Artificial Neural Network for Classification Task in Tabular Datasets and Image Processing: A Systematic Literature Review

Authors

  • Adi Zaenul Mustaqim Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Indonesia
  • Nurdana Ahmad Fadil Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Indonesia
  • Dyah Aruming Tyas Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Indonesia

DOI:

https://doi.org/10.15575/join.v8i2.1002

Keywords:

Artificial Neural Network, Tabular Dataset, Image Processing, Classification, Systematic Literature Review

Abstract

Artificial Neural Network (ANN) is one of the machine learning algorithms that is widely used for classification cases. Some examples of classification cases that can be handled with ANN include classifications in the health sector, banking, and classification in image processing. This study presents a systematic literature review (SLR) of the ANN algorithm to find a research gap that can be used in future research. There are 3 phases used in preparing the SLR. Those are planning, conducting, and reporting. Formulation of research questions and establishing a review protocol is carried out in the planning phase. The second phase is conducted. In this phase, searching for relevant articles is carried out, determining the quality of the literature found and selecting particles according to what has been formulated in the planning phase. The selected literature is then carried out by the process of extracting data and information and then synthesizing the data. Writing SLR articles based on existing findings is carried out in the last phase, namely reporting. The results of data and information extraction from the 13 reviewed articles show that the ANN algorithm is powerful enough with satisfactory results to handle classification cases that use tabular datasets or image datasets. The challenges faced are the need for extensive training data so that ANN performance can be better, the use of appropriate evaluation measures based on the cases studied does not only rely on accuracy scores, and the determination of the correct hyperparameters to get better performance in the case of image processing.

References

K. Wisaeng, “A Comparison of Different Classification Techniques for Bank Direct Marketing,” 2013.

D. T. Larose, “Discovering Knowledge in Data: An Introduction to Data Mining,” 2005.

S. M. Gorade, A. Deo, and P. Purohit, “A Study of Some Data Mining Classification Techniques,” International Research Journal of Engineering and Technology, 2017, [Online]. Available: www.irjet.net

A. J. Khalil, A. M. Barhoom, B. S. Abu-Nasser, M. M. Musleh, and S. S. Abu-Naser, “Energy Efficiency Prediction using Artificial Neural Network,” 2019. [Online]. Available: www.ijeais.org/ijapr

T. G. Pratama, R. Hartanto, and N. A. Setiawan, “Machine learning algorithm for improving performance on 3 AQ-screening classification,” 2019.

J. Wei et al., “Machine learning in materials science,” InfoMat, vol. 1, no. 3. Blackwell Publishing Ltd, pp. 338–358, Sep. 01, 2019. doi: 10.1002/inf2.12028.

A. hai Li et al., “Prediction and verification of the effect of psoriasis on coronary heart disease based on artificial neural network,” Heliyon, vol. 8, no. 9, Sep. 2022, doi: 10.1016/j.heliyon.2022.e10677.

S. C. Dubeh, K. S. Mundhe, and A. A. Kadam, Credit Card Fraud Detection using Artificial Neural Network and Backpropagation. 2020.

N. Portillo Juan and V. Negro Valdecantos, “Review of the application of Artificial Neural Networks in ocean engineering,” Ocean Engineering, vol. 259. Elsevier Ltd, Sep. 01, 2022. doi: 10.1016/j.oceaneng.2022.111947.

J. Han, M. Kamber, and J. Pei, “Data Mining Classification,” in Data Mining, Elsevier, 2012, pp. 393–442. doi: 10.1016/B978-0-12-381479-1.00009-5.

Y. S. Park and S. Lek, “Artificial Neural Networks: Multilayer Perceptron for Ecological Modeling,” Developments in Environmental Modelling, vol. 28, pp. 123–140, Jan. 2016, doi: 10.1016/B978-0-444-63623-2.00007-4.

V. Chandwani, S. K. Vyas, V. Agrawal, and G. Sharma, “Soft Computing Approach for Rainfall-runoff Modelling: A Review,” Aquat Procedia, vol. 4, pp. 1054–1061, Jan. 2015, doi: 10.1016/J.AQPRO.2015.02.133.

H. K. Ghritlahre and R. K. Prasad, “Application of ANN technique to predict the performance of solar collector systems - A review,” Renewable and Sustainable Energy Reviews, vol. 84. Elsevier Ltd, pp. 75–88, Mar. 01, 2018. doi: 10.1016/j.rser.2018.01.001.

H. nan Guo, S. biao Wu, Y. jie Tian, J. Zhang, and H. tao Liu, “Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review,” Bioresour Technol, vol. 319, p. 124114, Jan. 2021, doi: 10.1016/J.BIORTECH.2020.124114.

P. Saikia, R. D. Baruah, S. K. Singh, and P. K. Chaudhuri, “Artificial Neural Networks in the doprimary of reservoir characterization: A review from shallow to deep models,” Computers and Geosciences, vol. 135. Elsevier Ltd, Feb. 01, 2020. doi: 10.1016/j.cageo.2019.104357.

D. A. Otchere, T. O. Arbi Ganat, R. Gholami, and S. Ridha, “Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models,” J Pet Sci Eng, vol. 200, p. 108182, May 2021, doi: 10.1016/J.PETROL.2020.108182.

E. W. C. Wong and D. K. Kim, “A simplified method to predict fatigue damage of TTR subjected to short-term VIV using artificial neural network,” Advances in Engineering Software, vol. 126, pp. 100–109, Dec. 2018, doi: 10.1016/J.ADVENGSOFT.2018.09.011.

