Mike 5-15-2018 - Computer Science bibliographies - in Harvard style

In-text: (Amato et al., 2013)

Your Bibliography: Amato, F., López, A., Peña-Méndez, E., Vaňhara, P., Hampl, A. and Havel, J., 2013. Artificial neural networks in medical diagnosis. Journal of Applied Biomedicine, 11(2), pp.47-58.

In-text: (Andrysiak, 2016)

Your Bibliography: Andrysiak, T., 2016. Machine Learning Techniques Applied to Data Analysis and Anomaly Detection in ECG Signals. Applied Artificial Intelligence, 30(6), pp.610-634.

In-text: (Arboleda, Aedo and Rivera, 2016)

Your Bibliography: Arboleda, J., Aedo, J. and Rivera, F., 2016. Wireless system for supporting home health care of chronic disease patients. 2016 IEEE Colombian Conference on Communications and Computing (COLCOM),.

In-text: (Bychkov et al., 2018)

Your Bibliography: Bychkov, D., Linder, N., Turkki, R., Nordling, S., Kovanen, P., Verrill, C., Walliander, M., Lundin, M., Haglund, C. and Lundin, J., 2018. Deep learning based tissue analysis predicts outcome in colorectal cancer. Scientific Reports, 8(1).

In-text: (Cabitza, Rasoini and Gensini, 2017)

Your Bibliography: Cabitza, F., Rasoini, R. and Gensini, G., 2017. Unintended Consequences of Machine Learning in Medicine. JAMA, 318(6), p.517.

In-text: (Guo, Chen, Chen and Lv, 2015)

Your Bibliography: Guo, H., Chen, L., Chen, G. and Lv, M., 2015. Smartphone-based activity recognition independent of device orientation and placement. International Journal of Communication Systems, 29(16), pp.2403-2415.

In-text: (Hassanpour et al., 2017)

Your Bibliography: Hassanpour, S., Korbar, B., Olofson, A., Miraflor, A., Nicka, C., Suriawinata, M., Torresani, L. and Suriawinata, A., 2017. Deep learning for classification of colorectal polyps on whole-slide images. Journal of Pathology Informatics, 8(1), p.30.

In-text: (Heng, Wang and Wang, 2014)

Your Bibliography: Heng, X., Wang, Z. and Wang, J., 2014. Human activity recognition based on transformed accelerometer data from a mobile phone. International Journal of Communication Systems, 29(13), pp.1981-1991.

In-text: (Huang, Huang and Jong, 2013)

Your Bibliography: Huang, C., Huang, K. and Jong, G., 2013. Artificial neural network integrated heart rate variability with detection system. 2013 International Joint Conference on Awareness Science and Technology & Ubi-Media Computing (iCAST 2013 & UMEDIA 2013),.

In-text: (Janowczyk and Madabhushi, 2016)

Your Bibliography: Janowczyk, A. and Madabhushi, A., 2016. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. Journal of Pathology Informatics, 7(1), p.29.

In-text: (Lisboa, 2002)

Your Bibliography: Lisboa, P., 2002. A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Networks, 15(1), pp.11-39.

In-text: (Matta, Sankari and Rihana, 2018)

Your Bibliography: Matta, S., Sankari, Z. and Rihana, S., 2018. Heart rate variability analysis using neural network models for automatic detection of lifestyle activities. Biomedical Signal Processing and Control, 42, pp.145-157.

In-text: (P.T., Joseph and Jacob, 2011)

Your Bibliography: P.T., A., Joseph, P. and Jacob, J., 2011. Automated Diagnosis of Diabetes Using Heart Rate Variability Signals. Journal of Medical Systems, 36(3), pp.1935-1941.

In-text: (Rajkomar et al., 2018)

Your Bibliography: Rajkomar, A., Oren, E., Chen, K., Dai, A., Hajaj, N., Hardt, M., Liu, P., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G., Irvine, J., Le, Q., Litsch, K., Mossin, A., Tansuwan, J., Wang, D., Wexler, J., Wilson, J., Ludwig, D., Volchenboum, S., Chou, K., Pearson, M., Madabushi, S., Shah, N., Butte, A., Howell, M., Cui, C., Corrado, G. and Dean, J., 2018. Scalable and accurate deep learning with electronic health records. npj Digital Medicine, 1(1).

In-text: (Silipo and Marchesi, 1998)

Your Bibliography: Silipo, R. and Marchesi, C., 1998. Artificial neural networks for automatic ECG analysis. IEEE Transactions on Signal Processing, 46(5), pp.1417-1425.

In-text: (Trebeschi et al., 2017)

Your Bibliography: Trebeschi, S., van Griethuysen, J., Lambregts, D., Lahaye, M., Parmar, C., Bakers, F., Peters, N., Beets-Tan, R. and Aerts, H., 2017. Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR. Scientific Reports, 7(1).