Education Quality Detection Method Based on the Probabilistic Neural Network Algorithm

• Autorzy: Wu Changdong , Jiang Hua , Wang Ping

Identyfikatory

DOI

10.29354/diag/127194

• Języki publikacji: EN

Abstrakty

EN

The traditional education quality detection method is too single and unreasonable, which is not suitable to evaluate students' ability comprehensively. In this paper, the probabilistic neural network (PNN) algorithm is used to detect the education quality by considering the important impact between the various achievements of students. PNN algorithm originates from Bayesian decision rule, and it uses the non-linear Gaussian Parzen window as the probability density function. As PNN model has the virtues of strong nonlinear and anti-interfering ability, it is fit to detect the education quality by classifying the students' achievements. Besides, the influences of different evaluation models on classification accuracy and efficiency are also discussed in this paper. Furthermore, the effect of spread value on PNN model is also discussed. Finally, the actual data are used to detect the education quality. Experimental results show that the detection accuracy can reach 95%, and the detection time is only 0.0156s based on the proposed method. That is to say, the method is a very practical detection algorithm with high accuracy and efficiency. Moreover, it also provides a reference for how to further improve the teaching quality.

Słowa kluczowe

EN

detection; probabilistic neural network; classification; students' achievement; education quality

PL

probabilistyczna sieć neuronowa; klasyfikacja; osiągnięcia studentów; jakość kształcenia

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2020
• Tom: Vol. 21, No. 4
• Strony: 79--86
• Opis fizyczny: Bibliogr. 33 poz., rys.

Twórcy

autor

Wu Changdong

• School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, 610039, China

autor

Jiang Hua

• School of information science and technology, Southwest Jiaotong University, Chengdu, 610031, China