H. Ha and H. Zhang, “DeepPerf: Performance Prediction for Configurable Software with Deep Sparse Neural Network,” in Proceedings - International Conference on Software Engineering, May 2019, vol. 2019-May, pp. 1095–1106. doi: 10.1109/ICSE.2019.00113.

F. López-Martínez, E. R. Núñez-Valdez, R. G. Crespo, and V. García-Díaz, “An artificial neural network approach for predicting hypertension using NHANES data,” Sci Rep, vol. 10, no. 1, Dec. 2020, doi: 10.1038/s41598-020-67640-z.

N. Portillo Juan and V. Negro Valdecantos, “Review of the application of Artificial Neural Networks in ocean engineering,” Ocean Engineering, vol. 259. Elsevier Ltd, Sep. 01, 2022. doi: 10.1016/j.oceaneng.2022.111947.

A. Apicella, F. Donnarumma, F. Isgrò, and R. Prevete, “A survey on modern trainable activation functions,” Neural Networks, vol. 138, pp. 14–32, Jun. 2021, doi: 10.1016/J.NEUNET.2021.01.026.

C. Pelletier, G. I. Webb, and F. Petitjean, “Temporal convolutional neural network for the classification of satellite image time series,” Remote Sens (Basel), vol. 11, no. 5, Mar. 2019, doi: 10.3390/rs11050523.

A. Apicella, F. Donnarumma, F. Isgrò, and R. Prevete, “A survey on modern trainable activation functions,” Neural Networks, vol. 138, pp. 14–32, Jun. 2021, doi: 10.1016/J.NEUNET.2021.01.026.

H. R. Maier, A. Jain, G. C. Dandy, and K. P. Sudheer, “Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions,” Environmental Modelling & Software, vol. 25, no. 8, pp. 891–909, Aug. 2010, doi: 10.1016/J.ENVSOFT.2010.02.003.

W. Wu, G. C. Dandy, and H. R. Maier, “Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling,” Environmental Modelling & Software, vol. 54, pp. 108–127, Apr. 2014, doi: 10.1016/J.ENVSOFT.2013.12.016.

L. Ali, A. Rahman, A. Khan, M. Zhou, A. Javeed, and J. A. Khan, “An Automated Diagnostic System for Heart Disease Prediction Based on ?2 Statistical Model and Optimally Configured Deep Neural Network,” IEEE Access, vol. 7, pp. 34938–34945, 2019, doi: 10.1109/ACCESS.2019.2904800.

M. M. Bukhari, B. F. Alkhamees, S. Hussain, A. Gumaei, A. Assiri, and S. S. Ullah, “An Improved Artificial Neural Network Model for Effective Diabetes Prediction,” Complexity, vol. 2021, 2021, doi: 10.1155/2021/5525271.

P. Sonar and K. Jayamalini, Diabetes Prediction Using Different Machine Learning Approaches. 2019.

L. Ali, A. Rahman, A. Khan, M. Zhou, A. Javeed, and J. A. Khan, “An Automated Diagnostic System for Heart Disease Prediction Based on ?2 Statistical Model and Optimally Configured Deep Neural Network,” IEEE Access, vol. 7, pp. 34938–34945, 2019, doi: 10.1109/ACCESS.2019.2904800.

A. RB and S. K. KR, “Credit card fraud detection using artificial neural network,” Global Transitions Proceedings, vol. 2, no. 1, pp. 35–41, Jun. 2021, doi: 10.1016/j.gltp.2021.01.006.

K. Sujatha, B. Chandrashaker Reddy, R. S. Ponmagal, and S. Q. Cao, “Analysis of CT images for detection of Colorectal Cancers using Hybrid Artificial Neural Networks and Fire Fly Algorithm,” in Procedia Computer Science, 2020, vol. 171, pp. 1517–1526. doi: 10.1016/j.procs.2020.04.162.

T. Jasi?ski, “Modeling electricity consumption using nighttime light images and artificial neural networks,” Energy, vol. 179, pp. 831–842, Jul. 2019, doi: 10.1016/j.energy.2019.04.221.

L. Pan, R. Rogulin, and S. Kondrashev, “Artificial neural network for defect detection in CT images of wood,” Comput Electron Agric, vol. 187, Aug. 2021, doi: 10.1016/j.compag.2021.106312.

S. Yunhong, Y. Shilei, Z. Xiaojing, and Y. Jinhua, “Edge Detection Algorithm of MRI Medical Image Based on Artificial Neural Network,” Procedia Comput Sci, vol. 208, pp. 136–144, 2022, doi: 10.1016/j.procs.2022.10.021.

R. Azadnia and K. Kheiralipour, “Recognition of leaves of different medicinal plant species using a robust image processing algorithm and artificial neural networks classifier,” J Appl Res Med Aromat Plants, vol. 25, Dec. 2021, doi: 10.1016/j.jarmap.2021.100327.

H. Azgomi, F. R. Haredasht, and M. R. Safari Motlagh, “Diagnosis of some apple fruit diseases by using image processing and artificial neural network,” Food Control, vol. 145, p. 109484, Mar. 2023, doi: 10.1016/j.foodcont.2022.109484.

I. Faturrahman, "Pattern Recognition of Hijaiyah Khat Kufi Letters With Sobel Edge Detection Method Based on Backpropagation Imitation Neural Network," Journal of Informatics Engineering, vol. 11, no. 1, pp. 37–46, May 2018, doi: 10.15408/jti.v11i1.6262.

Downloads

Published

2023-12-28

Issue

Section

Article

Citation Check