• Previous Article
    A novel hybrid AGWO-PSO algorithm in mitigation of power network oscillations with STATCOM
  • NACO Home
  • This Issue
  • Next Article
    Global and regional constrained controllability for distributed parabolic linear systems: RHUM approach
December  2021, 11(4): 567-578. doi: 10.3934/naco.2020056

Individual biometrics pattern based artificial image analysis techniques

Department of Computer Sciences, College of Education for Pure Sciences, University of Mosul, Mosul, Iraq

* Corresponding author: Israa Mohammed Khudher

Received  January 2020 Revised  October 2020 Published  December 2021 Early access  March 2021

Biometric characteristics have been used since antiquated decades, particularly in the detection of crimes and investigations. The rapid development in image processing made great progress in biometric features recognition that is used in all life directions, especially when these features recognition is constructed as a computer system. The target of this research is to set up a left foot biometric system by hybridization between image processing and artificial bee colony (ABC) for feature choice that is addressed within artificial image processing. The algorithm is new because of the rare availability of hybridization algorithms in the literature of footprint recognition with the artificial bee colony assessment. The suggested system is tested on a live-captured ninety colored footprint images that composed the visual database. Then the constructed database was classified into nine clusters and normalized to be used at the advanced stages. Features database is constructed from the visual database off-line. The system starts with a comparison operation between the foot-tip image features extracted on-line and the visual database features. The outcome from this process is either a reject or an acceptance message. The results of the proposed work reflect the accuracy and integrity of the output. That is affected by the perfect choice of features as well as the use of artificial bee colony and data clustering which decreased the complexity and later raised the recognition rate to 100%. Our outcomes show the precision of our proposed procedures over others' methods in the field of biometric acknowledgment.

Citation: Israa Mohammed Khudher, Yahya Ismail Ibrahim, Suhaib Abduljabbar Altamir. Individual biometrics pattern based artificial image analysis techniques. Numerical Algebra, Control & Optimization, 2021, 11 (4) : 567-578. doi: 10.3934/naco.2020056
References:
[1]

M. M. A. AbuqadumahM. A. M. AliA. A. Almisreb and B. Durakovic, Transfer learning for human identification based on footprint: a comparative study, Periodicals of Engineering and Natural Science, 7 (2019), 1300-1307.   Google Scholar

[2]

V. D. Ambeth Kumar and M. Ramakrishnan, Footprint recognition using modified sequential haar energy transform (MSHET), IJCSI International Journal of Computer Science, 7 (2010). Google Scholar

[3]

V. Bachu and J. Anuradha, A review of feature selection and its methods, Cybernetics and Information Technologies, 19 (2019), 1-26.  doi: 10.2478/cait-2019-0001.  Google Scholar

[4]

F. Baji and M. Mocanu, Chain code approach for shape-based image retrieval, Indian Journal of Science and Technology, 11 (2018), 1-17.   Google Scholar

[5]

J. C. BansalH. Sharma and S. S. Jadon, Artificial bee colony algorithm: a survey, International Journal Advanced Intelligence Paradigms, 5 (2013), 123-159.   Google Scholar

[6]

M. A. Bin-Basir and F. Binti-Ahmad, Comparison on swarm algorithms for feature selections/reductions, International Journal of Scientific and Engineering Research, 5 (2014), 479-486.   Google Scholar

[7]

M. Boelkins, D. Austen and S. Schlicker, Active Calculus 2.0, 2$^{nd}$ edition, Grand Valley State University Libraries Publisher, USA, 2017. Google Scholar

[8]

C. Chidambaram and H. S. Lopes, An improved artificial bee colony algorithm for the object recognition problem in complex digital images using template matching, International Journal of Natural Computing Research, 1 (2010), 54-70.   Google Scholar

[9]

E. Cuevas, F. Sencin-Echauri, D. Zaldivar and M. Prez, Image Segmentation Using Artificial Bee Colony Optimization, 1$^{st}$ edition, Springer-Verlag, Berlin Heidelberg, 2013. Google Scholar

[10]

