Computational intelligence and neuroscience
Jul 6, 2021
Deep learning is the latest trend of machine learning and artificial intelligence research. As a new field with rapid development over the past decade, it has attracted more and more researchers' attention. Convolutional Neural Network (CNN) model is...
Convolutional neural networks (CNNs) are a state-of-the-art technique for speech emotion recognition. However, CNNs have mostly been applied to noise-free emotional speech data, and limited evidence is available for their applicability in emotional s...
The quantitative modeling of semantic representations in the brain plays a key role in understanding the neural basis of semantic processing. Previous studies have demonstrated that word vectors, which were originally developed for use in the field o...
Computational intelligence and neuroscience
Jun 14, 2021
Around 5% of the world population suffers from hearing impairment. One of its main barriers is communication with others since it could lead to their social exclusion and frustration. To overcome this issue, this paper presents a system to interpret ...
BACKGROUND: Unstructured text created by patients represents a rich, but relatively inaccessible resource for advancing patient-centred care. This study aimed to develop an ontology for ocular immune-mediated inflammatory diseases (OcIMIDo), as a too...
Assistive technology : the official journal of RESNA
May 28, 2021
Socially assistive robots (SAR) have the potential to impact therapies for Autism Spectrum Disorder (ASD) by supporting clinicians in increasing learning opportunities presented to individuals. Recent research on robot-mediated intervention (RMI) del...
BACKGROUND: Biomedical question answering (QA) is a sub-task of natural language processing in a specific domain, which aims to answer a question in the biomedical field based on one or more related passages and can provide people with accurate healt...
Computational intelligence and neuroscience
May 26, 2021
Relation classification is an important semantic processing task in the field of natural language processing (NLP). Data sources generally adopt remote monitoring strategies to automatically generate large-scale training data, which inevitably causes...
Neural networks : the official journal of the International Neural Network Society
May 25, 2021
Existing language models (LMs) represent each word with only a single representation, which is unsuitable for processing words with multiple meanings. This issue has often been compounded by the lack of availability of large-scale data annotated with...
The volume of Amharic digital documents has grown rapidly in recent years. As a result, automatic document categorization is highly essential. In this paper, we present a novel dimension reduction approach for improving classification accuracy by com...
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