التعرف على صور عاطفة الوجه بناءً على الخوارزمية الجينية الثنائية - الغابة العشوائية

S. Gupta, "Facial emotion recognition in real-time and static images," in 2018 2nd International Conference on Inventive Systems and Control (ICISC), 2018: IEEE, https://doi.org/10.1109/ICISC.2018.8398861, pp. 553-560.

Liebold et al., "Human Capacities for Emotion Recognition and their Implications for Computer Vision," i-com, vol. 14, no. 2, https://doi.org/10.1515/icom-2015-0032, pp. 126- 137, 2015.

C. Clavel, "Surprise and human-agent interactions," Review of Cognitive Linguistics. Published under the auspices of the Spanish Cognitive Linguistics Association, vol. 13, no. 2, https://doi.org/10.1075/rcl.13.2.08cla , pp. 461-477, 2015.

B. Liebold and P. Ohler, "Multimodal emotion expressions of virtual agents, mimic and vocal emotion expressions and their effects on emotion recognition," in 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, 2013: IEEE, https://doi.org/10.1109/ACII.2013.73 , pp. 405-410.

K. Bahreini, R. Nadolski, and W. Westera, "Towards multimodal emotion recognition in e-learning environments," Interactive Learning Environments, vol. 24, no. 3, https://doi.org/10.1080/10494820.2014.908927 , pp. 590-605, 2016.

M. A. A. Albadr, S. Tiun, and F. T. Al-Dhief, "Evaluation of machine translation systems and related procedures,"

https://www.arpnjournals.org/jeas/research_papers/rp

_2018/jeas_0618_7156.pdf , 2018.

M. A. A. Albadr, M. Ayob, S. Tiun, F. T. AL-Dhief,

A. Arram, and S. Khalaf, “Breast cancer diagnosis using the fast learning network algorithm,” Frontiers in Oncology, vol. 13, https://doi.org/10.3389/fonc.2023.1150840 , pp. 01- 16, 2023.

M. A. A. Albadr, S. Tiun, M. Ayob, F. T. AL-Dhief,

K. Omar, and M. K. Maen, "Speech emotion recognition using optimized genetic algorithm- extreme learning machine," Multimedia Tools and Applications, vol. 81, no. 17, https://doi.org/10.1007/s11042-022-12747-wpp. 23963-23989 , 2022.

A. M. Mohammad, H. Attia, and Y. H. Ali, "Comparative Analysis of MFO, GWO and GSO for

Classification of Covid-19 Chest X-Ray Images," Baghdad Science Journal, vol. 20, no. 4 (SI), https://doi.org/10.21123/bsj.2023.9236 , pp. 1540- 1540, 2023.

A. N. AL-Thaweni, W. H. Yousif, and S. S. Hassan, "Detection of BRCA1and BRCA2 mutation for Breast Cancer in Sample of Iraqi Women above 40 Years," Baghdad Science Journal, vol. 7, no. 1, https://www.iasj.net/iasj/download/1cc0a9dbc3aa95a 1 , pp. 394-400, 2010.

M. A. A. Albadr, M. Ayob, S. Tiun, F. T. Al-Dhief, and M. K. Hasan, "Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection," Frontiers in Public Health, vol. 10, https://doi.org/10.3389/fpubh.2022.925901 , p. 925901, 2022.

H. Venkatesan, T. V. Venkatasubramanian, and J. Sangeetha, "Automatic language identification using machine learning techniques," in 2018 3rd International Conference on Communication and Electronics Systems (ICCES), 2018: IEEE, https://doi.org/10.1109/CESYS.2018.8724070 , pp. 583-588.

A. Tunga, S. V. Nuthalapati, and J. Wachs, "Pose- based sign language recognition using GCN and BERT," in Proceedings of the IEEE/CVF winter conference on applications of computer vision, 2021, pp. 31-40 .

M. A. A. Albadr, S. Tiun, M. Ayob, F. T. Al-Dhief, T.-A. N. Abdali, and A. F. Abbas, "Extreme learning machine for automatic language identification utilizing emotion speech data," in 2021 international conference on electrical, communication, and computer engineering (ICECCE), 2021: IEEE, https://doi.org/10.1109/ICECCE52056.2021.9514107

, pp. 1-6.

P. Radhika, R. A. Nair, and G. Veena, "A comparative study of lung cancer detection using machine learning algorithms," in 2019 IEEE international conference on electrical, computer and communication technologies (ICECCT), 2019: IEEE, https://doi.org/10.1109/ICECCT.2019.8869001 , pp. 1-4.

