Machine Learning Applications based on SVM Classification A Review


  • Dakhaz Mustafa Abdullah Technical College of Informatics, Akre Duhok Polytechnic University Duhok, Iraq
  • Adnan Mohsin Abdulazeez Research Center of Duhok Polytechnic University Duhok Polytechnic University Duhok, Iraq



ML, Classification, SVM, Applications


Extending technologies and data development culminated in the need for quicker and more reliable processing of massive data sets. Machine Learning techniques are used excessively. This paper, therefore, attempts to deal with data processing, using a support vector machine (SVM) algorithm in different fields since it is a reliable, efficient classification method in the area of machine learning. Accordingly, many works have been explored in this paper to cover the use of SVM classifier. Classification based on SVM has been used in many fields like face recognition, diseases diagnostics, text recognition, sentiment analysis, plant disease identification and intrusion detection system for network security application. Based on this study, it can be concluded that SVM classifier has obtained high accuracy results in most of the applications, specifically, for face recognition and diseases identification applications.


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Chandra, M. A., & Bedi, S. S. (2018). Survey on SVM and their application in image classification. International Journal of Information Technology, 1-11.‏

Zeebaree, Diyar Qader, Habibollah Haron, Adnan Mohsin Abdulazeez, and Dilovan Asaad Zebari. "Trainable Model Based on New Uniform LBP Feature to Identify the Risk of the Breast Cancer." In 2019 International Conference on Advanced Science and Engineering (ICOASE), pp. 106-111. IEEE, 2019.

Zeebaree, Diyar Qader, Habibollah Haron, Adnan Mohsin Abdulazeez, and Dilovan Asaad Zebari. "Machine learning and Region Growing for Breast Cancer Segmentation." In 2019 International Conference on Advanced Science and Engineering (ICOASE), pp. 88-93. IEEE, 2019.

Khorshid, S. F., & Abdulazeez, A. M. (2021). BREAST CANCER DIAGNOSIS BASED ON K-NEAREST NEIGHBORS: A REVIEW. PalArch's Journal of Archaeology of Egypt/Egyptology, 18(4), 1927-1951.

Zebari, Dilovan Asaad, Diyar Qader Zeebaree, Adnan Mohsin Abdulazeez, Habibollah Haron, and Haza Nuzly Abdull Hamed. "Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images." IEEE Access 8 (2020): 203097-203116.

Zantalis, F., Koulouras, G., Karabetsos, S., & Kandris, D. (2019). A review of machine learning and IoT in smart transportation. Future Internet, 11(4), 94.‏

Adnan.M. Abdulazeez, Sulaiman, M. A. Diyar Q.Zeebaree (2020). Evaluating Data Mining Classification Methods Performance in Internet of Things Applications. Journal of Soft Computing and Data Mining, 1(2), 11-25.

Wei, H., Shan, C., Hu, C., Sun, H., & Lei, M. (2018). Software defect distribution prediction model based on NPE-SVM. China Communications, 15(5), 173-182.‏

Al-jaboriy, S. S., Sjarif, N. N. A., Chuprat, S., & Abduallah, W. M. (2019). Acute lymphoblastic leukemia segmentation using local pixel information. Pattern Recognition Letters, 125, 85-90.‏

Abdullah, D. M., & Ahmed, N. S. (2021). A Review of most Recent Lung Cancer Detection Techniques using Machine Learning. International Journal of Science and Business, 5(3), 159-173.

Jahwar, A. F., & Abdulazeez, A. M. (2020). Meta-Heuristic Algorithms For K-Means Clustering: A Review. PalArch's Journal of Archaeology of Egypt/Egyptology, 17(7), 12002-12020..

Somvanshi, M., Chavan, P., Tambade, S., & Shinde, S. V. (2016, August). A review of machine learning techniques using decision tree and support vector machine. In 2016 International Conference on Computing Communication Control and automation (ICCUBEA) (pp. 1-7). IEEE.‏

Sulaiman, D. M., Abdulazeez, A. M., Haron, H., & Sadiq, S. S. (2019, April). Unsupervised Learning Approach-Based New Optimization K-Means Clustering for Finger Vein Image Localization. In 2019 International Conference on Advanced Science and Engineering (ICOASE) (pp. 82-87). IEEE.‏

Charbuty, B., & Abdulazeez, A. (2021). Classification Based on Decision Tree Algorithm for Machine Learning. Journal of Applied Science and Technology Trends, 2(01), 20-28.