T. DavidoviD. Teodorovi and M. Selmic, Bee colony optimization part I: The algorithm overview, Yugoslav Journal of Operations Research, 25 (2015), 33-56.  doi: 10.2298/YJOR131011017D.  Google Scholar

[11]

M. U. Farooq, Q. Salman, M. Arshad, I. Khan, R. Akhtar and S. Kim, An artificial bee colony algorithm based on a multi-objective framework for supplier integration, Applied Science, 9 (2019). Google Scholar

[12]

L. GhoualmiA. Draa and S. Chikhi, An ear biometric system based on artificial bee and the scale invariant feature transform, Expert Systems with Applications, 57 (2016), 49-61.   Google Scholar

[13]

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2$^{nd}$ edition, Prentice-Hall, New Jersey, USA, 2002. Google Scholar

[14]

V. Govindaraju, Z. Shi and J. Schneider, Feature Extraction Using a Chain Coded Contour Representation of Fingerprint Images, Audio- and Video-Based Biometric Person Authentication, 2$^{nd}$ edition, Springer-Verlag Berlin Heidelberg, 2003. Google Scholar

[15]

Y. I. Ibrahim and I. M. Alhamdani, A hybrid technique for human footprint recognition, International Journal of Electrical and Computer Engineering (IJECE), 9 (2019), 4060-4068.   Google Scholar

[16]

S. O. Kamel and M. Nour, Feature selection methods for predicting the popularity of online news: comparative study, and a proposed method, Journal of Theoretical and Applied Information Technology, 96 (2018), 6969-6980.   Google Scholar

[17]

D. KarabogaB. GorkemliC. Ozturk and N. Karaboga, A comprehensive survey: artificial bee colony (abc) algorithm and applications, Artificial Intelligence Review, 42 (2014), 21-57.   Google Scholar

[18]

R. Khokher and R. Chandra Sin, Footprint-based personal recognition using dactyloscopy technique, Industrial Mathematics and Complex Systems, 1 (2017), 207-219.   Google Scholar

[19]

I. M. Khudher and Y. I. Ibrahim, Swarm intelligent hybridization biometric, Indonesian Journal of Electrical Engineering and Computer Science, 18 (2020), 385-395.   Google Scholar

[20]

S. KothuriP. Annapurna and S. Lukka, Digit recognition using freeman chain code, International Journal of Application or Innovation in Engineering and Management (IJAIEM), 2 (2013), 362-365.   Google Scholar

[21]

K. S. KumarK. Ventakalakshmi and K. Karthikeyan, Threshold based lung image segmentation with robust artificial bee colony algorithm optimization technique, Asian Journal of Information Technology, 15 (2016), 4426-4430.   Google Scholar

[22]

A. KumarD. Kumar and S. K. Jarial, A review on artificial bee colony algorithms and their applications to data clustering, Cybernetics and Information Technologies, 17 (2017), 3-28.  doi: 10.1515/cait-2017-0027.  Google Scholar

[23]

S. B. Mirza and A. A. Waoo, A comprehensive study in wireless sensor network (wsn) using artificial bee colony (abc) algorithms, Journal, 6 (2019), 873-879.   Google Scholar

[24]

K. K. Nagwanshi, Cyber-forensic review of human footprint and gait for personal identification, IAENG International Journal of Computer Science, 46 (2019), 1-17.   Google Scholar

[25]

K. K. Nagwanshi and S. Dubey, Biometric authentication using human footprint, International Journal of Applied Information Systems, 3 (2012), 1-6.   Google Scholar

[26]

K. K. Nagwanshi and S. Dubey, Mathematical modeling of footprint based biometric recognition, International of mathematical trends and technology (IJMIT), 54 (2018), 49-61.   Google Scholar

[27]

V. R. Naramala and B Raveendrababu, Combined histogram chain code feature extraction method to recognize handwritten digits with the probabilistic neural network, International Journal of Applied Engineering Research, 9 (2014), 4585-4589.   Google Scholar

[28]

