There are a vast number of quantitative and qualitative technological forecasting methods. In
the last decade, advanced quantitative technological forecasting methods based on the …

Abstract Machine Learning (ML) algorithms are generally designed for scenarios in which all
data is stored in one data center, where the training is performed. However, in many …

Automatic recognition of grocery products can be used to improve customer flow at
checkouts and reduce labor costs and store losses. Product recognition is, however, a …

Intent detection and slot filling are two main tasks in natural language understanding and
play an essential role in task-oriented dialogue systems. The joint learning of both tasks can …

The effectiveness of deep learning algorithms in discerning stress levels from biomedical
signals has attracted considerable attention. However, the computational demands of these …

The availability of new datasets and deep learning techniques have led to a surge of effort
directed towards the creation of new models that can exploit the large amount of data …

In this paper, a short-length document classification of the arxiv dataset using RoBERTa
(Robustly Optimized BERT Pre-training Approach) was performed. Here, the document …

In the past few years, the use of transformer-based models has experienced increasing
popularity as new state-of-the-art performance was achieved in several natural language …

Blockchain is rapidly changing land record management worldwide. It could solve land
conflicts and corruption in India's property ownership structure. Blockchain and smart …

During the last decades, a lot of step forward are made in Artificial Intelligence field. Until
recently, the Cloud Computing paradigm has allowed for increasingly complex and large …