Wendong, Y., Zhengzheng, L., & Bo, J. (2017, June). A multi-factor analysis model of quantitative investment based on GA and SVM. In 2017 2nd International Conference on Image, Vision and Computing (ICIVC) (pp. 1152-1155). IEEE.‏

Kareem, F. Q., & Abdulazeez, A. M. Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review.

- Zeebaree, Diyar Qader, Habibollah Haron, and Adnan Mohsin Abdulazeez. "Gene selection and classification of microarray data using convolutional neural network." In 2018 International Conference on Advanced Science and Engineering (ICOASE), pp. 145-150. IEEE, 2018.

Abdulqader, Dildar Masood, Adnan Mohsin Abdulazeez, and Diyar Qader Zeebaree. "Machine Learning Supervised Algorithms of Gene Selection: A Review." Machine Learning 62, no. 03 (2020).

Zebari, R., Abdulazeez, A., Zeebaree, D., Zebari, D., & Saeed, J. (2020). A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction. Journal of Applied Science and Technology Trends, 1(2), 56-70.‏

Dai, H. (2018, March). Research on SVM improved algorithm for large data classification. In 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA) (pp. 181-185). IEEE.

Haji, S. H., & Abdulazeez, A. M. (2021). COMPARISON OF OPTIMIZATION TECHNIQUES BASED ON GRADIENT DESCENT ALGORITHM: A REVIEW. PalArch's Journal of Archaeology of Egypt/Egyptology, 18(4), 2715-2743.

Tao, P., Sun, Z., & Sun, Z. (2018). An improved intrusion detection algorithm based on GA and SVM. Ieee Access, 6, 13624-13631.‏

Chauhan, V. K., Dahiya, K., & Sharma, A. (2019). Problem formulations and solvers in linear SVM: a review. Artificial Intelligence Review, 52(2), 803-855.‏

Ademujimi, T. T., Brundage, M. P., & Prabhu, V. V. (2017, September). A review of current machine learning techniques used in manufacturing diagnosis. In IFIP International Conference on Advances in Production Management Systems (pp. 407-415). Springer, Cham.

D. Q. Zeebaree, A. M. Abdulazeez, D. A. Zebari, H. Haron and H. Nuzly, "Multi-level fusion in ultrasound for cancer detection based on uniform lbp features," Computers, Materials & Continua, vol. 66, no.3, pp. 3363–3382, 2021.

Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE transactions on neural networks, 10(5), 988-999.

Yan, X., & Jia, M. (2018). A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing. Neurocomputing, 313, 47-64.‏

Wang, C., Zhang, Y., Song, J., Liu, Q., & Dong, H. (2019). A novel optimized SVM algorithm based on PSO with saturation and mixed time-delays for classification of oil pipeline leak detection. Systems Science & Control Engineering, 7(1), 75-88.‏

Fan, M., Wei, L., He, Z., Wei, W., & Lu, X. (2016). Defect inspection of solder bumps using the scanning acoustic microscopy and fuzzy SVM algorithm. Microelectronics Reliability, 65, 192-197.‏

Long, S., Huang, X., Chen, Z., Pardhan, S., & Zheng, D. (2019). Automatic detection of hard exudates in color retinal images using dynamic threshold and SVM classification: algorithm development and evaluation. BioMed research international, 2019.‏

Abd Elkarim, I. S., & Agbinya, J. (2019). A Review of Parallel Support Vector Machines (PSVMs) for Big Data classification. Australian Journal of Basic and Applied Sciences, 13(12), 61-71.