S. Nebti and A. Boukerram, Handwritten digits recognition based on swarm optimization methods, Networked Digital Technologies. Communications in Computer and Information Science, 87 (2010), 45-54.   Google Scholar

[29]

M. OliveiraD. PinheiroM. MacedoC. Bastos-Filho and R. Menezes, Uncovering the social interaction in swarm intelligence with network science, Applied Network Science, 1 (2018), 1-23.   Google Scholar

[30]

M. W. Powers, Evaluation: from precision, recall, and f-factor to ROC, informedness, markedness and correlation, Journal of Machine Learning Technologies, 2 (2007), 37-63.   Google Scholar

[31]

K. R. Raji and W. Xiaopeng, Study of biometric identification method based on naked footprint, International Journal of Science and Engineering, 5 (2013), 29-35.   Google Scholar

[32]

W. RongW. Hong and N. Yang, The research on footprint recognition method based on wavelet and fuzzy neural network, Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems, 3 (2009), 428-432.   Google Scholar

[33]

N. RusdiZ. R. YahyaN. Roslan and W. Z. Azman, Reconstruction of medical images using artificial bee colony algorithm, Mathematical Problems in Engineering, 2 (2018), 1-7.   Google Scholar

[34]

A. M. SalemA. A. Sewisy and U. A. Elyan, A vertex chain code approach for image recognition, International Journal on Graphics, Vision and Image Processing, 5 (2005), 1-8.   Google Scholar

[35]

M. Shokouhifar and G. S. Abkenar, An artificial bee colony optimization for mri fuzzy segmentation of brain tissue, International Conference on Management and Artificial Intelligence, 6 (2011). Google Scholar

[36]

E. B. Tirkolaee, A. Gol and G. W. Weber, Fuzzy mathematical programming and self-adaptive artificial fish swarm algorithm for just-in-time energy-aware flow shop scheduling problem with outsourcing option, IEEE Transactions on Fuzzy Systems, 1 (2020). Google Scholar

[37]

E. B. TirkolaeeM. AlinaghianA. A. RahmanimM. B. Sasia and A. K. Sangaiahc, An improved ant colony optimization for the multi-trip capacitated arc routing problem, Computers and Electrical Engineering Journal, 77 (2019), 457-470.   Google Scholar

[38]

E. B. TirkolaeeI. MahdaviM. M. Esfahani and G. W. Weber, A hybrid augmented ant colony optimization for the multi-trip capacitated arc routing problem under fuzzy demands for urban solid waste management, Waste Management and Research: The Journal for a Sustainable Circular Economy, 38 (2020), 156-172.   Google Scholar

[39]

U. UludagS. PankantiS. Prabhakar and A. Jain, Biometric Cryptosystems: issues and challenges, Proceedings of the IEEE, 92 (2004), 948-960.   Google Scholar

[40]

L. YangX. SunL. PengJ. Shao and T. Chi, An improved artificial bee colony algorithm for optimal land-use allocation, International Journal of Geographical Information Science, 26 (2015), 1470-1489.   Google Scholar

[41]

T. ZhangB. DingX. Zhao and Q. Yue, A fast feature selection algorithm based on swarm intelligence in acoustic defect detection, IEEE Access, 6 (2018), 28848-28858.   Google Scholar

show all references

References:
[1]

M. M. A. AbuqadumahM. A. M. AliA. A. Almisreb and B. Durakovic, Transfer learning for human identification based on footprint: a comparative study, Periodicals of Engineering and Natural Science, 7 (2019), 1300-1307.   Google Scholar

[2]

V. D. Ambeth Kumar and M. Ramakrishnan, Footprint recognition using modified sequential haar energy transform (MSHET), IJCSI International Journal of Computer Science, 7 (2010). Google Scholar

[3]

V. Bachu and J. Anuradha, A review of feature selection and its methods, Cybernetics and Information Technologies, 19 (2019), 1-26.  doi: 10.2478/cait-2019-0001.  Google Scholar

[4]

F. Baji and M. Mocanu, Chain code approach for shape-based image retrieval, Indian Journal of Science and Technology, 11 (2018), 1-17.   Google Scholar

