Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …

In order to spot trends in the methodologies and procedures employed, this systematic
literature review will look at works on time series categorization. Six research questions are …

Talent recruitment has become a crucial issue for companies since finding suitable
candidates from the massive data on potential candidates from online talent platforms is a …

We are trying to reveal the status of Chinese universities in the global talent circulation
system and how university prestige affects mobility patterns in China. Other than geographic …

Monitoring water demand is extremely helpful in the early detection of issues and
malfunctions in water distribution networks. Therefore, distinguishing abnormal water meter …

Modeling complex data, eg time series as well as network-based data, is a prominent area
of research. In this paper, we focus on a combination of both, analyzing network-based …

This paper presents twin-hyperspheres of resisting noise for binary classification to
imbalanced data with noise. First, employing the decision of evaluating the contributions …

Abstract Cognitive Internet of Things (CIoT) is a new subfield of the Internet of Things (IoT)
that aims to integrate cognition into the IoT's architecture and design. Various CIoT …

Aiming at binary classification of highly imbalanced data, this paper proposes a novel twin-
hypersphere method with conformal transformation. To provide favorable environments that …

In recent years, two time series classification models, ROCKET and MINIROCKET, have
attracted much attention for their low training cost and state-of-the-art accuracy. Utilizing …