Voice signals are the essential input source for applications based on human and computer interaction technology. Gender identification through voice is one of most challenging tasks. For signal analysis, deep learning algorithms provide an alternative to traditional conventional classification. To identify gender female, male ‘first-time’ transgender, algorithm used improve robustness model wi...

Previous studies on Data Augmentation (DA) mostly use a fine-tuned Language Model (LM) to strengthen the constraints but ignore fact that potential of diversity could improve effectiveness generated data. To address this dilemma, we propose Diversity-Enhanced and Constraints-Relaxed (DECRA) has two essential components top transformer-based backbone model, including \(\mathbf {k}\)-\(\varvec{\b...

Pathology slides of lung malignancies are classified using resource-frugal convolution neural networks (CNNs) that may be deployed on mobile devices. In particular, the challenging task distinguishing adenocarcinoma (LUAD) and squamous-cell carcinoma (LUSC) cancer subtypes is approached in two stages. First, whole-slide histopathology images downsampled to a size too large for CNN analysis but ...

It is necessary to provide a method to store Web information effectively so it can be utilised as a future knowledge resource. A commonly adopted approach is to classify the retrieved information based on its content. A technique that has been found to be suitable for this purpose is Multiple Classification Ripple-Down Rules (MCRDR). The MCRDR system constructs a classification knowledge base o...

Sensor applications often face three main restrictions: power consumption, cost, and lack of infrastructure. Most the challenges imposed by these limitations can be addressed embedding Machine Learning (ML) classifiers in sensor hardware, creating smart sensors able to interpret low-level data stream. However, for this approach cost-effective, we need highly efficient suitable execute resource-...

In many languages, sparse availability of resources causes numerous challenges for textual analysis tasks. Text classification is one of such standard tasks that is hindered due to limited availability of label information in lowresource languages. Transferring knowledge (i.e. label information) from high-resource to low-resource languages might improve text classification as compared to the ot...

Recent research has shown the usefulness of social tags as a data source to feed resource classification. Little is known about the effect of settings on folksonomies created on social tagging systems. In this work, we consider the settings of social tagging systems to further understand tag distributions in folksonomies. We analyze in depth the tag distributions on three large-scale social tag...

Social or collaborative tagging systems emerged as a novel classification scheme on the Web based on the collective knowledge of people. In sites such as Del.icio.us, Technorati or Flickr, users annotate a variety of resources, including Web pages, blogs, pictures, videos or bibliographic references; using freely chosen textual labels or tags. Underlying collaborative tagging systems are ternar...