International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Contact Us

IJSTR >> Volume 9 - Issue 1, January 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Intelligent Neural Network For Bacteria Classification: An Innovation In Artificial Neural Network

[Full Text]



Ananda Khamaru, Sunil Karforma, Soumendranath Chatterjee, Ishita Saha Raktima Bandyopadhyay



Medically important bacteria, INN, Cost function, SGD, SSE



The work focused on reliable outcome from next generation artificial neural network (ANN). ANN was efficiently used for decision making on labeled and unlabeled data but problem was that it was always generated as a result though the short input data. The conventional ANN model is being used in some financial sectors for prediction and analysis of financial data, but it would not make an outcome due to less applicable data. Our objective is to design a neural network which will have the intelligence by which it can generate most prominent decision. A mathematical model of new generation artificial neural network called Intelligent Neural Network (INN) has been proposed, which would solve that problem and would make the decision like a human. The INN model has been designed with two layers of fully connected neurons, where the first layer neurons has taken input as the features of bacteria and produced input for hidden neurons; and in the second layer the output from hidden neurons provided as input of decision neurons and the output of decision neurons was the expected result. This model was trained by back propagation process by reducing Sum Squared Error(SSE) through Stochastic Gradient Descent(SGD) technique. Prediction accuracy of this model was 97.11% to distinguish medically important bacteria. This study would help to laboratory users to identify medically important bacteria in an easy way.



