Scientific collaboration is a significant behavior in knowledge creation and idea exchange. To tackle large and complex research questions, a trend of team formation has been observed in recent decades. In this study, we focus on recognizing collaborative teams and exploring inner patterns using scholarly big graph data. We propose a collaborative team recognition (CORE) model with a "core + extension" team structure to recognize collaborative teams in large academic networks. In CORE, we combine an effective evaluation index called the collaboration intensity index with a series of structural features to recognize collaborative teams in which members are in close collaboration relationships. Then, CORE is used to guide the core team members to their extension members. CORE can also serve as the foundation for team-based research. The simulation results indicate that CORE reveals inner patterns of scientific collaboration: senior scholars have broad collaborative relationships and fixed collaboration patterns, which are the underlying mechanisms of team assembly. The experimental results demonstrate that CORE is promising compared with state-of-the-art methods.

Time series analysis is relevant in various disciplines such as physics, biology, chemistry, and Time series analysis plays a pivotal role in extracting valuable finance. In this paper, we present a novel neural insights from sequential data, uncovering patterns, network architecture that integrates elements trends, and underlying structures that drive temporal dynamics from ResNet structures, while introducing the innovative (Zhang, 2003; Tang et al., 1991). The ubiquity incorporation of the Taylor series framework. of time series data across diverse domains, including finance, This approach demonstrates notable enhancements healthcare, and environmental science, underscores in test accuracy across many of the the critical need for accurate and efficient analytical methods baseline datasets investigated.

I need to admit that it is unclear how image search will work with other domains. At the moment, everything is designed to work on natural images. To be applied to medical or satellite, I will need new models, and I do not have them in front of me. If there is interest, we can explore this option. I have a request -- if you have an idea how your product may benefit from an image search, do me a favor, and write in the comments or message on LinkedIn.

Welcome to the February edition of our best and favorite articles in AI that were published this month. We are a Paris-based company that does Agile data development. This month, we spotted among others, articles about AI that can diagnose breast cancer with higher accuracy than experts! Let's start, as usual, with the comic of the month: A recent evaluation of a AI system for breast cancer screening concludes that it is capable of surpassing human experts in breast cancer prediction. It is essential to identify breast cancer at earlier stages of the disease when treatment can be more successful.