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Year 2021, Volume: 4 Issue: 1, 11 - 18, 03.01.2022

Alla Saad Hisham Mohammed

Abstract

References

  • K. Abbo and M. S Jaborry, Learning rate for the back propagation algorithm based on modified scant equation, Iraqi J. Stat. Sci., 14(26) 2014, 1–11.
  • Y. A. Laylani, K. K. Abbo, and H. M. Khudhur, Training feed forward neural network with modified Fletcher-Reeves method, Journal of Multidisciplinary Modeling and Optimization, 1(1) 2018, 14–22.
  • A. Antoniou and W.-S. Lu, Practical Optimization: Algorithms and Engineering Applications. Springer Science & Business Media, 2007.
  • J. Nocedal and S. Wright, Numerical Optimization. Springer Science & Business Media, 2006.
  • E. Polak and G. Ribiere, “Note sur la convergence de méthodes de directions conjuguées,” ESAIM Math. Model. Numer. Anal. Mathématique Anal. Numérique, 3(R1) 1969, 35-43.
  • M. R. Hestenes and E. Stiefel, Methods of conjugate gradients for solving linear systems, J. Res. Nat. Bur. Stand.,49 (1) 1952, 409-436.
  • R. Fletcher and C. M. Reeves, Function minimization by conjugate gradients, Comput. J., 7(2) 1964, 149-154.
  • Y. H. Dai and Y. Yuan, A nonlinear conjugate gradient method with a strong global convergence property, SIAM J. Optim., 10(1) 1999, 177-182.
  • L. C. W. Dixon, Conjugate gradient algorithms: quadratic termination without linear searches, IMA J. Appl. Math., 15(1) 1975, 9-18.
  • K. K. Abbo and H. M. Khudhur, New A hybrid conjugate gradient Fletcher-Reeves and Polak-Ribiere algorithm for unconstrained optimization, Tikrit J. Pure Sci., 21(1) 2015, 124-129.
  • H. N. Jabbar, K. K. Abbo, and H. M. Khudhur, “Four--term conjugate gradient (CG) method based on pure conjugacy condition for unconstrained optimization,” Kirkuk Univ. J. Sci. Stud., 13(2) 2018, 101–113.
  • K. K. Abbo and H. M. Khudhur, New A hybrid Hestenes-Stiefel and Dai-Yuan conjugate gradient algorithms for unconstrained optimization, Tikrit J. Pure Sci., 21(1) 2015, 118–123.
  • Z. M. Abdullah, M. Hameed, M. K. Hisham, and M. A. Khaleel, Modified new conjugate gradient method for Unconstrained Optimization, Tikrit J. Pure Sci., 24(5) 2019, 86–90.
  • B. Y. Al-Khayat, Introduction to Mathematical Modeling Using MATLAB, Dar Ibn Al-Atheer for Printing and Publication University of Mosul, Mosul, 2012.

Developing a New Optimization Algorithm to Predict the Risk of Car Accidents Due to Drinking Alcoholic Drinks by Using Feed-Forward Artificial Neural Networks

Year 2021, Volume: 4 Issue: 1, 11 - 18, 03.01.2022

Alla Saad Hisham Mohammed

Abstract

In this research, we have developed a new algorithm in the field of optimiza-tion and its application in teaching artificial neural networks with front feeding to predict the risk of car accidents due to consuming alcoholic beverages, and the algorithm has proven a high efficiency in prediction as it was compared with the results of the model predicting the risk of car accidents due to eating Given alco-hol and the results were very close to the true solution to the model

Keywords

Artificial Neural Networks Conjugate Gradient Machine Learning Mathematical Modelling Optimization

