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portada Emerging Technologies in Data Mining and Information Security: Proceedings of Iemis 2022, Volume 2
Type
Physical Book
Publisher
Language
Inglés
Pages
759
Format
Paperback
Dimensions
23.4 x 15.6 x 4.0 cm
Weight
1.09 kg.
ISBN13
9789811940514

Emerging Technologies in Data Mining and Information Security: Proceedings of Iemis 2022, Volume 2

Dutta, Paramartha ; Chakrabarti, Satyajit ; Bhattacharya, Abhishek (Author) · Springer · Paperback

Emerging Technologies in Data Mining and Information Security: Proceedings of Iemis 2022, Volume 2 - Dutta, Paramartha ; Chakrabarti, Satyajit ; Bhattacharya, Abhishek

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Synopsis "Emerging Technologies in Data Mining and Information Security: Proceedings of Iemis 2022, Volume 2"

​Part 1: Data Science and Data Analytics.- Chapter 1. Iot Security: Recent Trends And Challenges.- Chapter 2. Assistive Technology for Pedagogical Support and Application of Data Analysis in Neurodevelopmental Disability.- Chapter 3. Developing Smart ML Based Recommendation System.- Chapter 4. Unsupervised Hybrid Change Detection using Geospatial Spectral Classification of Time Series Remote Sensing Datasets.- Chapter 5. Trust based resolving of conflicts for collaborative data sharing in online social networks.- Chapter 6. Startup Profit Predictor using Machine Learning Techniques.- Chapter 7. Right To Be Forgotten In A Post- Ai World: How Effective Is This Right Where Machines Do Not Forget?- Chapter 8. Age, Gender, and Gesture Classification using Open-Source Computer Vision.- Chapter 9. Estimated Time of Arrival for Sustainable Transport using Deep Neural Network.- Chapter 10. Texture Feature based on ANN for Security aspects.- Chapter 11. ML based prediction model for cardiovascular disease.- Chapter 12. Sentiment Analysis in Airlines Industry using Machine Learning Techniques.- Chapter 13. Facial recognition to detect mood and play songs using Machine Intelligence.- Chapter 14. Banana Leaf Diseases and Machine Learning Algorithms Applied To Detect Diseases: A Study.- Chapter 15. Covid-19 prediction analysis using machine learning approach.

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