autor

Wang Ping

• School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, 610039, China

Bibliografia

• 1. Mohamed Ben Haj Rhouma, Joviša Žunić, Mohammed Chachan Younis. Moment invariants for multicomponent shapes with applications to leaf classification. Computers and Electronics in Agriculture. 2017;142(A):326-337. https://doi.org/10.1016/j.compag.2017.08.029.
• 2. Fallatah Ahmad, Jones Simon, Mitchell David. Objectbased random forest classification for informal settlements identification in the Middle East: Jeddah a case study. International Journal of Remote Sensing. 2020;41(11): 4421-4445. https://doi.org/10.1080/01431161.2020.1718237
• 3. Ko BC, Gim JW, Nam JY. Cell image classification based on ensemble features and random forest. Electronics Letters.2011;47 (11):638-U72. http://dx.doi.org/10.1049/el.2011.0831
• 4. Sarah N, Alyami,Sunday O. Olatunji.Application of Support Vector Machine for Arabic Sentiment Classification Using Twitter-Based Dataset.Journal of Information & Knowledge Management. 2020;19 (01)2040018:1-13. https://doi.org/10.1142/S0219649220400183
• 5. Arivudainambi D, Varun Kumar KA, Sibi Chakkaravarthy S, et al. Malware traffic classification using principal component analysis and artificial neural network for extreme surveillance. Computer Communications. 2019;(147): 50-57. https://doi.org/10.1016/j.comcom.2019.08.003.
• 6. Hamid Ghanbari, Saeid Homayouni, Abdolreza Safari. et al. Gaussian mixture model and Markov random fields for hyperspectral image classification. European Journal of Remote Sensing. 2018; 51(1): 889-900. https://doi.org/10.1080/22797254.2018.1503565
• 7. Mirvaziri H, Mobarakeh ZS. Improvement of EEGbased motor imagery classification using ring topology-based particle swarm optimization. Biomedical Signal Processing and Control. 2017; (32): 69-75. https://doi.org/10.1016/j.bspc.2016.10.015
• 8. Anibou C, Saidi MN, Aboutajdine D. Classification of textured images based on discrete wavelet transform and information fusion. Journal of Information Processing Systems. 2015;11(3):421-437. https://doi.org/10.3745/JIPS.02.0028
• 9. Xin RY, Zhang J, Shao YT. Complex network classification with convolutional neural network. Tsinghua Science and technology. 2020;25(4): 447-457. https://dx.doi.org/10.26599/TST.2019.9010055
• 10. Griesshaber D, Vu NT, Maucher J. Low-resource text classification using domain-adversarial learning. Computer Speech and Language. 2020;62:101056. https://doi.org/10.1016/j.csl.2019.101056
• 11. Zhang Chao, Wang Jiandong, Huang Jian, et al. Detection and classification of short-circuit faults in distribution networks based on fortescue approach and softmax regression. International Journal of Electrical Power & Energy System. 2020; 118-105812. https://doi.org/10.1016/j.ijepes.2019.105812
• 12. Wang P, Zhang XM, Hao Y. A method combining CNN and ELM for feature extraction and classification of SAR image. Journal of Sensors. 2019:6134610.
• 13. Naik, SM, Jagannath, RPK, Kuppili.V. Bat algorithmbased weighted Laplacian probabilistic neural network. Neural Computing & Applications. 2020;32(4): 1157-1171. https://doi.org/10.1007/s00521-019-04475-4
• 14. Tsang BHP, Ma CC, Kuo CH. A PNN self-learning tool breakage detection system in end milling operations. Applied Soft Computing Journal. 2015;(37): 114-124. https://doi.org/10.1016/j.asoc.2015.08.019
• 15. Yiming Huang, Shuaishuai Hou, Shufeng Xu,et al. EMD-PNN based welding defects detection using laser-induced plasma electrical signals. Journal of Manufacturing Processes.2019;(45):642-651. https://doi.org/10.1016/j.jmapro.2019.08.006.
• 16. Behnam M. Pourghassem H. Spectral correlation power-based seizure detection using statistical multiLevel dimensionality reduction and PSO-PNN optimization algorithm. IETE Journal of Research. 2017;63(5):736-753. https://doi.org/10.1080/03772063.2017.1308845
• 17. Mondal A, Ghosh S, Ghosh A. Partially camouflaged object tracking using modified probabilistic neural network and fuzzy energy based active contour. International Journal of Computer Vision. 2017;122(1)116-148. https://doi.org/10.1007/s11263-016-0959-5
• 18. Beritelli F, Capizzi G, Lo SG, et al. Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks. Biomedical Engineering Letters. 2018;8(1):77-85. https://doi.org/10.1007/s13534-017-0046-z
• 19. Kusy M, Kluska J. Assessment of prediction ability for reduced probabilistic neural network in data classification problems. Soft Computing. 2017;21(1): 199-212. https://doi.org/10.1007/s00500-016-2382-9
• 20. Xiaoyan Huang, Zhaoyong Guan, Li He, et al. A PNN prediction scheme for local tropical cyclone intensity over the South China Sea. Natural Hazards. 2015; 81(2):1-19. https://doi.org/10.1007/s11069-015-2132-9
• 21. Lan XL, Cui J, Zhang ST. Research on the video advertising detection based on PNN and text detection method. Journal of Information Hiding and Multimedia Signal Processing. 2017;8(5):1082-1091.
• 22. Yang AM, Han Y, Xing HW. Building SVM and PNN optimal classifiers based on GA-PLS algorithm and the application in infrared spectrum. International Journal of Advanced Media and Communication. 2016;6(2-4)198-210. https://dx.doi.org/10.1504/IJAMC.2016.080977
• 23. Porwik P, Doroz R, Orczyk T. Signatures verification based on PNN classifier optimised by PSO algorithm. Pattern Recognition. 2016;(60):998-1014. https://doi.org/10.1016/j.patcog.2016.06.032
• 24 Rastegarzadeh G, Nemati M. Primary mass discrimination of high energy cosmic rays using PNN and k-NN methods. Advances in Space Research. 2018;61(4) 1181. https://doi.org/10.1016/j.asr.2017.11.016
• 25 Cao Y, Lv X, Han G. et al. Research on gas-path faultdiagnosis method of marine gas turbine based on exergy loss and probabilistic neural network. Energies 2019;(12): 4701. https://doi.org/10.3390/en12244701
• 26 Chandrasekara V, Tilakaratne C, Mammadov M. An improved probabilistic neural network model for directional prediction of a stock market index. Appl. Sci. 2019;(9): 5334. https://doi.org/10.3390/app9245334
• 27 Anggriawan DO, Amsyar A, Prasetyono E, et al. Load identification using harmonic based on probabilistic neural network. Emitter-International Journal of Engineering Technology. 2019;7(1): 71-82. https://doi.org/10.24003/emitter.v7i1.330
• 28 Zhang SJ, Zhao HM, Xu JJ, et al. A novel fault diagnosis method based on improved adaptive variational mode decomposition, energy entropy, and probabilistic neural network. Transaction of the Canadian Society for Mechanical Engineering. 2020;44(1): 121-132. https://doi.org/10.1139/tcsme-2018-0195
• 29 Xie Zihao, Yang Xiaohui, Li Anyi. et al. Fault diagnosis in industrial chemical processes using optimal probabilistic neural network. Canadian Journal of Chemical Engineering. 2019;97(9):2453-2464. https://doi.org/10.1002/cjce.23491
• 30 Mohanty Mihir Narayan, Palo Hemanta Kumar. Child emotion recognition using probabilistic neural network with effective features. Measurement. 2020;(152): 107369. https://doi.org/10.1016/j.measurement.2019.107369
• 31 Wang B, Ke HW, Ma XD, et al. Fault diagnosis method for engine control system based on probabilistic neural network and support vector machine. Applied Sciences-Basel. 2019;9(19): 4122. https://doi.org/10.3390/app9194122
• 32 Ying Xuxia, Tang Bibo, Zhou Canxin. Nursing scheme based on back propagation neural network and probabilistic neural network in chronic kidney disease. Journal of Medical Image and Health informatics. 2020;10(2): 416-421. https://doi.org/10.1166/jmihi.2020.2879
• 33 Fan Hongguang, Pei Jihong, Zhao Yang. An optimized probabilistic neural network with unit hyperspherical crown mapping and adaptive kernel coverage. Neurocomputing. 2020;(373): 24-34. https://doi.org/10.1016/j.neucom.2019.09.029

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).