F. T. AL-Dhief et al., "Voice Pathology Detection Using Decision Tree Classifier," in 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), 2023: IEEE,

https://doi.org/10.1109/ICTC58733.2023.10392786 , pp. 36-41.

A. Al-Nasheri, G. Muhammad, M. Alsulaiman, and

Z. Ali, "Investigation of voice pathology detection and classification on different frequency regions using correlation functions," Journal of Voice, vol. 31, no. 1, https://doi.org/10.1016/j.jvoice.2016.01.014

, pp. 3-15, 2017.

V. Mittal and R. Sharma, "Deep learning approach for voice pathology detection and classification," International Journal of Healthcare Information Systems and Informatics (IJHISI), vol. 16, no. 4, https://doi.org/10.4018/IJHISI.20211001.oa28 , pp. 1- 30, 2021.

M. A. A. Albadr, S. Tiun, M. Ayob, and F. T. Al- Dhief, "Particle swarm optimization-based extreme learning machine for covid-19 detection," Cognitive Computation, https://doi.org/doi.org/10.1007/s12559- 022-10063-x , pp. 1-16, 2022.

A. N. Navaz, S. M. Adel, and S. S. Mathew, "Facial Image Pre-Processing and Emotion Classification: A Deep Learning Approach," in 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), 2019: IEEE, https://doi.org/10.1109/AICCSA47632.2019.9035268

, pp. 1-8.

K. Rajesh and M. Naveenkumar, "A robust method for face recognition and face emotion detection system using support vector machines," in 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), 2016: IEEE, https://doi.org/10.1109/ICEECCOT.2016.7955175 , pp. 1-5.

A. Koutlas and D. I. Fotiadis, "An automatic region based methodology for facial expression recognition," in 2008 IEEE International Conference on Systems, Man and Cybernetics, 2008: IEEE, https://doi.org/10.1109/ICSMC.2008.4811353 , pp. 662-666.

Y. Zhong, L. Sun, C. Ge, and H. Fan, "HOG-ESRs Face Emotion Recognition Algorithm Based on HOG Feature and ESRs Method," Symmetry, vol. 13, no. 2, https://doi.org/10.3390/sym13020228 , pp. 228, 2021.

T. Quoc Bao, N. T. Tan Kiet, T. Quoc Dinh, and H.

X. Hiep, "Plant species identification from leaf patterns using histogram of oriented gradients feature space and convolution neural networks," Journal of Information and Telecommunication, vol. 4, no. 2, https://doi.org/10.1080/24751839.2019.1666625 , pp. 140-150, 2020.

J. Zeng, Y. Chen, Y. Zhai, J. Gan, W. Feng, and F. Wang, "A novel finger-vein recognition based on quality assessment and multi-scale histogram of oriented gradients feature," International Journal of Enterprise Information Systems (IJEIS), vol. 15, no. 1, https://doi.org/10.4018/IJEIS.2019010106 , pp. 100-115, 2019.

W. Zhou, S. Gao, L. Zhang, and X. Lou, "Histogram of oriented gradients feature extraction from raw

Bayer pattern images," IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 67, no. 5, https://doi.org/10.1109/TCSII.2020.2980557 , pp. 946-950, 2020.

M. Sun and D. Li, "Smart face identification via improved LBP and HOG features," Internet Technology Letters, vol. 4, no. 3, https://doi.org/10.1002/itl2.229 , Pp. e229, 2021.

C. Bi, "Deterministic local alignment methods improved by a simple genetic algorithm," Neurocomputing, vol. 73, no. 13-15, https://doi.org/10.1016/j.neucom.2010.01.023 , pp. 2394-2406, 2010.

M. H. Mohamed, "Rules extraction from constructively trained neural networks based on genetic algorithms," Neurocomputing, vol. 74, no. 17, https://doi.org/10.1016/j.neucom.2011.04.009 , pp. 3180-3192, 2011.

J. H. HOLLAND, "Adaption in Natural and Artificial Systems," An Introductory Analysis with Application to Biology, Control and Artificial Intelligence, 1975.

D. E. Goldberg and J. H. Holland, "Genetic algorithms and machine learning," Machine learning, vol. 3, no. 2, pp. 95-99, 1988.

K. Srinivas, V. S. Rao, and M. Sreemalli, "Binarized genetic algorithm with neural network for stock market prediction," in Proceedings of the 2018 International Conference on Communication Engineering and Technology, 2018, https://doi.org/10.1145/3194244.3194249 , pp. 8-11.

A. K. Shukla, P. Singh, and M. Vardhan, "A new hybrid feature subset selection framework based on binary genetic algorithm and information theory," International Journal of Computational Intelligence and Applications, vol. 18, no. 03, https://doi.org/10.1142/S1469026819500202 , pp. 1950020, 2019.