Li, Q., Du, X., Zhang, H., Li, M., & Ba, W. (2018, May). Liquid pipeline leakage detection based on moving windows LS-SVM algorithm. In 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) (pp. 701-705). IEEE.‏

Dai, C., Yang, J., Qin, Y., & Liu, J. (2016, October). Physical layer authentication algorithm based on SVM. In 2016 2nd IEEE International Conference on Computer and Communications (ICCC) (pp. 1597-1601). IEEE.

Sun, Z., Hu, K., Hu, T., Liu, J., & Zhu, K. (2018). Fast multi-label low-rank linearized SVM classification algorithm based on approximate extreme points. IEEE Access, 6, 42319-42326.‏

Batool, M., Jalal, A., & Kim, K. (2019, August). Sensors Technologies for Human Activity Analysis Based on SVM Optimized by PSO Algorithm. In 2019 International Conference on Applied and Engineering Mathematics (ICAEM) (pp. 145-150). IEEE.

Maulud, D., & Abdulazeez, A. M. (2020). A Review on Linear Regression Comprehensive in Machine Learning. Journal of Applied Science and Technology Trends, 1(4), 140-147.

Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y. A., & Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sensing, 12(7), 1135.

Al-Zebari, A., & Sengur, A. (2019, November). Performance Comparison of Machine Learning Techniques on Diabetes Disease Detection. In 2019 1st International Informatics and Software Engineering Conference (UBMYK) (pp. 1-4). IEEE.‏

Yi, Q., Wang, H., Guo, R., Li, S., & Jiang, Y. (2017). Laser ultrasonic quantitative recognition based on wavelet packet fusion algorithm and SVM. Optik, 149, 206-219.

Graser, J., Kauwe, S. K., & Sparks, T. D. (2018). Machine learning and energy minimization approaches for crystal structure predictions: A review and new horizons. Chemistry of Materials, 30(11), 3601-3612.

Khalaf, B. A., Mostafa, S. A., Mustapha, A., Mohammed, M. A., & Abduallah, W. M. (2019). Comprehensive review of artificial intelligence and statistical approaches in distributed denial of service attack and defense methods. IEEE Access, 7, 51691-51713.‏

Zhang, Y., Ni, M., Zhang, C., Liang, S., Fang, S., Li, R., & Tan, Z. (2019, May). Research and application of adaboost algorithm based on svm. In 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) (pp. 662-666). IEEE.‏

Dhabhai, A., & Gupta, Y. K. (2016). Empirical study of image classification techniques to classify the image using svm: A review. International Journal of Innovative Research in Computer and Communication Engineering, 4(10).‏

Tao, Q. Q., Zhan, S., Li, X. H., & Kurihara, T. (2016). Robust face detection using local CNN and SVM based on kernel combination. Neurocomputing, 211, 98-105.

Kumar, S., Singh, S., & Kumar, J. (2019). Multiple face detection using hybrid features with SVM classifier. In Data and Communication Networks (pp. 253-265). Springer, Singapore.

Kumar, S., Singh, S., & Kumar, J. (2018). Automatic live facial expression detection using genetic algorithm with haar wavelet features and SVM. Wireless Personal Communications, 103(3), 2435-2453.

Dino, H. I., & Abdulrazzaq, M. B. (2019, April). Facial expression classification based on SVM, KNN and MLP classifiers. In 2019 International Conference on Advanced Science and Engineering (ICOASE) (pp. 70-75). IEEE.

Azzopardi, G., Greco, A., & Vento, M. (2016, July). Gender recognition from face images using a fusion of svm classifiers. In International Conference on Image Analysis and Recognition (pp. 533-538). Springer, Cham.

Shi, L., Wang, X., & Shen, Y. (2020). Research on 3D face recognition method based on LBP and SVM. Optik, 220, 165157.

Dadi, H. S., & Pillutla, G. M. (2016). Improved face recognition rate using HOG features and SVM classifier. IOSR Journal of Electronics and Communication Engineering, 11(4), 34-44.

Chen, H., & Haoyu, C. (2019, May). Face Recognition Algorithm Based on VGG Network Model and SVM. In Journal of Physics: Conference Series (Vol. 1229, No. 1, p. 012015). IOP Publishing.