References

  • K. Abbo and M. S Jaborry, Learning rate for the back propagation algorithm based on modified scant equation, Iraqi J. Stat. Sci., 14(26) 2014, 1–11.
  • Y. A. Laylani, K. K. Abbo, and H. M. Khudhur, Training feed forward neural network with modified Fletcher-Reeves method, Journal of Multidisciplinary Modeling and Optimization, 1(1) 2018, 14–22.
  • A. Antoniou and W.-S. Lu, Practical Optimization: Algorithms and Engineering Applications. Springer Science & Business Media, 2007.
  • J. Nocedal and S. Wright, Numerical Optimization. Springer Science & Business Media, 2006.
  • E. Polak and G. Ribiere, “Note sur la convergence de méthodes de directions conjuguées,” ESAIM Math. Model. Numer. Anal. Mathématique Anal. Numérique, 3(R1) 1969, 35-43.
  • M. R. Hestenes and E. Stiefel, Methods of conjugate gradients for solving linear systems, J. Res. Nat. Bur. Stand.,49 (1) 1952, 409-436.
  • R. Fletcher and C. M. Reeves, Function minimization by conjugate gradients, Comput. J., 7(2) 1964, 149-154.
  • Y. H. Dai and Y. Yuan, A nonlinear conjugate gradient method with a strong global convergence property, SIAM J. Optim., 10(1) 1999, 177-182.
  • L. C. W. Dixon, Conjugate gradient algorithms: quadratic termination without linear searches, IMA J. Appl. Math., 15(1) 1975, 9-18.
  • K. K. Abbo and H. M. Khudhur, New A hybrid conjugate gradient Fletcher-Reeves and Polak-Ribiere algorithm for unconstrained optimization, Tikrit J. Pure Sci., 21(1) 2015, 124-129.
  • H. N. Jabbar, K. K. Abbo, and H. M. Khudhur, “Four--term conjugate gradient (CG) method based on pure conjugacy condition for unconstrained optimization,” Kirkuk Univ. J. Sci. Stud., 13(2) 2018, 101–113.
  • K. K. Abbo and H. M. Khudhur, New A hybrid Hestenes-Stiefel and Dai-Yuan conjugate gradient algorithms for unconstrained optimization, Tikrit J. Pure Sci., 21(1) 2015, 118–123.
  • Z. M. Abdullah, M. Hameed, M. K. Hisham, and M. A. Khaleel, Modified new conjugate gradient method for Unconstrained Optimization, Tikrit J. Pure Sci., 24(5) 2019, 86–90.
  • B. Y. Al-Khayat, Introduction to Mathematical Modeling Using MATLAB, Dar Ibn Al-Atheer for Printing and Publication University of Mosul, Mosul, 2012.
There are 14 citations in total.

Details

Primary Language English
Subjects Mathematical Sciences
Journal Section Articles
Authors

Alla Saad 0000-0002-4191-1754

Hisham Mohammed 0000-0001-7572-9283

Publication Date January 3, 2022
Published in Issue Year 2021 Volume: 4 Issue: 1

Cite

APA Saad, A., & Mohammed, H. (2022). Developing a New Optimization Algorithm to Predict the Risk of Car Accidents Due to Drinking Alcoholic Drinks by Using Feed-Forward Artificial Neural Networks. Journal of Multidisciplinary Modeling and Optimization, 4(1), 11-18.
AMA Saad A, Mohammed H. Developing a New Optimization Algorithm to Predict the Risk of Car Accidents Due to Drinking Alcoholic Drinks by Using Feed-Forward Artificial Neural Networks. jmmo. January 2022;4(1):11-18.
Chicago Saad, Alla, and Hisham Mohammed. “Developing a New Optimization Algorithm to Predict the Risk of Car Accidents Due to Drinking Alcoholic Drinks by Using Feed-Forward Artificial Neural Networks”. Journal of Multidisciplinary Modeling and Optimization 4, no. 1 (January 2022): 11-18.
EndNote Saad A, Mohammed H (January 1, 2022) Developing a New Optimization Algorithm to Predict the Risk of Car Accidents Due to Drinking Alcoholic Drinks by Using Feed-Forward Artificial Neural Networks. Journal of Multidisciplinary Modeling and Optimization 4 1 11–18.
IEEE A. Saad and H. Mohammed, “Developing a New Optimization Algorithm to Predict the Risk of Car Accidents Due to Drinking Alcoholic Drinks by Using Feed-Forward Artificial Neural Networks”, jmmo, vol. 4, no. 1, pp. 11–18, 2022.
ISNAD Saad, Alla - Mohammed, Hisham. “Developing a New Optimization Algorithm to Predict the Risk of Car Accidents Due to Drinking Alcoholic Drinks by Using Feed-Forward Artificial Neural Networks”. Journal of Multidisciplinary Modeling and Optimization 4/1 (January 2022), 11-18.
JAMA Saad A, Mohammed H. Developing a New Optimization Algorithm to Predict the Risk of Car Accidents Due to Drinking Alcoholic Drinks by Using Feed-Forward Artificial Neural Networks. jmmo. 2022;4:11–18.
MLA Saad, Alla and Hisham Mohammed. “Developing a New Optimization Algorithm to Predict the Risk of Car Accidents Due to Drinking Alcoholic Drinks by Using Feed-Forward Artificial Neural Networks”. Journal of Multidisciplinary Modeling and Optimization, vol. 4, no. 1, 2022, pp. 11-18.
Vancouver Saad A, Mohammed H. Developing a New Optimization Algorithm to Predict the Risk of Car Accidents Due to Drinking Alcoholic Drinks by Using Feed-Forward Artificial Neural Networks. jmmo. 2022;4(1):11-8.
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