[5]

J. C. BansalH. Sharma and S. S. Jadon, Artificial bee colony algorithm: a survey, International Journal Advanced Intelligence Paradigms, 5 (2013), 123-159.   Google Scholar

[6]

M. A. Bin-Basir and F. Binti-Ahmad, Comparison on swarm algorithms for feature selections/reductions, International Journal of Scientific and Engineering Research, 5 (2014), 479-486.   Google Scholar

[7]

M. Boelkins, D. Austen and S. Schlicker, Active Calculus 2.0, 2$^{nd}$ edition, Grand Valley State University Libraries Publisher, USA, 2017. Google Scholar

[8]

C. Chidambaram and H. S. Lopes, An improved artificial bee colony algorithm for the object recognition problem in complex digital images using template matching, International Journal of Natural Computing Research, 1 (2010), 54-70.   Google Scholar

[9]

E. Cuevas, F. Sencin-Echauri, D. Zaldivar and M. Prez, Image Segmentation Using Artificial Bee Colony Optimization, 1$^{st}$ edition, Springer-Verlag, Berlin Heidelberg, 2013. Google Scholar

[10]

T. DavidoviD. Teodorovi and M. Selmic, Bee colony optimization part I: The algorithm overview, Yugoslav Journal of Operations Research, 25 (2015), 33-56.  doi: 10.2298/YJOR131011017D.  Google Scholar

[11]

M. U. Farooq, Q. Salman, M. Arshad, I. Khan, R. Akhtar and S. Kim, An artificial bee colony algorithm based on a multi-objective framework for supplier integration, Applied Science, 9 (2019). Google Scholar

[12]

L. GhoualmiA. Draa and S. Chikhi, An ear biometric system based on artificial bee and the scale invariant feature transform, Expert Systems with Applications, 57 (2016), 49-61.   Google Scholar

[13]

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2$^{nd}$ edition, Prentice-Hall, New Jersey, USA, 2002. Google Scholar

[14]

V. Govindaraju, Z. Shi and J. Schneider, Feature Extraction Using a Chain Coded Contour Representation of Fingerprint Images, Audio- and Video-Based Biometric Person Authentication, 2$^{nd}$ edition, Springer-Verlag Berlin Heidelberg, 2003. Google Scholar

[15]

Y. I. Ibrahim and I. M. Alhamdani, A hybrid technique for human footprint recognition, International Journal of Electrical and Computer Engineering (IJECE), 9 (2019), 4060-4068.   Google Scholar

[16]

S. O. Kamel and M. Nour, Feature selection methods for predicting the popularity of online news: comparative study, and a proposed method, Journal of Theoretical and Applied Information Technology, 96 (2018), 6969-6980.   Google Scholar

[17]

D. KarabogaB. GorkemliC. Ozturk and N. Karaboga, A comprehensive survey: artificial bee colony (abc) algorithm and applications, Artificial Intelligence Review, 42 (2014), 21-57.   Google Scholar

[18]

R. Khokher and R. Chandra Sin, Footprint-based personal recognition using dactyloscopy technique, Industrial Mathematics and Complex Systems, 1 (2017), 207-219.   Google Scholar

[19]

I. M. Khudher and Y. I. Ibrahim, Swarm intelligent hybridization biometric, Indonesian Journal of Electrical Engineering and Computer Science, 18 (2020), 385-395.   Google Scholar

[20]

S. KothuriP. Annapurna and S. Lukka, Digit recognition using freeman chain code, International Journal of Application or Innovation in Engineering and Management (IJAIEM), 2 (2013), 362-365.   Google Scholar

[21]

K. S. KumarK. Ventakalakshmi and K. Karthikeyan, Threshold based lung image segmentation with robust artificial bee colony algorithm optimization technique, Asian Journal of Information Technology, 15 (2016), 4426-4430.   Google Scholar

[22]

A. KumarD. Kumar and S. K. Jarial, A review on artificial bee colony algorithms and their applications to data clustering, Cybernetics and Information Technologies, 17 (2017), 3-28.  doi: 10.1515/cait-2017-0027.  Google Scholar