Aljanabi, M., Qutqut, H. M., & Hijjawi, M. (2018). Machine learning classification techniques for heart disease prediction: A review. International Journal of Engineering & Technology, 7(4), 5373-5379.

Caballé, N. C., Castillo-Sequera, J. L., Gómez-Pulido, J. A., Gómez-Pulido, J. M., & Polo-Luque, M. L. (2020). Machine learning applied to diagnosis of human diseases: A systematic review. Applied Sciences, 10(15), 5135.

Mahajan, S., Bangar, G., & Kulkarni, N. (2020). Machine Learning Algorithms for Classification of Various Stages of Alzheimer's Disease: A review. Machine Learning, 7(08).

Asuntha, A., Brindha, A., Indirani, S., & Srinivasan, A. (2016). Lung cancer detection using SVM algorithm and optimization techniques. J. Chem. Pharm. Sci, 9(4), 3198-3203.

Vadali, S., Deekshitulu, G. V. S. R., & Murthy, J. V. R. (2019). Analysis of liver cancer using data mining SVM algorithm in MATLAB. In Soft Computing for Problem Solving (pp. 163-175). Springer, Singapore.

Alam, J., Alam, S., & Hossan, A. (2018, February). Multi-stage lung cancer detection and prediction using multi-class svm classifie. In 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2) (pp. 1-4). IEEE.

Dolatabadi, A. D., Khadem, S. E. Z., & Asl, B. M. (2017). Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM. Computer methods and programs in biomedicine, 138, 117-126.

Qiao, Z., Zhang, Q., Dong, Y., & Yang, J. J. (2017, October). Application of SVM based on genetic algorithm in classification of cataract fundus images. In 2017 IEEE International Conference on Imaging Systems and Techniques (IST) (pp. 1-5). IEEE.

Nilashi, M., Ahmadi, N., Samad, S., Shahmoradi, L., Ahmadi, H., Ibrahim, O., ... & Yadegaridehkordi, E. (2020). Disease Diagnosis Using Machine Learning Techniques: A Review and Classification. Journal of Soft Computing and Decision Support Systems, 7(1), 19-30.

Nindrea, R. D., Aryandono, T., Lazuardi, L., & Dwiprahasto, I. (2018). Diagnostic accuracy of different machine learning algorithms for breast cancer risk calculation: a meta-analysis. Asian Pacific journal of cancer prevention: APJCP, 19(7), 1747.

Li, H. (2020). Text recognition and classification of english teaching content based on SVM. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-11.

Francis, L. M., & Sreenath, N. (2020). TEDLESS–Text detection using least-square SVM from natural scene. Journal of King Saud University-Computer and Information Sciences, 32(3), 287-299.

Wei, F., Qin, H., Ye, S., & Zhao, H. (2018, December). Empirical study of deep learning for text classification in legal document review. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 3317-3320). IEEE.

Hassan, A. K. A., & Kadhm, M. S. (2016). Arabic handwriting text recognition based on efficient segmentation, DCT and HOG features. International Journal of Multimedia and Ubiquitous Engineering, 11(10), 83-92.

Ismayilov, E. A. (2018). APPLICATION OF SVM AND SOFT FEATURES TO AZERBAIJANI TEXT RECOGNITION. ICTACT Journal on Image & Video Processing, 9(2).

Lin, W., Ji, D., & Lu, Y. (2017). Disorder recognition in clinical texts using multi-label structured SVM. BMC bioinformatics, 18(1), 75.

Hassan, A. K. A., Mahdi, B. S., & Mohammed, A. A. (2019). Arabic handwriting word recognition based on scale invariant feature transform and support vector machine. Iraqi Journal of Science, 381-387.

Sharma, S., Sasi, A., & Cheeran, A. N. (2017, May). A SVM based character recognition system. In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 1703-1707). IEEE.

Wawre, Suchita V., and Sachin N. Deshmukh. (2016)"Sentiment classification using machine learning techniques." International Journal of Science and Research (IJSR) 5.4: 819-821.

Singla, Z., Randhawa, S., & Jain, S. (2017, June). Sentiment analysis of customer product reviews using machine learning. In 2017 International Conference on Intelligent Computing and Control (I2C2) (pp. 1-5). IEEE.

