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    . International Journal of Innovative Research in Information Security, 8 (3): 27-33 (August 2021)1. Abdulkadir Sengur, “An expert system based on linear Discriminant analysis and adaptive neuro-fuzzy inference system to diagnosis heart valve diseases”, ELSEVIER, Expert Systems with Applications 35, 214-222, 12 June 2007. 2. Jain Yu, “General C-Means Clustering Model”, IEEE Transactions On Pattern Analysis And Machine Intelugence, VOL. 27, NO. 8, August 2005. 3. Meyer-Baese, O. Lange, A. wismueller, and M. K. Hurdal, “Analysis of Dynamic Susceptibility constrast MRI Time Series Based on Unsupervised Clustering Methods”, IEEE Transactions on Information Technology in Biomedicine, VOL. 11, NO. 5, September 2007. 4. Shaikh Abdul Hannan, "Heart Disease Diagnosis by using FFBP and GRNN algorithm of Neural Network", International Journal of Computer Science and Information Security, Vol 12, Number 6, June 2014, ISSN 1945-5500, United States of America. 5. Fraser, H.S.F., et al., “Differential diagnoses of the heart disease program have better sensitivity than Resident Physicians”, Tufts-New England Medical Center, Boston, MA(2001). 6. Mohammad Eid Alzaharani, Shaikh Abdul Hannan, “Diagnosis and Medical Prescription of Heart Disease Using FFBP, SVM and RBF”, Issue,1, Vol 5, , KKU Journal of Basic and Applied Sciences, Mar 2019 , Page 6-15. 7. Frawley and Piatetsky-Shapiro, 1996. Knowledge Discovery in Databases: An Overview. The AAAI/MIT Press, Menlo Park, C.A. 8. Aqueel Ahmed, Shaikh Abdul Hannan, “Data Mining Techniques to Find Out Heart Diseases: An Overview”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), An ISO 9001:2008 Certified International Journal, Volume-1, Issue-4, September 2012, ISSN: 2278-3075, New Delhi, India. 9. I. Turkoglu, A. Arslan, E. Ilkay, “An expert system for diagnosis of the heart valve diseases”, Expert Systems with Applications vol.23, pp. 229–236, 2002. 10. I. Turkoglu, A. Arslan, E. Ilkay, “An intelligent system for diagnosis of heart valve diseases with wavelet packet neural networks”, Computer in Biology and Medicine vol.33 pp.319–331, 2003. 11. Haque ME, Sudhakar KV. ANN back propagation prediction model for fracture toughness in microalloy steel. Int J Fatique 2002;24:1003–10. 12. Shaikh Abdul Hannan, A.V. Mane, R. R. Manza and R. J. Ramteke, “Prediction of Heart Disease Medical Prescription Using Radial Basis Function", IEEE International Conference on Computational Intelligence and Computing Research at Tamilnadu College of Engineering, Coimbatore, Tamilnadu, India, ICCIC-2010, December 28-29, 2010. 13. P.I.J. Keeton, F.S. Schlindwein, Application of Wavelets in Doppler Ultrasound, vol. 17, number 1, MCB University Press, 1997, pp. 38–45. 14. I.A. Wright, N.A.J. Gough, F. Rakebrandt, M. Wahab, J.P. Woodcock, Neural network analysis of Doppler ultrasound blood low signals: a pilot study, Ultrasound in Medicine & Biology 23 (5) (1997) 683–690. 15. I. Guler, M.K. Kiymik, S. Kara, M.E. Yuksel, Application of autoregressive analysis to 20 MHz pulsed Doppler data in real time, International Journal Biomedical Computing 31 (3–4) (1992) 247–256. 16. Shaikh Abdul Hannan, "Heart Disease Diagnosis by using FFBP and GRNN algorithm of Neural Network", International Journal of Computer Science and Information Security, Vol 12, Number 6, June 2014, ISSN 1945-5500, United States of America. 