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Integrated Ferroelectrics
An International Journal
Volume 201, 2019 - Issue 1
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Articles

Ferroelectric Memristive Networks for Dimensionality Reduction: A Process for Effectively Classifying Cancer Datasets

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Pages 126-141 | Received 10 Jul 2019, Accepted 06 Sep 2019, Published online: 10 Dec 2019

References

  • C. E. DeSantis et al., Cancer treatment and survivorship statistics, CA. Cancer J. Clin. 64 (4), 252 (2014). DOI: 10.3322/caac.21235.
  • B. Davies et al., Activity of type IV collagenases in benign and malignant breast disease. Br. J. Cancer 67 (5), 1126 (1993). DOI: 10.1038/bjc.1993.207.
  • L. Fass, Imaging and cancer: a review. Mol. Oncol. 2 (2), 115 (2008). DOI: 10.1016/j.molonc.2008.04.001.
  • J. A. Ludwig and J. N. Weinstein, Biomarkers in cancer staging, prognosis and treatment selection. Nat. Rev. Cancer 5 (11), 845 (2005). DOI: 10.1038/nrc1739.
  • H. Zaidi, H. Vees, and M. Wissmeyer, Molecular PET/CT imaging-guided radiation therapy treatment planning. Acad. Radiol. 16 (9), 1108 (2009). DOI: 10.1016/j.acra.2009.02.014.
  • B. L. Lambert et al., Diagnostic error in medicine. Arch Intern Med. 169 (20), 1881 (2009).
  • D. Dua and E. K. Taniskidou, Breast Cancer Wisconsin (Original) Data Set – UCI Machine Learning Repository (n.d.). https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%2
  • Y. Kurita et al., A novel “SMAFTI” package for inter-chip wide-band data transfer, presented at the 56th Electronic Components and Technology Conference, San Diego, CA, USA, 30 May to 2 June 2006.
  • K. Kourou et al., Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 13, 8 (2015). DOI: 10.1016/j.csbj.2014.11.005.
  • M. Heidari et al., Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm. Phys. Med. Biol. 63 (3), 035020 (2018). DOI: 10.1088/1361-6560/aaa1ca.
  • Ö. Türel, J. H. Lee, X. Ma, and K. K. Likharev, Neuromorphic architectures for nanoelectronic circuits. Int. J. Circ. Theor. Appl. 32 (5), 277 (2004). DOI: 10.1002/cta.282.
  • L. O. Chua and L. Yang, Cellular neural networks: applications. IEEE Trans. Circuits Syst. 35 (10), 1273 (1988). DOI: 10.1109/31.7601.
  • J. Friedman, T. Hastie, and R. Tibshirani, Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann. Stat. 28 (2), 337 (2000). DOI: 10.1214/aos/1016218223.
  • S. Choi, P. Sheridan, and W. D. Lu, Data clustering using memristor networks. Sci. Rep. 5, 10492 (2015).DOI: 10.1038/srep10492.
  • I. T. Jolliffe, Principal Component Analysis, Second Edition. Encyclopedia of Statistics in Behavioral Science (Springer-Verlag, New York, NY, USA, 2002).
  • D. S. Jeong et al., Memristors for energy-efficient new computing paradigms. Adv. Electron. Mater. 2 (9), 1600090 (2016). DOI: 10.1002/aelm.201600090.
  • R. Hasan and T. M. Taha, Enabling back propagation training of memristor crossbar neuromorphic processors, presented at the 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China, 6–11 Jul. 2014.
  • P. M. P. Raj et al., Programming of memristive artificial synaptic crossbar network using PWM techniques. J. Circuit. Syst. Comp. 28, S0218126619502013 (2019). DOI: 10.1142/S0218126619502013.
  • N. Talati, S. Gupta, P. Mane, and S. Kvatinsky, Logic design within memristive memories using memristor-aided loGIC (MAGIC). IEEE Trans. Nanotechnol. 15 (4), 635 (2016). DOI: 10.1109/TNANO.2016.2570248.
