IEEE Intelligent Systems

IEEE Intelligent Systems is promoting young and aspiring artificial intelligence (AI) scientists and recognizing the rising stars as “AI‘s 10 Watch.” This biennial 2022 edition is slightly different from the previous editions: We solicited submissions ...

ChatGPT has shown the potential of emerging general artificial intelligence capabilities, as it has demonstrated competent performance across many natural language processing tasks. In this work, we evaluate the capabilities of ChatGPT to perform text ...

The evolution of natural language processing techniques has led to the development of advanced conversational tools such as ChatGPT, capable of assisting users with a variety of activities. Media attention has centered on ChatGPT’s potential impact,...

Multilabel data comprise instances associated with multiple binary target variables. The main learning task from such data is multilabel classification, where the goal is to output a bipartition of the target variables into relevant and irrelevant ones ...

Deep anomaly analytics is a rapidly evolving field that leverages the power of deep learning to identify anomalies in various datasets. The use of deep anomaly analytics has increased significantly in recent years due to the growing need to detect ...

Fine particulate matter (PM2.5) values of a particular location form a time series, whose prediction is challenging due to the complicated interactions between numerous factors from meteorological measurements, terrain conditions, and industry and human ...

With the increasing deployment of small unmanned aerial systems (sUASs) on various tasks, it becomes crucial to analyze and detect anomalies from their flight logs. To support research in this area, we curate Drone Log Anomaly (DLA), the first real-world ...

Graph anomaly detection is a popular and vital task in various real-world scenarios, which has been studied for several decades. Recently, many studies extending deep learning-based methods have shown preferable performance on graph anomaly detection. ...

Hyperspectral imaging (HSI) is a widely used technology, yet hard to implement in real-time anomaly detection due to its extensive data flow volume. An autoencoder structured hybrid optical–electrical neural network method is proposed in this work that ...

We have conducted a comparison between three types of recurrent neural networks and their ability to predict anomalies occurring in oil wells using a publicly available dataset. We have included two types of well-known state-of-the-art recurrent neural ...