Aims and scope

Data Science and Artificial Intelligence is an English-language journal published annually each June, covering the full spectrum of AI and data science research. Its scope includes machine learning (from supervised to reinforcement learning), deep learning and foundational models, and optimization algorithms. It features advances in computer vision, natural language processing and generative AI, as well as big data processing and cloud/decentralized computing. The journal also addresses trustworthy and explainable AI, data visualization techniques, and interdisciplinary applications across healthcare, finance, transportation and more.

  • Language: English
  • Frequency: 1 issues/year
  • Release time: Jun
  • Page size: 21 cm x 29 cm
  • Deposit: CrossRef DOI
  • Template and Guidelines: Word Template or Latex Template

Topics

  • Machine learning: machine learning methods (supervised learning, unsupervised learning, semi-supervised learning, self-supervised learning, reinforcement learning), deep learning, foundational models, optimization algorithms in machine learning, anomaly detection, time series prediction...
  • Computer vision: image classification, object detection, semantic segmentation, action recognition, video processing...
  • Natural language processing: machine translation, sentiment analysis, text summarization, entity recognition, relation extraction, chatbots...
  • Big data: Big data analysis and processing, continuous data stream processing, cloud computing, decentralized computing...
  • Generative AI: Models and applications of generative AI in image, text, speech processing; large language models; code generation and debugging; multimodal data processing…
  • Trustworthy AI: Issues related to transparency, ethics and trust in artificial intelligence systems.
  • Explainable AI (X-AI): Methods and techniques to explain AI model decisions, enhancing trust and usability.
  • Data visualization: Data visualization techniques to represent information effectively.
  • Interdisciplinary applications: applied research of data science and AI in areas such as healthcare, finance, education, transportation, environment, energy, robotics and manufacturing…