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Enhancing Big Data Analytics through Deep Learning: Challenges and Future Directions

Author

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  • Dipikaben Umakant Thakar
  • Bhawesh Kumawat
  • Darshanaben Dipakkumar Pandya
  • Khushbu

Abstract

Big Data Analytics and Deep Learning are crucial fields due to the use of massive domain-specific data for solving complex challenges in cybersecurity, marketing, and healthcare. Deep Learning facilitates recognition of complex patterns via hierarchical learning and building higher-level abstractions on top of lower-level abstractions, suitable for handling colossal volumes of untagged data. This work discusses how issues in semantic indexing, data annotation, and fast information retrieval could be addressed with Deep Learning. It also recognizes problems such as streaming data, high-dimensionality, and scalability, and in the future, work will target data sampling, improved semantic indexing, and semi-supervised learning methods. In addition, inclusion of distributed computing for scalability is also mentioned among the key areas of future research in Deep Learning models.

Suggested Citation

  • Dipikaben Umakant Thakar & Bhawesh Kumawat & Darshanaben Dipakkumar Pandya & Khushbu, 2025. "Enhancing Big Data Analytics through Deep Learning: Challenges and Future Directions," International Journal of Scientific Research in Science, Engineering and Technology, Technoscience Academy, vol. 12(2), pages 155-159, April.
    • Handle: RePEc:ijs:ijsrse:v12:y2025:i2:id:348
    DOI: 10.32628/IJSRSET25122142
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