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  <titleInfo>
    <title>Trends and Applications in Knowledge Discovery and Data Mining</title>
    <subTitle>PAKDD 2024 Workshops, RAFDA and IWTA, Taipei, Taiwan, May 7–10, 2024, Proceedings</subTitle>
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    <namePart>Wang, Zhaoxia.</namePart>
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    <namePart>Tan, Chang Wei.</namePart>
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    <dateIssued encoding="marc">2024</dateIssued>
    <edition>1st ed. 2024.</edition>
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    <extent>XIV, 175 p. 63 illus., 52 illus. in color. online resource.</extent>
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  <abstract>This book constitutes the workshops that have been held in conjunction with the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan, during May 25–28, 2023. For RAFDA 2024, Workshop on Research and Applications of Foundation Models for Data Mining and Affective Computing, 15 submissions have been received and 9 full papers have been accepted for publication. For IWTA 2024, International Workshop on Temporal Analytics, 4 full papers have been accepted from a total of 6 submissions. .</abstract>
  <note type="statement of responsibility">edited by Zhaoxia Wang, Chang Wei Tan.</note>
  <subject authority="lcsh">
    <topic>Artificial intelligence</topic>
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  <subject authority="lcsh">
    <topic>Computers</topic>
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  <subject authority="lcsh">
    <topic>Data mining</topic>
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  <subject authority="lcsh">
    <topic>Information storage and retrieval systems</topic>
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  <subject authority="lcsh">
    <topic>Image processing</topic>
    <topic>Digital techniques</topic>
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  <subject authority="lcsh">
    <topic>Computer vision</topic>
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  <subject>
    <topic>Artificial Intelligence</topic>
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  <subject>
    <topic>Computing Milieux</topic>
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  <subject>
    <topic>Data Mining and Knowledge Discovery</topic>
  </subject>
  <subject>
    <topic>Information Storage and Retrieval</topic>
  </subject>
  <subject>
    <topic>Computer Imaging, Vision, Pattern Recognition and Graphics</topic>
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      <title>Lecture Notes in Artificial Intelligence, 14658</title>
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  <identifier type="isbn">9789819726509</identifier>
  <identifier type="uri">https://doi.org/10.1007/978-981-97-2650-9</identifier>
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