[23]

S. B. Mirza and A. A. Waoo, A comprehensive study in wireless sensor network (wsn) using artificial bee colony (abc) algorithms, Journal, 6 (2019), 873-879.   Google Scholar

[24]

K. K. Nagwanshi, Cyber-forensic review of human footprint and gait for personal identification, IAENG International Journal of Computer Science, 46 (2019), 1-17.   Google Scholar

[25]

K. K. Nagwanshi and S. Dubey, Biometric authentication using human footprint, International Journal of Applied Information Systems, 3 (2012), 1-6.   Google Scholar

[26]

K. K. Nagwanshi and S. Dubey, Mathematical modeling of footprint based biometric recognition, International of mathematical trends and technology (IJMIT), 54 (2018), 49-61.   Google Scholar

[27]

V. R. Naramala and B Raveendrababu, Combined histogram chain code feature extraction method to recognize handwritten digits with the probabilistic neural network, International Journal of Applied Engineering Research, 9 (2014), 4585-4589.   Google Scholar

[28]

S. Nebti and A. Boukerram, Handwritten digits recognition based on swarm optimization methods, Networked Digital Technologies. Communications in Computer and Information Science, 87 (2010), 45-54.   Google Scholar

[29]

M. OliveiraD. PinheiroM. MacedoC. Bastos-Filho and R. Menezes, Uncovering the social interaction in swarm intelligence with network science, Applied Network Science, 1 (2018), 1-23.   Google Scholar

[30]

M. W. Powers, Evaluation: from precision, recall, and f-factor to ROC, informedness, markedness and correlation, Journal of Machine Learning Technologies, 2 (2007), 37-63.   Google Scholar

[31]

K. R. Raji and W. Xiaopeng, Study of biometric identification method based on naked footprint, International Journal of Science and Engineering, 5 (2013), 29-35.   Google Scholar

[32]

W. RongW. Hong and N. Yang, The research on footprint recognition method based on wavelet and fuzzy neural network, Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems, 3 (2009), 428-432.   Google Scholar

[33]

N. RusdiZ. R. YahyaN. Roslan and W. Z. Azman, Reconstruction of medical images using artificial bee colony algorithm, Mathematical Problems in Engineering, 2 (2018), 1-7.   Google Scholar

[34]

A. M. SalemA. A. Sewisy and U. A. Elyan, A vertex chain code approach for image recognition, International Journal on Graphics, Vision and Image Processing, 5 (2005), 1-8.   Google Scholar

[35]

M. Shokouhifar and G. S. Abkenar, An artificial bee colony optimization for mri fuzzy segmentation of brain tissue, International Conference on Management and Artificial Intelligence, 6 (2011). Google Scholar

[36]

E. B. Tirkolaee, A. Gol and G. W. Weber, Fuzzy mathematical programming and self-adaptive artificial fish swarm algorithm for just-in-time energy-aware flow shop scheduling problem with outsourcing option, IEEE Transactions on Fuzzy Systems, 1 (2020). Google Scholar

[37]

E. B. TirkolaeeM. AlinaghianA. A. RahmanimM. B. Sasia and A. K. Sangaiahc, An improved ant colony optimization for the multi-trip capacitated arc routing problem, Computers and Electrical Engineering Journal, 77 (2019), 457-470.   Google Scholar

[38]

E. B. TirkolaeeI. MahdaviM. M. Esfahani and G. W. Weber, A hybrid augmented ant colony optimization for the multi-trip capacitated arc routing problem under fuzzy demands for urban solid waste management, Waste Management and Research: The Journal for a Sustainable Circular Economy, 38 (2020), 156-172.   Google Scholar

[39]

U. UludagS. PankantiS. Prabhakar and A. Jain, Biometric Cryptosystems: issues and challenges, Proceedings of the IEEE, 92 (2004), 948-960.   Google Scholar

[40]