17. Santosh K. Maher, Sumegh Tharewal, Abdul Hannan, “Review on HRV based Prediction and Detection of Heart Disease”, International Journal of Computer Applications (0975 – 8887), Pag 7-12, Volume 179 – No.46, June 2018. 18. https://www.who.int/en/news-room/fact sheets/detail/cardiovascular-diseases-(cvds) https://www.mayoclinic.org/diseases-conditions/stroke/symptoms-causes/syc-20350113. 19. Shaikh Abdul Hannan, “An Overview of Big Data and Hadoop”, International Journal of Computer Application”, Volume 154, Number 10, ISSN – 0975-887, November 2016, New York, USA. 20. Santosh Maher, Shaikh Abdul Hannan, Sumegh Tharewal, K. V. Kale " HRV based Human Heart Disease Prediction and Classification using Machine Learning " December 2019, (Vol. 17 No. 2 International Journal of Computer Science and Information SecApplicatiion (IJCA), New York, USA. 21. Akram Ablsubari, Shaikh Abdul Hannan, Mohammed Eid Alzaharani, Rakesh Ramteke, "Composite Feature Extraction and Classification for Fusion of Palmprint and Iris Biometric Traits", Engineering Technology and Applied Science Research, (ETASR) Volume 9, No 1, Feb 2019, ISSN: 2241-4487, Greece. 22. Yogesh Rajput, Shaikh Abdul Hannan, Mohammed Eid Alzaharani, D. Patil Ramesh Manza, Design and Development of New Algorithm for person identification Based on Iris statistical features and Retinal blood Vessels Bifurcation points” ” International Conference on Recent Trends in Image Processing & Pattern Recognition (RTIP2R), December 21-22, 2018, India. 23. Yogesh Rajput, Shaikh Abdul Hannan, “Design New Wavelet Filter for Detection and Grading of Non-proliferative Diabetic Retinopathy Lesions”, International Conference on Recent Trends in Image Processing and Pattern Recognition, Jan 2020, Springer, Singpore. 24. Y. M. Rajput, A. H. Hannan, M. E. Alzahrani, R. R. Manza, D. D. Patil, “EEG-Based Emotion Recognition Using Different Neural Network and Pattern Recognition Techniques–A Review”, International Journal of Computer Sciences and Engineering, Vol 6, Issue 9, Sep 2018. 25. Shaikh Abdul Hannan, Bharatratna P. Gaikwad, Ramesh Manza, "Brain Tumor from MRI Images : A Review". International Journal of Scientific and Engineering Research (IJSER), Volume 5, Issue 4, April-2014 ISSN 2229-5518, France. 26. Eliane Rich and KevinKnight – Artificial Intelligence – Secone Edition Mcgraw Hill , 1983. 27. Long, W., et.al., “Developing a program for Tracking Heart Failure”, MIT Lab for Computer Science, Cambridge, MA(2001). 28. Frase, H.S.F., et.al., “Comparing complex diagnoses a formative evalution of the heart disease program”, MIT Lab for Computer Science, Cambridge, MA(2001) 29. Shaikh Abdul Hannan, A.V. Mane, R. R. Manza and R. J. Ramteke, “Prediction of Heart Disease Medical Prescription Using Radial Basis Function", IEEE International Conference on Computational Intelligence and Computing Research at Tamilnadu College of Engineering, Coimbatore, Tamilnadu, India, ICCIC-2010, December 28-29, 2010. 30. J. Galindo, P. Tamayo, Credit risk assessment using statistical and machine learning: basic methodology and risk modeling applications, Computational Economics 15 (1 – 2) (2000) 107–143 31. V. Vapnik, The Nature of Statistical Learning Theory, Springer- Verlag, New York, 1995. 32. M.A. Hearst, S.T. Dumais, E. Osman, J. Platt, B. Scho¨lkopf, Support vector machines, IEEE Intelligent Systems 13 (4) (1998) 18– 28. 33. Broomhead D., & Lowe, D., Multivariable functional interpolation and adaptive networks. Complex Systems, vol.2, pp.321-355, 1988. 