Rahat, A. M., Kahir, A., & Masum, A. K. M. (2019, November). Comparison of Naive Bayes and SVM Algorithm based on sentiment analysis using review dataset. In 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART) (pp. 266-270). IEEE.

Bhavitha, B. K., Rodrigues, A. P., & Chiplunkar, N. N. (2017, March). Comparative study of machine learning techniques in sentimental analysis. In 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 216-221). IEEE.

Abro, S., Shaikh, S., Abro, R. A., Soomro, S. F., & Malik, H. M. (2020). Aspect Based Sentimental Analysis of Hotel Reviews: A Comparative Study. Sukkur IBA Journal of Computing and Mathematical Sciences, 4(1), 11-20.

Ahmad, M., Aftab, S., Muhammad, S. S., & Ahmad, S. (2017). Machine learning techniques for sentiment analysis: A review. Int. J. Multidiscip. Sci. Eng, 8(3), 27.

Elmurngi, E., & Gherbi, A. (2017, August). An empirical study on detecting fake reviews using machine learning techniques. In 2017 seventh international conference on innovative computing technology (INTECH) (pp. 107-114). IEEE.

Polpinij, J., Srikanjanapert, N., & Sopon, P. (2017, July). Word2Vec approach for sentiment classification relating to hotel reviews. In International Conference on Computing and Information Technology (pp. 308-316). Springer, Cham.

Lei, Y. (2017, October). Network anomaly traffic detection algorithm based on SVM. In 2017 International Conference on Robots & Intelligent System (ICRIS) (pp. 217-220). IEEE.

Jupin, J. A., Sutikno, T., Ismail, M. A., Mohamad, M. S., Kasim, S., & Stiawan, D. (2019). Review of the machine learning methods in the classification of phishing attack. Bulletin of Electrical Engineering and Informatics, 8(4), 1545-1555.

Emadi, H. S., & Mazinani, S. M. (2018). A novel anomaly detection algorithm using DBSCAN and SVM in wireless sensor networks. Wireless Personal Communications, 98(2), 2025-2035.

Gharaee, H., & Hosseinvand, H. (2016, September). A new feature selection IDS based on genetic algorithm and SVM. In 2016 8th International Symposium on Telecommunications (IST) (pp. 139-144). IEEE.

Yavanoglu, O., & Aydos, M. (2017, December). A review on cyber security datasets for machine learning algorithms. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 2186-2193). IEEE.

Wang, C., Zheng, J., & Li, X. (2017, October). Research on DDoS attacks detection based on RDF-SVM. In 2017 10th International Conference on Intelligent Computation Technology and Automation (ICICTA) (pp. 161-165). IEEE.

Tan, B., Tan, Y., & Li, Y. X. (2016). Research on Intrusion Detection System Based on Improved PSO-SVM Algorithm. Chemical Engineering Transactions, 51, 583-588.

Shruthi, U., Nagaveni, V., & Raghavendra, B. K. (2019, March). A review on machine learning classification techniques for plant disease detection. In 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS) (pp. 281-284). IEEE.

Chung, C. L., Huang, K. J., Chen, S. Y., Lai, M. H., Chen, Y. C., & Kuo, Y. F. (2016). Detecting Bakanae disease in rice seedlings by machine vision. Computers and electronics in agriculture, 121, 404-411.

Ebrahimi, M. A., Khoshtaghaza, M. H., Minaei, S., & Jamshidi, B. (2017). Vision-based pest detection based on SVM classification method. Computers and Electronics in Agriculture, 137, 52-58.

Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.

Silva, J. C. F., Teixeira, R. M., Silva, F. F., Brommonschenkel, S. H., & Fontes, E. P. (2019). Machine learning approaches and their current application in plant molecular biology: A systematic review. Plant Science, 284, 37-47.



How to Cite

Mustafa Abdullah, D., & Mohsin Abdulazeez, A. . (2021). Machine Learning Applications based on SVM Classification A Review. Qubahan Academic Journal, 1(2), 81–90.




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