L. YangX. SunL. PengJ. Shao and T. Chi, An improved artificial bee colony algorithm for optimal land-use allocation, International Journal of Geographical Information Science, 26 (2015), 1470-1489.   Google Scholar

[41]

T. ZhangB. DingX. Zhao and Q. Yue, A fast feature selection algorithm based on swarm intelligence in acoustic defect detection, IEEE Access, 6 (2018), 28848-28858.   Google Scholar

Figure 1.  Bee Colony procedure
Figure 2.  A sample from the visual database
Figure 3.  A sample from the visual database
Figure 4.  The system outcome for acceptance message
Table 1.  Summary of biometric features related papers
Biometric Type Technique Performance Version
Foot-tip Morphology, statistical 83.38 - 89.52 2019 [13]
Foot-tip Modified Sequential Haar Energy Transform (MSHET) 92.37 2019 [19]
Foot-tip texture and shape 99 2016 [26]
Foot-tip Modified Haar Energy (MHE) 93 2016 [36]
Foot-tip (ABC) Algorithm for finding the curve fitting 97.15 2010 [14]
Lung image (ABC) to segment clinical 99.2 2016 [12]
Ear print Extracting the most discriminant key-points 99.6 2010 [17]
Foot-tip Fuzzy neural network 90-92.80 2010 [36]
Foot-tip Suggested work Statistical Chain code-based (ABC) 100 2020
Biometric Type Technique Performance Version
Foot-tip Morphology, statistical 83.38 - 89.52 2019 [13]
Foot-tip Modified Sequential Haar Energy Transform (MSHET) 92.37 2019 [19]
Foot-tip texture and shape 99 2016 [26]
Foot-tip Modified Haar Energy (MHE) 93 2016 [36]
Foot-tip (ABC) Algorithm for finding the curve fitting 97.15 2010 [14]
Lung image (ABC) to segment clinical 99.2 2016 [12]
Ear print Extracting the most discriminant key-points 99.6 2010 [17]
Foot-tip Fuzzy neural network 90-92.80 2010 [36]
Foot-tip Suggested work Statistical Chain code-based (ABC) 100 2020
Table 2.  Features description
Moment title Equation Parameter
Mean $ M_i = \frac{1}{N} \sum_{j=1}^{N} fit_i $ (5) Where $ f_ij $ is the value of the feature and N Denotes features frequency [27].
Description This factor is directly proportional to brightness. The big value the more brightness and vice versa [27].
Moment title Equation Parameter
Standard Deviation (STD) $ \varsigma = (\frac{1}{N} \sum_{j=1}^{n} (f_i - M_i)^2)^{\frac{1}{2}} $ (6) Where Mi represents the average of the image, $ f_ij $ denotes the value of the feature and N reflects the observation size [27].
Description This factor is inversely proportional to image contrast. The big value the small contrast and vice versa [27].
Center-Angle $ \Theta = | atan^2 (y, x) \pi / 180 $ (7) y, x are convoluted images with specific masks[27].
Description This factor describes the direction of the chain code.
Mean, Std of Histogram of the Chain Code = =
Description These factors describe the intensity and contrast of the Chain Code [22]
Moment title Equation Parameter
Mean $ M_i = \frac{1}{N} \sum_{j=1}^{N} fit_i $ (5) Where $ f_ij $ is the value of the feature and N Denotes features frequency [27].
