Page 92 - Big Data Analytics for Intelligent Healthcare Management
P. 92
REFERENCES 85
[19] R. Sahani, C. Rout, J.C. Badajena, A.K. Jena, H. Das, Classification of intrusion detection using data mining
techniques, in: Progress in Computing, Analytics and Networking, Springer, Singapore, 2018, pp. 753–764.
[20] H. Das, A.K. Jena, J. Nayak, B. Naik, H.S. Behera, A Novel PSO Based Back Propagation Learning-MLP
(PSO-BP-MLP) for Classification, Computational Intelligence in Data Mining, Vol. 2, Springer, New Delhi,
2015, pp. 461–471.
[21] C. Pradhan, H. Das, B. Naik, N. Dey, Handbook of Research on Information Security in Biomedical Signal
Processing. IGI Global, Hershey, PA, 2018, pp. 1–414, https://doi.org/10.4018/978-1-5225-5152-2.
[22] K.H.K. Reddy, H. Das, D.S. Roy, A Data Aware Scheme for Scheduling Big-Data Applications with
SAVANNA Hadoop. Futures of Network, CRC Press, 2017.
[23] B.S.P. Mishra, H. Das, S. Dehuri, A.K. Jagadev, Cloud Computing for Optimization: Foundations, Applica-
tions, and Challenges, 39 Springer, 2018.
[24] P.K. Pattnaik, S.S. Rautaray, H. Das, J. Nayak (Eds.), Progress in Computing, Analytics and Networking:
Proceedings of ICCAN 2017, Vol. 710, Springer, 2018.
[25] C.R. Panigrahi, M. Tiwary, B. Pati, H. Das, Big data and cyber foraging: Future scope and challenges,
in: Techniques and Environments for Big Data Analysis, Springer, Cham, 2016, pp. 75–100.
[26] Cs231n.github.io, CS231n Convolutional Neural Networks for Visual Recognition, Available from: http://
cs231n.github.io/convolutional-networks/, 2018. (Accessed 25 September 2018).
[27] K. He, X. Zhang, S. Ren, J. Sun, Deep Residual Learning for Image Recognition, Arxiv.org. Available from:
https://arxiv.org/abs/1512.03385, 2018. (Accessed 25 September 2018).
[28] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the Inception Architecture for Computer
Vision, Arxiv.org. Available from: https://arxiv.org/abs/1512.00567, 2018. (Accessed 25 September 2018).
[29] C. Szegedy, S. Ioffe, V. Vanhoucke, A. Alemi, Inception-v4, Inception-ResNet and the Impact of Residual
Connections on Learning, Arxiv.org. Available from: https://arxiv.org/abs/1602.07261, 2018. (Accessed 25
September 2018).
[30] F. Chollet, Xception: Deep Learning with Depthwise Separable Convolutions, Arxiv.org. Available from:
https://arxiv.org/abs/1610.02357, 2018. (Accessed 25 September 2018).
[31] Keras.io, Keras Documentation, Available from: https://keras.io/, 2018. (Accessed 10 June 2018).
[32] Cs231n.github.io, CS231n Convolutional Neural Networks for Visual Recognition, Available from: http://
cs231n.github.io/transfer-learning/, 2018. (Accessed 10 June 2018).
[33] GeeksforGeeks, Introduction to Dimensionality Reduction-GeeksforGeeks, Available from: https://www.
geeksforgeeks.org/dimensionality-reduction/, 2018. (Accessed 11 June 2018).
[34] Plot.ly, Principal Component Analysis, Available from: https://plot.ly/ipython-notebooks/principal-
component-analysis/, 2018. (Accessed 11 June 2018).
[35] J. Brownlee, Supervised and Unsupervised Machine Learning Algorithms. Machine Learning Mastery,
Available from: https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-
algorithms/, 2018. (Accessed 10 June 2018).
[36] J. Brownlee, Logistic Regression for Machine Learning. Machine Learning Mastery, Available from: https://
machinelearningmastery.com/logistic-regression-for-machine-learning/, 2018. (Accessed 10 June 2018).
[37] M. Learning, U. Code, Understanding Support Vector Machine Algorithm From Examples (Along With
Code). Analytics Vidhya, Available from: https://www.analyticsvidhya.com/blog/2017/09/understaing-
support-vector-machine-example-code/, 2018. (Accessed 10 June 2018).
[38] J. Brownlee, K-Nearest Neighbors for Machine Learning. Machine Learning Mastery, Available from:
https://machinelearningmastery.com/k-nearest-neighbors-for-machine-learning/, 2018. (Accessed 10 June
2018).
[39] Scikit-image.org, Scikit-Image: Image Processing in Python—Scikit-Image, Available from: http://scikit-
image.org/, 2018. (Accessed 10 June 2018).
[40] Scikit-learn.org, scikit-Learn: Machine Learning in Python—Scikit-Learn 0.19.1 Documentation, Available
from: http://scikit-learn.org/stable/, 2018. (Accessed 10 June 2018).