[1] Tom Coughli,175 Zettabytes By 2025, https://www.forbes.com/sites/tomcoughlin/2018/11/27/175-zettabytes-by-2025/#6eb438a05459
[2] Larsen, Peter et al. (2015), Predicting Bacterial Community Assemblages Using an Artificial Neural Network Approach, Artificial Neural Networks, Methods in Molecular Biology, Springer Science, Vol-1260, Pages 33-43
[3] Yang Shen and Ad Bax (2015), Protein Structural Information Derived from NMR Chemical Shift with the Neural Network Program TALOS-N, Artificial Neural Networks, Methods in Molecular Biology, Springer Science, Vol-1260, Pages 17-32
[4] Jenkins et al (2017), Infectious Diseases, 4th Edition, Elsevier, Pages 1565-1578
[5] Meredith and Ulrich (2013), Retina, Fifth Edition, ScienceDirect, Pages 2019-2039
[6] Paterson (2012),Infections Due to Other Members of the Enterobacteriaceae, Including Management of Multidrug-Resistant Strains, 24th Edition, ELSEVIER, Volume 2, Pages 1874-1877
[7] Davis (2018), E.coli 0157:H7 infection early symptoms, treatment, and prevention, https://www.medicinenet.com/e_coli__0157h7/article.htm, Online
[8] McGrath (2017), Enterobacter aerogenes & Disease, https://healthyliving.azcentral.com/enterobacter- aerogenes-disease-12320900.html. Online
[9] Khan (2004), Meningitis due to Enterobacter aerogenes subsequent to resection of an acoustic ne uroma and abdominal fat graft to the mastoid,Brazilian Society of Infectious Diseases, vol.8 no.5 Salvador
[10] Amako et al. (1988), Fine Structures of the Capsules of Klebsiella pneumoniae and Escherichia coli K1, American Society for Microbiology Journals(JOURNAL OF BACTERIOLOGY), Vol. 170, No. 10, Pages 4960–4962
[11] Ashurst and Dawson(2019), Klebsiella Pneumonia, Stat Pearls, Online
[12] Niyogi (2005) , Shigellosis, The journal of microbiology, vol.- 43, pages133-143
[13] Keusch et al. (2011), Shigellosis, Third Edition, ELSEVIER, Chapter-18, Pages 137-144
[14] Patel and McCormick (2014), Mucosal Inflammatory Response to Salmonella typhimurium Infection, Front Immunol, vol. 5: 311
[15] Ashurst and Woodbury (2019), Salmonella Typhi, StatPearls , online
[16] Gart et al.(2016), Salmonella typhimurium and multidirectional communication in the gut, Front Microbiol, Vol. 7: 1827
[17] Bahashwan and Shafey (2013) , Antimicrobial resistance patterns of Proteus isolates from clinical specimens, September Edition, European Scientific Journal, vol. 9, no. 27
[18] Herter and Broeck (1911), A biochemical study of Proteus vulgaris, Journal Of Biological Chemistry, Vol. 9:491
[19] Braton et al. (2015),"Phenotyping and Genotyping Characterization of Proteus vulgaris After Biofield Treatment", Science Publishing Group,vol. 3(6), pages 66-73
[20] Colmer-Hamoodet al. (2016), Progress in Molecular Biology and Translational Science, Science Direct, Volume 142, Pages 151-191
[21] Weihai and Jin ( 2015), Molecular Medical Microbiology, Second Edition, Science Direct, Volume 2, Pages 753-767
[22] Cafasso (2016), Pseudomonas Infections, www.healthline.com/health/pseudomonas-infections, online
[23] Salehizadeh and Mohammadizad (2009), Microbial Enhanced Oil Recovery Using Biosurfactant Produced by Alcaligenes faecalis, Irian Journal for Biotechhnology, Article 3, Volume 7, Issue 4, Page 216-223
[24] Joo et al.(2007), Improvement in ammonium removal efficiency in wastewater treatment by mixed culture of Alcaligenes faecalis no. 4 and L1, J Biosci Bioeng, vol. 103(1):66-73.
[25] Licitra (2013), Etymologia: Staphylococcus, Centers for Disease Control and Prevention, Volume 19, Number 9, page- 1553
[26] Foster (1996), Medical Microbiology, 4th Edition, The University of Texas Medical Branch, Galveston (TX), Chapter 12 Staphylococcus
[27] Song et al. (2017), Erratum to: A review on Lactococcus lactis: from food to factory, Microbial Cell Factories, Vol. 16(1), 139
[28] Rakhashiya et al.(2015), Whole genome sequences and annotation of Micrococcus luteus SUBG006, a novel phytopathogen of mango, ELSEVIER,Volume 6, Pages 10-11
[29] Umadevi and Krishnaveni (2013), Antibacterial activity of pigment produced from Micrococcus luteus KF532949, ELSEVIER, Volume 4, Issue 3, Pages 149-152
[30] Cattani et al. (2000), Sepsis caused by Corynebacterium xerosis in neonatology: report of a clinic case, Acta Biomed Ateneo Parmense, vol. 71, 1:777-80
[31] Giovanni Gherardi(2016) , The Diverse Faces of Bacillus cereus (Bacillus cereus disease other than food-born poisonin), ELSEVIER, 2016, Pages 93-106
[32] Hassankashi(3 Apr 2019), Neural Network, www.codeproject.com, online
[33] Huang and Wu (December 2018), Novel neural network application for bacterial colony classification, Springer, Online
[34] Manzoor et al.(April 2014), Rapid identification and discrimination of bacterial strains by laser induced breakdown spectroscopy and neural networks, ELSEVIER, Volume 121, April 2014, Pages 65-70
[35] Carrillo and Durán (2019),Fast identification of Bacteria for Quality Control of Drinking Water through A Static Headspace Sampler Coupled to a Sensory Perception System, Biosensors. Vol. 9(1), 23
[36] Haykin (1999), Neural Networks A Comprehensive Foundation, Second Edition, Pearson, India, pages 178-266
[37] Panday and Simon (2015), Soft Computing with MATLAB Programming, First Edition, Oxford University Press, India, pages 118-152
[38] Cappuccino and Sherman (2009), Microbiology A Laboratory Manual, 7th Edition, Pearson, India, pages 1-528
[39] Georgountzos et al.(2018), Infective Endocarditis in a Young Adult due to Lactococcus lactis: A Case Report and Review of the Literature, Case Reports in Medicine, Volume 2018, Article ID 5091456, 4 pages.
[40] Cappuccino and Sherman (2014), Microbiology A Laboratory Manual, 10th Edition, Pearson, India, pages 216-217.
[41] Zieliński et al. (2017) Deep learning approach to bacterial colony classification. PLoS ONE 12(9): e0184554. https://doi.org/10.1371/journal.pone.0184554
[42] Huang, L. & Wu, T. Theor Biol Med Model (2018), BioMed Central, 15: 22. https://doi.org/10.1186/s12976-018-0093-x