34. Haralambos Sarimveis, Philip Doganis, Alex Alexandridis, “A classification technique based on radial basis function neural networks”, Advances in Engineering Software vol.37, pp.218–221, 2006. 35. Shaikh Abdul Hannan, Pravin Yannawar, R.R. Manza and R.J. Ramteke, “Expert System Data Collection Technique for Heart Disease” , in IT & Business Intelligence, on 06-08 Nov 2009, Organised By IMT, Nagpur, India. 36. Haralambos Sarimveis, Philip Doganis, Alex Alexandridis, “A classification technique based on radial basis function neural networks”, Advances in Engineering Software vol.37, pp.218–221, 2006. 37. Chauvin in, Y and D.E. Ruumehart Backpropagation : Theory, Architechtures and Applications, Erbaum Mahwah, NJ, ISBN : 080581258. PP 561, 1995. 38. Shaikh Abdul Hannan, V. D. Bhagile, R.R. Manza, R. J. Ramteke, “Heart Disease Diagnosis By Using FFBP algorithm of Artificial Neural Network”, International Conference on Communication, Computation, Control and Nanotechnology, ICN-2010 Organized by Rural Engineering College Bhalki-585328, during October 29-30, 2010. 39. Cigizoglu HK, Alp M. Generalized regression neural network in modelling river sediment yield. Adv Eng Software 2005;37:63–8. 40. Kim B, Lee DW, Parka KY, Choi SR, Choi S. Prediction of plasma etching using a randomized generalized regression neural network. Vacuum 2004;76:37–43 41. Jang JSR, Sun CT, Mizutani E. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence, Prentice Hall, Upper Saddle River, New Jersey, USA; 1997 Chapter 9. 42. Shaikh Abdul Hannan, R. R. Manza and R.J. Ramteke, “Association Rules for Filtering The Medicine To Avoid Side Effects Of Heart Patients”, on 16 -19 Dec 2009, at Advances in Computer Vision and Information Technology – 09, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad. 43. Anupriya Kamble, Abdul Hannan, Yogesh, Dnyaneshwari, “Association detection of Regular Insulin and NPH Insulin Using Statistical Features”, Second International Conference on Cognitive Knowledge Engineering, 21-23 December 2016 (ICKE-2016), Aurangabad, Maharashtra, India, pp 59-62, ISBN 978-93-80876-89-4. 44. Shaikh Abdul Hannan, Pravin Yannawar, R. R. Manza and R.J. Ramteke, “Data Mining Technique for Detection of Cardiac Problems Using Symptoms Medicine and Its Side effects”, in IT & Business Intelligence -09 , in IT & Business Intelligence, on 06-08 Nov 2009, Organized By IMT, Nagpur, India. 45. Shaikh Abdul Hannan, Jameel Ahmed, Naveed Ahmed, Rizwan Alam Thakur, “Data Mining and Natural Language Processing Methods for Extracting Opinions from Customer Reviews”, International Journal of Computational Intelligence and Information Security, pp 52-58, Vol. 3, No. 6, July 2012. ( ISSN: 1837-7823). 46. Ordonez C,” Association rule discovery with the train and test approach for heart disease prediction”, IEEE Transactions on Information Technology in Biomedicine, P(334 – 343), April 2006 47. Aqueel Ahmed, Shaikh Abdul Hannan, “Data Mining Techniques to Find Out Heart Diseases: An Overview”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), An ISO 9001:2008 Certified International Journal, Volume-1, Issue-4, September 2012, ISSN: 2278-3075, New Delhi, India. 48. AbuKhousa, E “Predictive data mining to support clinical decisions: An overview of heart disease prediction systems”, IEEE Transaction on Innovations in Information Technology (IIT), pp(267 - 272) March 2012..
