International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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A Predictive Model to identify possible affected Bipolar disorder students using Naive Baye's, Random Forest and SVM machine learning techniques of data mining and Building a Sequential Deep Learning Model using Keras

  • Peerbasha, S. (P.G. & Research Department of Computer Science - Jamal Mohamed College (Autonomous), Affiliated to Bharathidasan University) ;
  • Surputheen, M. Mohamed (P.G. & Research Department of Computer Science - Jamal Mohamed College (Autonomous), Affiliated to Bharathidasan University)
  • Received : 2021.05.05
  • Published : 2021.05.30
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Abstract

Medical care practices include gathering a wide range of student data that are with manic episodes and depression which would assist the specialist with diagnosing a health condition of the students correctly. In this way, the instructors of the specific students will also identify those students and take care of them well. The data which we collected from the students could be straightforward indications seen by them. The artificial intelligence has been utilized with Naive Baye's classification, Random forest classification algorithm, SVM algorithm to characterize the datasets which we gathered to check whether the student is influenced by Bipolar illness or not. Performance analysis of the disease data for the algorithms used is calculated and compared. Also, a sequential deep learning model is builded using Keras. The consequences of the simulations show the efficacy of the grouping techniques on a dataset, just as the nature and complexity of the dataset utilized.

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References

  1. Renjit JA, Shanmuganathan KL, "Distributed and helpful multi-specialist based interruption discovery framework", Indian J Sci Technology 3(10): 2010, pp. 1070-1074 https://doi.org/10.17485/ijst/2010/v3i10.2
  2. Priyadarshini R, Dash N, Mishra R, "An epic way to deal with anticipate diabetes mellitus utilizing changed limit learning machine". In: International Conference on Electronics and Communication Systems (ICECS), 2014, pp 1-5.
  3. Sankaranarayanan S, Perumal TP, "Diabetic guess through information mining techniques and techniques". In: International Conference on Intelligent Computing Applications, 2014, pp 162-166
  4. Dahiwade D, Patle G, Meshram E, "Designing sickness forecast model utilizing AI approach.", In: Third IEEE International Conference on Computing Methodologies and Communication (ICCMC), 2019.
  5. Geetha R, Sivasubramanian S, Kaliappan M et al., "Cervical disease distinguishing proof with engineered minority oversampling strategy and PCA investigation utilizing irregular woodland classifer", J Med Syst 43:286. https://doi.org/10.1007/s10916-019-1402-6
  6. Annamalai S, Udendhran R, Vimal S, "A wise framework network dependent on distributed computing infra structures". Nov Pract Trends Grid Cloud Computing. https://doi.org/10.4018/978-1-5225-9023-1.ch005
  7. Wu H, Yang S, Huang Z, He J, Wang X (2018) Type 2 diabetes mellitus forecast model dependent on information mining. Advise Med Unlocked 10:100-1075218 V. Jackins et al. 1 3
  8. Sarwar A and Sharma, "Intelligent Naive Bayes way to deal with analyze diabetes type-2", In: Special Issue of International Journal of Computer Applications on Issues and Challenges in Networking, Intelligence and Computing Technologies, November 2012.
  9. Pradeepa S, Manjula KR, Vimal S et al (2020) DRFS: "Recognizing hazard factor of stroke infection from online media utilizing AI strategies", Neural Process Lett. https://doi.org/10.1007/s11063-020-10279-8.
  10. Kalaiselvi C, Nasira GM (2014) another methodology of conclusion of diabetes and expectation of malignancy utilizing ANFIS. In: IEEE Computing and Communicating Technologies, 2014, pp 188-190.
  11. Robinson YH, Vimal S, Khari M, Hernandez FCL, Crespo RG, "Tree-based convolutional neural organizations for object classifcation in portioned satellite pictures", Int J High Perform Comput Appl. https ://doi.org/10.1177/1094342020945026.
  12. Undre P, Kaur H, Patil P, "Improvement in expectation rate and precision of diabetic determination framework utilizing fluffy rationale mixture blend", In: International Conference on Pervasive Computing (ICPC), 2015, pp 1-4
  13. Yi Y, Wu J, Xu W, "Incremental SVM dependent on saved set for network interruption recognition", Else vier J Expert Syst Appl 38(6):7698-7707 https://doi.org/10.1016/j.eswa.2010.12.141
  14. Ramamurthy M, Krishnamurthi I, Vimal S, Harold Y, "Robinson profound learning based genome examination and NGS-RNA LL identification with a novel crossover model", 2020, 197: 104211. https://doi.org/https ://doi.org/10.1016/j.biosystems.2020.104211
  15. Pradeepa S, Gayathri P, Nishmitha P, Vimal S, Oh-Young S, Usman T, Raheel N (2020) IoT based wellbeing related point acknowledgment from arising on the web wellbeing local area: drug help utilizing AI method. Gadgets 9(9):1469.
  16. Babu S, Vivek EM, Famina KP, Fida K, AswathiP, Shanid M, Hena M (2017) Heart sickness determination utilizing information mining procedure. In: International Conference on Electronics, Communication, and Aero space Technology, ICECA2017
  17. Sampaul TGA, Robinson YH, Julie EG, Shanmuganathan V, Nam Y, Rho S, "Diabetic retin opathy diagnostics from retinal pictures dependent on profound convolution networks", Preprints. https://doi.organization/10.20944/preprints20, 2005. 0493.v1
  18. Vimal S et al, "Deep learning-based dynamic with WoT for brilliant city advancement", In: Jain A, Crespo R, Khari M (eds) Smart development of web of things, CRC Press, Boca Raton, 2020, pp 51-62. , https://doi.org/10.1201/9780429298462
  19. Kumari M, Vohra R, Arora A, "Prediction of diabetes utilizing Bayesian organization", Int J Comput Sci Inf Technol (IJCSIT) 5(4):2014, 5174-5178
  20. Krishnaiah V, Narsimha G, Chandra NS, "Diagnosis of cellular breakdown in the lungs expectation framework utilizing information mining classification strategies", Int J Comput Sci Inf Technol 4(1): 2013, 39-45
  21. C. Li Sa, D. H. b. Abang Ibrahim, E. Dahliana Hossain and M. canister Hossin, "Understudy execution investigation framework (SPAS)," The fifth International Conference on Information and Communication Technology for The Muslim World (ICT4M), Kuching, 2014, pp. 1-6.
  22. G. Barata, S. Gama, J. Jorge and D. Goncalves, "Early Prediction of Student Profiles Based on Performance and Gaming Preferences," in IEEE Transactions on Learning Technologies, vol. 9, no. 3, 2015, pp. 272-284. https://doi.org/10.1109/TLT.2016.2541664
  23. Singh, A. S. Sabitha and A. Bansal, "Understudy execution investigation utilizing a bunching calculation," 2016 sixth International Conference - Cloud System and Big Data Engineering (Confluence), Noida, 2016, pp. 294-299.
  24. Duru, G. Dogan and B. Diri, "An outline of learns about understudies' presentation investigation and learning examination in MOOCs," 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, 2016, pp. 1719-1723.
  25. M. Agaoglu, "Anticipating Instructor Performance Using Data Mining Techniques in Higher Education," in IEEE Access, vol. 4, 2016, pp. 2379-2387. https://doi.org/10.1109/ACCESS.2016.2568756