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Corrigenda and Addenda (43)

Published on in Vol 4, No 4 (2016): Oct-Dec

Correction of: Sleep Quality Prediction From Wearable Data Using Deep Learning

Correction of: Sleep Quality Prediction From Wearable Data Using Deep Learning

Correction of: Sleep Quality Prediction From Wearable Data Using Deep Learning

Authors of this article:

Aarti Sathyanarayana1 Author Orcid Image ;   Shafiq Joty1 Author Orcid Image ;   Luis Fernandez-Luque1 Author Orcid Image ;   Ferda Ofli1 Author Orcid Image ;   Jaideep Srivastava1 Author Orcid Image ;   Ahmed Elmagarmid1 Author Orcid Image ;   Teresa Arora2 Author Orcid Image ;   Taheri Shahrad2 Author Orcid Image
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  3. Gao Z, Liu W, McDonough D, Zeng N, Lee J. The Dilemma of Analyzing Physical Activity and Sedentary Behavior with Wrist Accelerometer Data: Challenges and Opportunities. Journal of Clinical Medicine 2021;10(24):5951 View
  4. Misra B, Roy N, Dey N, Sherratt R. Visualizing Wearable Medical Device Research Trends: A Co-occurrence Network-Based Bibliometric Analysis. Galician Medical Journal 2023;30(3):E202332 View
  5. Miller D, Roach G, Lastella M, Capodilupo E, Sargent C. Hit the gym or hit the hay: can evening exercise characteristics predict compromised sleep in healthy adults?. Frontiers in Physiology 2023;14 View
  6. Geng L, Yan P, Ji Z, Song C, Song S, Zhang R, Zhang Z, Zhai Y, Jiang L, Yang K. A novel nondestructive detection approach for seed cotton lint percentage using deep learning. Journal of Cotton Research 2024;7(1) View

Books/Policy Documents

  1. Kumar U, Tripathi E, Tripathi S, Gupta K. Design and Implementation of Healthcare Biometric Systems. View
  2. Mathew P, Pillai A. Enabling AI Applications in Data Science. View