  • S. Choi et al., Experimental demonstration of feature extraction and dimensionality reduction using memristor networks. Nano Lett. 17 (5), 3113 (2017). DOI: 10.1021/acs.nanolett.7b00552.
  • S. Kundu et al., Lead-free epitaxial ferroelectric material integration on semiconducting (100) Nb-doped SrTiO3 for low-power non-volatile memory and efficient ultraviolet ray detection. Sci. Rep. 5, 12415 (2015). DOI: 10.1038/srep12415.
  • C. Wang et al., Switchable diode effect and ferroelectric resistive switching in epitaxial BiFeO3 thin films. Appl. Phys. Lett. 98 (19), 192901 (2011). DOI: 10.1063/1.3589814.
  • S. Kundu et al., Integration of lead-free ferroelectric on HfO2/Si (100) for high performance non-volatile memory applications. Sci. Rep. 5, 8494 (2015). DOI: 10.1038/srep08494.
  • H. Y. Lee et al., Low power and high speed bipolar switching with a thin reactive Ti buffer layer in robust HfO2 based RRAM, presented at the 2008 IEEE International Electron Devices Meeting, San Francisco, CA, USA, 15–17 Dec. 2008.
  • M. Jerry et al., A ferroelectric field effect transistor based synaptic weight cell. J. Phys. D: Appl. Phys. 51 (43), 434001 (2018). DOI: 10.1088/1361-6463/aad6f8.
  • M.-K. Kim and J.-S. Lee, Ferroelectric analog synaptic transistors. Nano Lett. 19 (3), 2044 (2019). DOI: 10.1021/acs.nanolett.9b00180.
  • P. K. R. Boppidi et al., Unveiling the dual role of chemically synthesized copper doped zinc oxide for resistive switching applications. J. Appl. Phys. 124 (21), 214901 (2018). DOI: 10.1063/1.5052619.
  • S. Kvatinsky, M. Ramadan, E. G. Friedman, and A. Kolodny, VTEAM: a general model for voltage-controlled memristors. IEEE Trans. Circuits Syst. II Express. 62 (8), 786 (2015). DOI: 10.1109/TCSII.2015.2433536.
  • B. Suresh et al., Realizing STDP learning rule in Pt/Cu:ZnO/Nb:STO memristors for implementing single spike based denoising autoencoder. J. Micromech. Microeng. 29 (8) (2019). DOI: 10.1088/1361-6439/ab235f.
  • W. H. M. Wolberg, Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Natl. Acad. Sci. USA. 87 (23), 9193 (1990).
  • R. Berdan et al., A μ-controller-based system for interfacing selectorless RRAM crossbar arrays. IEEE Trans. Electron Devices 62 (7), 2190 (2015). DOI: 10.1109/TED.2015.243366.
  • T. D. Sanger, Optimal unsupervised learning in feedforward neural networks. AI Lab TR. 2, 459 (1989). DOI: 10.1016/0893-6080(89)90044-0.
  • E. Oja, Simplified neuron model as a principal component analyzer. J. Math. Biol. 15 (3), 267 (1982). DOI: 10.1007/BF00275687.
  • C. M. Bishop, Pattern Recognition and Machine Learning (New York: Springer, 2013).
  • M. Karabatak and M. C. Ince, An expert system for detection of breast cancer based on association rules and neural network. Expert Syst. Appl. 36 (2), 3465 (2009). DOI: 10.1016/j.eswa.2008.02.064.
  • A. Marcano-Cedeño, J. Quintanilla-Domínguez, and D. Andina, WBCD breast cancer database classification applying artificial metaplasticity neural network. Expert Syst. Appl. 38 (8), 9573 (2011). DOI: 10.1016/j.eswa.2011.01.167.
  • K. Polat and S. Güneş, Breast cancer diagnosis using least square support vector machine. Digit. Signal Process. 17 (4), 694 (2007). DOI: 10.1016/j.dsp.2006.10.008.
  • H. L. Chen, B. Yang, J. Liu, and D. Y. Liu, A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst. Appl. 38 (7), 9014 (2011). DOI: 10.1016/j.eswa.2011.01.120.
  • P. J. Denning, Thrashing: its causes and prevention, presented at the Proceedings of the AFIPS Fall Joint Computer Conference, 9–11 Dec. 1968 (ACM Press, New York, NY, USA), pp. 915–922. DOI: 10.1145/1476589.1476705.

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