Description This factor is directly proportional to brightness. The big value the more brightness and vice versa [27].
Moment title Equation Parameter
Standard Deviation (STD) $ \varsigma = (\frac{1}{N} \sum_{j=1}^{n} (f_i - M_i)^2)^{\frac{1}{2}} $ (6) Where Mi represents the average of the image, $ f_ij $ denotes the value of the feature and N reflects the observation size [27].
Description This factor is inversely proportional to image contrast. The big value the small contrast and vice versa [27].
Center-Angle $ \Theta = | atan^2 (y, x) \pi / 180 $ (7) y, x are convoluted images with specific masks[27].
Description This factor describes the direction of the chain code.
Mean, Std of Histogram of the Chain Code = =
Description These factors describe the intensity and contrast of the Chain Code [22]
Table 3.  The experimental results
Query name Bestsolution Image number Cluster Time in sec
Qry1 0 4 1 0.2888
Qry2 0.0740 13 2 0.1215
Qyr3 0.2932 28 3 0.1318
Qry4 0 31 4 0.1261
Qry5 0.0765 42 5 0.1196
Qry6 1.6607 52 6 0.1215
Qry7 0 61 7 0.1222
Qry8 0 71 8 0.1204
Qry9 0 82 9 0.1207
Qry10 0 83 9 0.1210
Query name Bestsolution Image number Cluster Time in sec
Qry1 0 4 1 0.2888
Qry2 0.0740 13 2 0.1215
Qyr3 0.2932 28 3 0.1318
Qry4 0 31 4 0.1261
Qry5 0.0765 42 5 0.1196
Qry6 1.6607 52 6 0.1215
Qry7 0 61 7 0.1222
Qry8 0 71 8 0.1204
Qry9 0 82 9 0.1207
Qry10 0 83 9 0.1210
Table 4.  Enhancement metrics
Metric Value
Accurecy 100%
Confidence 100%
Metric Value
Accurecy 100%
Confidence 100%
Table 5.  Enhancement analysis of related work
Moment title Equation Parameter
Technique Author Accuracy Rate %
(ABC) Algorithm for finding the curve fitting and Euclidean K. K. Nagwanshi and S. Dubey [14] 97.15
distance for foot-tip K. K. Nagwanshi and S. Dubey [15] 85
Fuzzy Neural Networks + Geometrical Characteristics W. Rong et al. [40] 90-92.80
(ABC) for Area Allocation L. Yang et al. [18] 67.4-67.7
(ABC) for object recognition C. Chidambaram and H. S. Lopes [3] 88-99
(ABC)+Fuzzy C Mean M. Shokouhifar and G. S. Abkenar [24] 98.38
(ABC) for Handwritten recognition S. Nebti and A. Boukerram [29] 99.82
The suggested paper (ABC)+Chain code+ statistical features Israa M. Kh., et.al. 100
Moment title Equation Parameter
Technique Author Accuracy Rate %
(ABC) Algorithm for finding the curve fitting and Euclidean K. K. Nagwanshi and S. Dubey [14] 97.15
distance for foot-tip K. K. Nagwanshi and S. Dubey [15] 85
Fuzzy Neural Networks + Geometrical Characteristics W. Rong et al. [40] 90-92.80
(ABC) for Area Allocation L. Yang et al. [18] 67.4-67.7
(ABC) for object recognition C. Chidambaram and H. S. Lopes [3] 88-99
(ABC)+Fuzzy C Mean M. Shokouhifar and G. S. Abkenar [24] 98.38
(ABC) for Handwritten recognition S. Nebti and A. Boukerram [29] 99.82
The suggested paper (ABC)+Chain code+ statistical features Israa M. Kh., et.al. 100
[1]

