technology-enhanced settings. Scaffolds can enhance CSCIL processes so that students can complete more challenging problems than they could without scaffolds. Scaffolding CSCIL, however, would optimally adapt to the needs of a specific context, group, and stage of the group's learning process. In CSCIL, the stage of the learning process can be characterized by the inquiry-based learning (IBL) phase (orientation, conceptualization, investigation, conclusion, and discussion). In this presentation, we illustrate the potential of a…

Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification

2020

The search for anti-nucleus antibodies (ANA) represents a fundamental step in the diagnosis of autoimmune diseases. The test considered the gold standard for ANA research is indirect immunofluorescence (IIF). The best substrate for ANA detection is provided by Human Epithelial type 2 (HEp-2) cells. The first phase of HEp-2 type image analysis involves the classification of fluorescence intensity in the positive/negative classes. However, the analysis of IIF images is difficult to perform and particularly dependent on the experience of the immunologist. For this reason, the interest of the scientific community in finding relevant technological solutions to the problem has been high. Deep lea…

Deep Convolutional Neural Networks for Fire Detection in Images

2017

Detecting fire in images using image processing and computer vision techniques has gained a lot of attention from researchers during the past few years. Indeed, with sufficient accuracy, such systems may outperform traditional fire detection equipment. One of the most promising techniques used in this area is Convolutional Neural Networks (CNNs). However, the previous research on fire detection with CNNs has only been evaluated on balanced datasets, which may give misleading information on real-world performance, where fire is a rare event. Actually, as demonstrated in this paper, it turns out that a traditional CNN performs relatively poorly when evaluated on the more realistically balance…

Resource-efficient hardware implementation of a neural-based node for automatic fingerprint classification

2017

Modern mobile communication networks and Internet of Things are paving the way to ubiquitous and mobile computing. On the other hand, several new computing paradigms, such as edge computing, demand for high computational capabilities on specific network nodes. Ubiquitous environments require a large number of distributed user identification nodes enabling a secure platform for resources, services and information management. Biometric systems represent a useful option to the typical identification systems. An accurate automatic fingerprint classification module provides a valuable indexing scheme that allows for effective matching in large fingerprint databases. In this work, an efficient em…