X.-B. Hu and E. Di Paolo, "Binary-representation- based genetic algorithm for aircraft arrival sequencing and scheduling," IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 2, https://doi.org/10.1109/TITS.2008.922884 , pp. 301- 310, 2008.

M. Gholizadeh, M. Jamei, I. Ahmadianfar, and R. Pourrajab, "Prediction of nanofluids viscosity using random forest (RF) approach," Chemometrics and Intelligent Laboratory Systems, vol. 201, https://doi.org/10.1016/j.chemolab.2020.104010 , pp. 104010, 2020.

N. Farnaaz and M. Jabbar, "Random forest modeling for network intrusion detection system," Procedia Computer Science, vol. 89, https://doi.org/10.1016/j.procs.2016.06.047 , pp. 213- 217, 2016.

J. Wang, H. Yu, Q. Hua, S. Jing, Z. Liu, X. Peng, and

Y. Luo, "A descriptive study of random forest algorithm for predicting COVID-19 patients

outcome," PeerJ, vol. 8

https://doi.org/10.7717/peerj.9945 , pp. e9945, 2020.

S. Wang, Y. Wang, D. Wang, Y. Yin, Y. Wang, and

Y. Jin, "An improved random forest-based rule extraction method for breast cancer diagnosis," Applied Soft Computing, vol. 86, https://doi.org/10.1016/j.asoc.2019.105941 , pp. 105941, 2020.

N. Siddiqui, R. Dave, T. Bauer, T. Reither, D. Black, and M. Hanson, "A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluation," arXiv preprint arXiv: 2201.12705, https://doi.org/10.1109/CACML55074.2022.00020 , 2022.

C. Xu, C. Yan, M. Jiang, F. Alenezi, A. Alhudhaif, N. Alnaim, K. Polat, and W. Wu, "A novel facial emotion recognition method for stress inference of facial nerve paralysis patients," Expert Systems with Applications, vol. 197, https://doi.org/10.1016/j.eswa.2022.116705 , pp. 116705, 2022.

J. Zhi, T. Song, K. Yu, F. Yuan, H. Wang, G. Hu, and

H. Yang, "Multi-Attention Module for Dynamic Facial Emotion Recognition," Information, vol. 13, no. 5, https://doi.org/10.3390/info13050207 , pp. 207, 2022.

X. Li and J. Sun, "Facial emotion recognition via stationary wavelet entropy and particle swarm optimization," in Cognitive Systems and Signal Processing in Image Processing: Elsevier, 2022, https://doi.org/10.1016/B978-0-12-824410-4.00005-2

, pp. 145-162.

Y. Khaireddin and Z. Chen, "Facial emotion recognition: State of the art performance on FER2013," arXiv preprint arXiv:2105.03588, https://doi.org/10.48550/arXiv 2105.03588 , 2021.

J. H. Kim, A. Poulose, and D. S. Han, "The extensive usage of the facial image threshing machine for facial emotion recognition performance," Sensors, vol. 21, no. 6, https://doi.org/10.3390/s21062026 , pp. 2026, 2021.

P. Sumathy and A. Chandrasekaran, "An Optimized Image Pre-Processing Technique for Face Emotion Recognition System," Annals of the Romanian Society for Cell Biology, vol. 25, no. 6, pp. 6247- 6261, 2021.

G. Yu, "Emotion Monitoring for Preschool Children Based on Face Recognition and Emotion Recognition Algorithms," Complexity, vol. 2021, https://doi.org/10.1155/2021/6654455 , 2021.

M. Nasri, M. A. Hmani, A. Mtibaa, D. Petrovska- Delacretaz, M. B. Slima, and A. B. Hamida, "Face emotion recognition from static image based on convolution neural networks," in 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2020: IEEE,

https://doi.org/10.1109/ATSIP49331.2020.9231537 , pp. 1-6.

V. Sati, S. M. Sánchez, N. Shoeibi, A. Arora, and J.

M. Corchado, "Face Detection and Recognition, Face Emotion Recognition Through NVIDIA Jetson Nano," in International Symposium on Ambient Intelligence, 2020: Springer, https://doi.org/10.1007/978-3-030-58356-9_18 , pp. 177-185.

M. Zubair, A. Ali, S. Naeem, F. Jamal, and C. Chesneau, "Emotion recognition from facial expression using machine vision approach," J. Appl. Emerging Sci, vol. 10, no. 1, pp. 35-40, 2020.