    a year ago by @ijiris
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    . IJIRIS:: International Journal of Innovative Research in Information Security, Volume V (Issue IV): 21-24 (April 2018)1. Gary Stoneburner, Alice Goguen, and Alexis Feringa, "Risk Management Guide for Information Technology Systems", Recommendations of the National Institute of Standards and Technology, Special Publication 800-30, pp.1-5, July 2002 2. Richard Kissel, Kevin Stine, Matthew Scholl, Hart Rossman, Jim Fahlsing, Jessica Gulick, "Security Considerations in the System Development Life Cycle ",October 2008, pp. 2-3 3. Ugur Aksu, Hadi Dilek, ˙Islam Tatlı, Kemal Bicakci,˙Ibrahim Dirik,Umut Demirezen, Tayfun Aykır,Ä Quantitative CVSS- Based Cyber Security Risk Assessment Methodology For IT Systems", 23-26 Oct. 2017,IEEE 4. Daniel Tse, Zehan Xie, Zhaolin Song,Äwareness of information security and its implications to legal and ethical issues in our daily life",10-13 Dec. 2017,IEEE 5. Ayesha M. Talha, Ibrahim Kamel, Zaher Al Aghbari, "Enhancing Confidentiality and Privacy of Outsourced Spatial Data", 2015 IEE9E 2nd International Conference on Cyber Security and Cloud Computing, 2015 6. 074747474.5ark Stamp’s, “Information Security Principle and Practice”,Vol-4, Wiley Interscience, pp. 386, 405, 2006.
    6 years ago by @ijiris
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    , , , , and . IJIRIS:: International Journal of Innovative Research Journal in Information Security, Volume IV (Issue XII): 01-07 (December 2017)1 Hugh A. Chipman, Edward I. George, and Robert E. McCulloch. “Bayesian CART Model Search.” Journal of the American Statistical Association, Vol. 93(443), pp 935–948, September 1998. 2 Sujata Garera, Niels Provos, Monica Chew, and Aviel D. Rubin. “A framework for detection and measurement of phishing attacks.” In Proceedings of the 2007 ACM workshop on Recurring malicious code - WORM ’07, page 1, 2007. 3 Abhishek Gattani, AnHai Doan, Digvijay S. Lamba, NikeshGarera, Mitul Tiwari, Xiaoyong Chai, Sanjib Das, Sri Subramaniam, AnandRajaraman, and VenkyHarinarayan. “Entity extraction, linking, classifica- tion, and tagging for social media.” Proceedings of the VLDB Endowment, Vol. 6(11), pp 1126–1137, August 2013. 4 David D. Lewis. Naive (Bayes) at forty: The independence assumption in information retrieval. pages 4–15. 1998. 5 Justin Ma, Lawrence K. Saul, Stefan Savage, and Geoffrey M. Voelker. “Learning to detect malicious URLs.” ACM Transactions on Intelligent Systems and Technology, Vol. 2(3), pp 1–24, April 2011. 6 FadiThabtah Maher Aburrous, M.A.Hossain, KeshavDahal. “Intelligent phishing detection system for e-banking using fuzzy data mining.” Expert Systems with Applications, Vol. 37(12), pp 7913–7921, Dec 2010. 7 AnkushMeshram and Christian Haas. “Anomaly Detection in Industrial. Networks using Machine Learning: A Roadmap.” In Machine Learning for Cyber Physical Systems, pages 65–72. Springer Berlin Heidelberg, Berlin, Heidelberg, 2017. 8 Xuequn Wang Nik Thompson,Tanya Jane McGill. “Security begins at home: Determinants of home computer and mobile device security behavior.” Computers & Security, Vol. 70, pp 376–391, Sep 2017. 9 Dan Steinberg and Phillip Colla. “CART: Classification and Regression Trees.” The Top Ten Algorithms in Data Mining, pp 179–201, 2009. 10 D. Teal. “Information security techniques including detection, interdiction and/or mitigation of memory injection attacks,” Google patents. Oct 2013. 11 Kurt Thomas, Chris Grier, Justin Ma, Vern Paxson, and Dawn Song. “Design and Evaluation of a Real-Time URL Spam Filtering Service.” In 2011 IEEE Symposium on Security and Privacy, pp 447–462. May 2011. 12 Sean Whalen, Nathaniel Boggs, and Salvatore J. Stolfo. “Model Aggregation for Distributed Content Anomaly Detection.” In Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop - AISec ’14, pp 61–71, New York, USA, 2014. ACM Press. 13 Ying Yang and Geoffrey I. Webb. “Discretization for Naive-Bayes learning: managing a discretization bias and variance.” Machine Learning, Vol. 74(1), pp 39–74, Jan 2009..
    7 years ago by @ijiris
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