Open Access
Issue
E3S Web Conf.
Volume 297, 2021
The 4th International Conference of Computer Science and Renewable Energies (ICCSRE'2021)
Article Number 01073
Number of page(s) 7
DOI https://doi.org/10.1051/e3sconf/202129701073
Published online 22 September 2021
  1. Cruz JA, Wishart, D.S. Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2007;2:59–77. (2007). [Google Scholar]
  2. A. Mandal, S. Dutta and S. Pramanik, “Machine Intelligence of Pi from Geometrical Figures with Variable Parameters using SCILab”, in Methodologies and Applications of Computational Statistics for Machine Intelligence, D. Samanta, R.R. Althar, S. Pramanik and S. Dutta, Eds, IGI Global, DOI: 10.4018/978-1-7998-7701-1.ch003. [Google Scholar]
  3. Y. Meslie, W. Enbeyle, B.K. Pandey, S. Pramanik, D. Pandey, P. Dadeech, A. Belay and A. Saini, “Machine Intelligence-Based Trend Analysis of COVID-19 for Total Daily Confirmed Cases in Asia and Africa”, in Methodologies and Applications of Computational Statistics for Machine Intelligence, D. Samanta, R.R. Althar, S. Pramanik and S. Dutta, Eds, IGI Global, DOI: 10.4018/978-1-7998-7701-1.ch009. [Google Scholar]
  4. Koscielny, S. Why most gene expression signatures of tumors have not been useful in the clinic. Sci Transl Med. (2010) Jan 13;2(14):14ps2. doi: 10.1126/scitranslmed.3000313. PMID: 20371465. [CrossRef] [PubMed] [Google Scholar]
  5. A. Bhattacharya, A. Ghosal, A.A. Obaid, S. Krit, V.K. Shukla, K. Mandal and S. Pramanik, “Unsupervised Summarization Approach with Computational Statistics of Microblog Data”, in Methodologies and Applications of Computational Statistics for Machine Intelligence, D. Samanta, R.R. Althar, S. Pramanik and S. Dutta, Eds, IGI Global, DOI: 10.4018/978-1-7998-7701-1.ch002, (2021). [Google Scholar]
  6. Cicchetti, D. Neural networks and diagnosis in the clinical laboratory: state of the art. Clin Chem (1992);38:9–10. [CrossRef] [PubMed] [Google Scholar]
  7. Cochran, A.J. Prediction of outcome for patients with cutaneous melanoma. Pigment Cell Res. (1997) Jun;10(3):162–167. doi: 10.1111/j.1600-0749.1997.tb00479.x. PMID: 9266604. [CrossRef] [PubMed] [Google Scholar]
  8. Exarchos K P, Goletsis Y, Fotiadis, D.I. Multiparametric decision support system for the prediction of oral cancer reoccurrence. IEEE Trans Inf Technol Biomed (2012);16: 1127–1134. [CrossRef] [PubMed] [Google Scholar]
  9. Kononenko, I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med. (2001) Aug;23(1):89–109. doi: 10.1016/s0933-3657(01)00077-x. PMID: 11470218. [CrossRef] [PubMed] [Google Scholar]
  10. Sun Y, Goodison S, Li J, Liu L, Farmerie, W. Improved breast cancer prognosis through the combination of clinical and genetic markers. Bioinformatics. (2007) Jan 1;23(1):30–37. doi: 10.1093/bioinformatics/btl543. Epub 2006 Nov. 6. PMID: 17130137; PMCID: PMC3431620. [CrossRef] [PubMed] [Google Scholar]
  11. Gilmore S, Hofmann-Wellenhof R, Soyer, H.P. A support vector machine for decision support in melanoma recognition. Exp Dermatol. (2010) Sep;19(9):830–835. doi: 10.1111/j.1600-0625.2010.01112.x. Epub 2010 Jul 11. PMID: 20629732. [CrossRef] [PubMed] [Google Scholar]
  12. Mac Parthalain N., Zwiggelaar R. (2010) Machine Learning Techniques and Mammographic Risk Assessment. In: Marti, J., Oliver, A., Freixenet, J., Marti, R. (eds) Digital Mammography. IWDM 2010. Lecture Notes in Computer Science, vol. 136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13666-5_90 [Google Scholar]
  13. S. Pramanik and S.K. Bandyopadhyay, “Hiding Secret Message in an Image”, International Journal of Innovative Science, Engineering and Technology, vol. 1 pp. 553–559, (2014). [Google Scholar]
  14. S. Pramanik and S.K. Bandyopadhyay, “Image Steganography Using Wavelet Transform and Genetic Algorithm”, International Journal of Innovative Research in Advanced Engineering, vol. 1 pp. 1–4, (2014). [Google Scholar]
  15. Howlader N, Noone AM, Krapcho M, Garshell J, Neyman N, Altekruse SF, Kosary CL, Yu M, Ruhl J, Tatalovich Z, Cho H, Mariotto A, Lewis DR, Chen HS, Feuer EJ, Cronin, K.A. (eds). SEER Cancer Statistics Review, 1975-2010, National Cancer Institute. Bethesda, MD, https://seer.cancer.gov/archive/csr/19752010/, based on November 2012 SEER data submission, posted to the SEER web site, April (2013). [Google Scholar]
  16. Chen YC, Ke WC, Chiu, H.W. Risk classification of cancer survival using ANN with gene expression data from multiple laboratories. Comput Biol Med. 2014 May;48:1–7. doi: 10.1016/j.compbiomed.2014.02.006. Epub (2014) Feb 22. PMID: 24631783. [CrossRef] [PubMed] [Google Scholar]
  17. Park K, Ali A, Kim D, An Y, Kim M, Shin, H. Robust predictive model for evaluating breast cancer survivability. Engl Appl Artif Intell (2013);26:2194–2205. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.