Guangzhou Chen, Guijian Liu, Jiaquan Wang, Ruzhong Li. Identification of water quality model parameters using artificial bee colony algorithm. Numerical Algebra, Control & Optimization, 2012, 2 (1) : 157-165. doi: 10.3934/naco.2012.2.157

[2]

Harish Garg. Solving structural engineering design optimization problems using an artificial bee colony algorithm. Journal of Industrial & Management Optimization, 2014, 10 (3) : 777-794. doi: 10.3934/jimo.2014.10.777

[3]

Roya Soltani, Seyed Jafar Sadjadi, Mona Rahnama. Artificial intelligence combined with nonlinear optimization techniques and their application for yield curve optimization. Journal of Industrial & Management Optimization, 2017, 13 (4) : 1701-1721. doi: 10.3934/jimo.2017014

[4]

Miao Yu. A solution of TSP based on the ant colony algorithm improved by particle swarm optimization. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 979-987. doi: 10.3934/dcdss.2019066

[5]

Veysel Fuat Hatipoğlu. A novel model for the contamination of a system of three artificial lakes. Discrete & Continuous Dynamical Systems - S, 2021, 14 (7) : 2261-2272. doi: 10.3934/dcdss.2020176

[6]

Omar Saber Qasim, Ahmed Entesar, Waleed Al-Hayani. Solving nonlinear differential equations using hybrid method between Lyapunov's artificial small parameter and continuous particle swarm optimization. Numerical Algebra, Control & Optimization, 2021, 11 (4) : 633-644. doi: 10.3934/naco.2021001

[7]

Xia Zhao, Jianping Dou. Bi-objective integrated supply chain design with transportation choices: A multi-objective particle swarm optimization. Journal of Industrial & Management Optimization, 2019, 15 (3) : 1263-1288. doi: 10.3934/jimo.2018095

[8]

Cai-Tong Yue, Jing Liang, Bo-Fei Lang, Bo-Yang Qu. Two-hidden-layer extreme learning machine based wrist vein recognition system. Big Data & Information Analytics, 2017, 2 (1) : 59-68. doi: 10.3934/bdia.2017008

[9]

María Chara, Ricardo A. Podestá, Ricardo Toledano. The conorm code of an AG-code. Advances in Mathematics of Communications, 2021  doi: 10.3934/amc.2021018

[10]

Steady Mushayabasa, Drew Posny, Jin Wang. Modeling the intrinsic dynamics of foot-and-mouth disease. Mathematical Biosciences & Engineering, 2016, 13 (2) : 425-442. doi: 10.3934/mbe.2015010

[11]

Jing Liu, Xiaodong Liu, Sining Zheng, Yanping Lin. Positive steady state of a food chain system with diffusion. Conference Publications, 2007, 2007 (Special) : 667-676. doi: 10.3934/proc.2007.2007.667

[12]

Soliman A. A. Hamdallah, Sanyi Tang. Stability and bifurcation analysis of Filippov food chain system with food chain control strategy. Discrete & Continuous Dynamical Systems - B, 2020, 25 (5) : 1631-1647. doi: 10.3934/dcdsb.2019244

[13]

Anna Chiara Lai, Paola Loreti. Self-similar control systems and applications to zygodactyl bird's foot. Networks & Heterogeneous Media, 2015, 10 (2) : 401-419. doi: 10.3934/nhm.2015.10.401

[14]

Laura Luzzi, Ghaya Rekaya-Ben Othman, Jean-Claude Belfiore. Algebraic reduction for the Golden Code. Advances in Mathematics of Communications, 2012, 6 (1) : 1-26. doi: 10.3934/amc.2012.6.1

[15]

Irene Márquez-Corbella, Edgar Martínez-Moro, Emilio Suárez-Canedo. On the ideal associated to a linear code. Advances in Mathematics of Communications, 2016, 10 (2) : 229-254. doi: 10.3934/amc.2016003

[16]

Serhii Dyshko. On extendability of additive code isometries. Advances in Mathematics of Communications, 2016, 10 (1) : 45-52. doi: 10.3934/amc.2016.10.45

[17]

Konovenko Nadiia, Lychagin Valentin. Möbius invariants in image recognition. Journal of Geometric Mechanics, 2017, 9 (2) : 191-206. doi: 10.3934/jgm.2017008

[18]

Boling Guo, Haiyang Huang. Smooth solution of the generalized system of ferro-magnetic chain. Discrete & Continuous Dynamical Systems, 1999, 5 (4) : 729-740. doi: 10.3934/dcds.1999.5.729

[19]

Yang Yu. Introduction: Special issue on computational intelligence methods for big data and information analytics. Big Data & Information Analytics, 2017, 2 (1) : i-ii. doi: 10.3934/bdia.201701i

[20]

James H. Lambert, Benjamin L. Schulte, Nilesh N. Joshi. Multiple criteria intelligence tracking for detecting extremes from sequences of risk incidents. Journal of Industrial & Management Optimization, 2008, 4 (3) : 511-533. doi: 10.3934/jimo.2008.4.511

 Impact Factor: 

Article outline

Figures and Tables

[Back to Top]