D. Mungra, A. Agrawal, P. Sharma, S. Tanwar, and

M. S. Obaidat, "PRATIT: a CNN-based emotion recognition system using histogram equalization and data augmentation," Multimedia Tools and Applications, vol. 79, no. 3, https://doi.org/10.1007/s11042-019-08397-0 , pp. 2285-2307, 2020.

H. Zhang, A. Jolfaei, and M. Alazab, "A face emotion recognition method using convolutional neural network and image edge computing," IEEE Access, vol. 7,

https://doi.org/10.1109/ACCESS.2019.2949741 , pp. 159081-159089, 2019.

D. K. Jain, P. Shamsolmoali, and P. Sehdev, "Extended deep neural network for facial emotion recognition," Pattern Recognition Letters, vol. 120, https://doi.org/10.1016/j.patrec.2019.01.008 , pp. 69- 74, 2019.

A. Georghiades, P. Belhumeur, and D. Kriegman, "Yale face database," Center for computational Vision and Control at Yale University, vol. 2, no. 6, p. 33, 1997.

M. H. Mutar, E. H. Ahmed, and H. Majid Razaq Mohamed ALsemawi, "Ear Recognition System Using Random Forest and Histograms of Oriented Gradients Techniques," Solid State Technology, vol. 63, no. 4, https://doi.org/10.11591/ijeecs.v27.i1.pp181-188 , pp. 8740-8748, 2020.

A. J. Moghaddam and A. K. Doost, "Technical- economical optimization of horizontal axis wind turbines by means of the genetic algorithm," Natural Science, vol. 2013, https://doi.org/10.4236/ns.2013.512A001 , 2013.

Z. Michalewicz and S. J. Hartley, "Genetic algorithms+ data structures= evolution programs," Mathematical Intelligencer, vol. 18, no. 3, https://doi.org/10.1007/978-3-662-03315-9 , pp. 71, 1996.

A. R. Chowdhury, T. Chatterjee, and S. Banerjee, "A random forest classifier-based approach in the detection of abnormalities in the retina," Med. Biol. Eng. Comput., vol. 57, no. 1, https://doi.org/10.1007/s11517-018-1878-0 , pp. 193- 203, 2019.

M. Koranga, P. Pant, T. Kumar, D. Pant, A. K. Bhatt, and R. Pant, "Efficient water quality prediction models based on machine learning algorithms for Nainital Lake, Uttarakhand," Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2021.12.334 , 2022.

Z. Liu and Y. Shi, "A Hybrid IDS Using GA-Based Feature Selection Method and Random Forest," International Journal of Machine Learning and Computing, vol. 12, no. 2, 2022.

S. S. Shreem, H. Turabieh, S. Al Azwari, and F. Baothman, "Enhanced binary genetic algorithm as a feature selection to predict student performance," Soft Computing, https://doi.org/10.1007/s00500-021- 06424-7 , pp. 1-13, 2022.

T. Bashir, K. Morimoto, A. Iguchi, Y. Tsuji, T. Kashiwa, and S. Nishiwaki, "Optimal design of broadband non‐radiative dielectric guide devices using binary genetic algorithm and 2D‐FVFEM," International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, https://doi.org/10.1002/jnm.2984 , pp. e2984.

S. Kim, S. Lee, J. I. Choi, and H. Cho, "Binary genetic algorithm for optimal joinpoint detection: application to cancer trend analysis," Statistics in Medicine, vol. 40, no. 3, https://doi.org/10.1002/sim.8803 , pp. 799-822, 2021.

M. Aly, "Face recognition using SIFT features," CNS/Bi/EE report, vol. 186, 2006.

J. Cao, Y. Fu, X. Shi, and B. W.-K. Ling, "Subspace Clustering Based on Latent Low Rank Representation with Schatten-p Norm," in 2020 2nd World Symposium on Artificial Intelligence (WSAI), 2020: IEEE, https://doi.org/10.1109/WSAI49636.2020.9143313 , pp. 58-62.

S. K. Dandpat and S. Meher, "Performance improvement for face recognition using PCA and two-dimensional PCA," in 2013 International Conference on Computer Communication and Informatics, 2013: IEEE, https://doi.org/10.1109/ICCCI.2013.6466291 , pp. 1- 5.

D. Li, H. Luo, and Z. Shi, "Redundant DWT based translation invariant wavelet feature extraction for face recognition," in 2008 19th International Conference on Pattern Recognition, 2008: IEEE, https://doi.org/10.1109/ICPR.2008.4761070 , pp. 1-4.

C. Zhou, L. Wang, Q. Zhang, and X. Wei, "Face recognition based on PCA and logistic regression analysis," Optik, vol. 125, no. 20, https://doi.org/10.1016/j.ijleo.2014.07.080 , pp. 5916- 5919, 2014.