In-Silico Analysis of Chemical Compounds Cinnamomum verum for Antibacte
Abstract
Cinnamom or Cinnamomum verum is a natural substance that has been known as one of the spices,but later known as tradisional medicine. Cinnamon bark contains several antibacterial compounds,such as eugenol and cinnamaldehyde. Antibacteri are susbtances that can interfere with growth or even kill bacteria ny means of harmful microbial metabolism. This literature review aims to identify and analyze the trends, datasets, methods and frameworks used in the topic of attribute independence assumption assumptions on NB between 2010 and 2018. Based on the inclusion and exclusion criteria designed, it shows 71 study studies of attribute independence assumptions on the published NB between January 2010 and December 2018 are investigated in this literature review have been conducted as a review of systematic literature. A systematic literature review is defined as the process of identifying, assessing, and interpreting all available research evidence in order to provide answers to specific research questions.
References
J. Hernández-González, I. Inza, and J. A. Lozano, “Learning Bayesian network classifiers from label proportions,” Pattern Recognit., vol. 46, no. 12, pp. 3425–3440, 2013.
N. Friedman, D. Geiger, M. Goldszmidt, G. Provan, P. Langley, and P. Smyth, “Bayesian Network Classifiers *,” Mach. Learn., vol. 29, pp. 131–163, 1997.
L. Jiang, H. Zhang, and Z. Cai, “A novel bayes model: Hidden naive bayes,” IEEE Trans. Knowl. Data Eng., vol. 21, no. 10, pp. 1361–1371, 2009.
L. Jiang, H. Zhang, Z. Cai, and D. Wang, “Weighted average of one-dependence estimators,” J. Exp. Theor. Artif. Intell., vol. 24, no. 2, pp. 219–230, 2012.
P. harliana and R. Rahim, “Comparative Analysis of Membership Function on Mamdani Fuzzy Inference System for Decision Making,” J. Phys. Conf. Ser., vol. 930, no. 1, p. 012029, Dec. 2017.
R. Rahim et al., “Searching Process with Raita Algorithm and its Application,” J. Phys. Conf. Ser., vol. 1007, no. 1, p. 012004, Apr. 2018.
R. Rahim, A. S. Ahmar, A. P. Ardyanti, and D. Nofriansyah, “Visual Approach of Searching Process using Boyer-Moore Algorithm,” J. Phys. Conf. Ser., vol. 930, no. 1, p. 012001, Dec. 2017.
R. Rahim, S. Nurarif, M. Ramadhan, S. Aisyah, and W. Purba, “Comparison Searching Process of Linear, Binary and Interpolation Algorithm,” J. Phys. Conf. Ser., vol. 930, no. 1, p. 012007, Dec. 2017.
R. Rahim, Nurjamiyah, and A. R. Dewi, “Data Collision Prevention with Overflow Hashing Technique in Closed Hash Searching Process,” J. Phys. Conf. Ser., vol. 930, no. 1, p. 012012, Dec. 2017.
R. Rahim, D. Hartama, H. Nurdiyanto, A. S. Ahmar, D. Abdullah, and D. Napitupulu, “Keylogger Application to Monitoring Users Activity with Exact String Matching Algorithm,” J. Phys. Conf. Ser., vol. 954, no. 1, p. 012008, 2018.
C. Zhang, G.-R. Xue, Y. Yu, and H. Zha, “Web-scale classification with naive bayes,” Proc. 18th Int. Conf. World wide web - WWW ’09, p. 1083, 2009.
R. S. Wahono, “A Systematic Literature Review of Software Defect Prediction : Research Trends , Datasets , Methods and Frameworks,” J. Softw. Eng., vol. 1, no. 1, pp. 1–16, 2015.
C. Catal and B. Diri, “A systematic review of software fault prediction studies,” Expert Syst. Appl., vol. 36, no. 4, pp. 7346–7354, 2009.
A. S. Ahmar et al., “Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO),” J. Phys. Conf. Ser., vol. 954, no. 1, 2018.
B. Kitchenham and S. Charters, “Guidelines for performing Systematic Literature reviews in Software Engineering Version 2.3,” Engineering, vol. 45, no. 4ve, p. 1051, 2007.
D. Aha et al., “UCI Repository of Machine Learning Database,” 1987.
J. Wu, Z. Cai, S. Zeng, and X. Zhu, “Artificial immune system for attribute weighted Naive Bayes classification,” Proc. Int. Jt. Conf. Neural Networks, no. 61075063, 2013.
H. Zhang and S. Sheng, “Learning weighted naive bayes with accurate ranking,” Proc. - Fourth IEEE Int. Conf. Data Mining, ICDM 2004, pp. 567–570, 2004.
M. A. Hall, “uow-cs-wp-2000-08.pdf.” 2000.
P. Langley and S. Sage, “Induction of Selective Bayesian Classifiers,” Proc. Tenth Int. Conf. Uncertain. Artif. Intell., no. 1990, pp. 399–406, 1994.
M. A. Hall, “A decision tree-based attribute weighting filter for naive Bayes.pdf,” 2006.
M. Robnik-Siknja and I. Kononeko, “Theoretical and empirical analysis of RelifF and RReliefF,” Mach Learn, vol. 53, pp. 23–69, 2003.
Ö. F. Arar and K. Ayan, “A Feature Dependent Naive Bayes Approach and Its Application to the Software Defect Prediction Problem,” Appl. Soft Comput. J., 2017.
M. Borrotti, G. Minervini, D. De Lucrezia, and I. Poli, “Naïve Bayes ant colony optimization for designing high dimensional experiments,” Appl. Soft Comput. J., pp. 1–10, 2016.
T. Park and K. R. Ryu, “A dual-population genetic algorithm for adaptive diversity control,” IEEE Trans. Evol. Comput., vol. 14, no. 6, pp. 865–884, 2010.
Y. P. Huang, Y. T. Chang, S. L. Hsieh, and F. E. Sandnes, “An adaptive knowledge evolution strategy for finding near-optimal solutions of specific problems,” Expert Syst. Appl., vol. 38, no. 4, pp. 3806–3818, 2011.
J. Wu, S. Pan, X. Zhu, P. Zhang, and C. Zhang, “SODE: Self-Adaptive One-Dependence Estimators for classification,” Pattern Recognit., vol. 51, pp. 358–377, 2016.
J. Wu, S. Pan, X. Zhu, Z. Cai, P. Zhang, and C. Zhang, “Self-adaptive attribute weighting for Naive Bayes classification,” Expert Syst. Appl., vol. 42, no. 3, pp. 1487–1502, 2015.
J. Lakoumentas, J. Drakos, M. Karakantza, G. Sakellaropoulos, V. Megalooikonomou, and G. Nikiforidis, “Optimizations of the naïve-Bayes classifier for the prognosis of B-Chronic Lymphocytic Leukemia incorporating flow cytometry data,” Comput. Methods Programs Biomed., vol. 108, no. 1, pp. 158–167, 2012.
