Integrating deep learning algorithms to overcome challenges in big data analytics - Sujatha, R.; Aarthy, S. L.; Vettriselvan, R. (CRC Press, Taylor & Francis Group, 2022)
Data science revolves around two giants: Big Data analytics and Deep Learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of Big Data as well as Deep Learning systems. This book discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and decision-making. It also covers numerous applications in healthcare, education, communication, media, and entertainment. Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics offers innovative platforms for integrating Big Data and Deep Learning and presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval. FEATURESProvides insight into the skill set that leverages one’s strength to act as a good data analystDiscusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and help in decision-makingCovers numerous potential applications in healthcare, education, communication, media, and entertainmentOffers innovative platforms for integrating Big Data and Deep LearningPresents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval from Big DataThis book is aimed at industry professionals, academics, research scholars, system modelers, and simulation experts
1. Deep Learning for Analyzing the Big Data on Gaming Sector. 2. Deep Learning for Text Analysis. 3. Deep Learning for Analyzing the Data on Humanoid Robots. 4. Deep Learning for Analyzing the Data in IoT Based System. 5. Deep Learning for Analyzing the Data on Object Detection and Recognition. 6. Deep Learning for Medical Dataset Classification. 7. Performance Evaluation and Deep Learning Optimization. 8. Deep Learning for Image Data Classification. 9. World Wide Web Analysis. 10. Cyber Physical System Analysis. 11. Big Data Analysis for Financial Sector (Banking, Insurance, Stock Exchange etc.). 12. Learning Algorithm for Smart Cities using Big Data. 13. Big Data Analytics in Engineering. 14. Big Data Analytics in Healthcare. 15. Learning Algorithm for Social Media Data Analytics. 16. Big Data Analytics in Agriculture. 17. Big Data Analytics for Cloud, Mist and Fog Prediction. 18. Innovative Large-Scale Models for Deep Learning Algorithms and Architectures. 19. Innovative Platforms for Integrating Big Data and Deep Learning
Keywords: bisacsh / COMPUTERS / Database Management / Data Mining; bisacsh / COMPUTERS / Intelligence (AI) & Semantics