Multimedia Tools and Applications

, Volume 77, Issue 17, pp 22875–22896 | Cite as

Cat Swarm Optimization applied to alcohol use disorder identification

  • Yu-Dong ZhangEmail author
  • Yuxiu SuiEmail author
  • Junding SunEmail author
  • Guihu ZhaoEmail author
  • Pengjiang QianEmail author


(Aim) Alcohol use disorder may put health at risk and cause serious health problems. It is of increasing importance to identify alcohol use disorder as early as possible. (Method) This study proposed a computer-vision based technique. The dataset was scanned by magnetic resonance imaging in China participating hospitals. Afterwards, we combined wavelet entropy, two-layer feedforward neural network, and cat swarm optimization (CSO). The CSO mimics the behavior of cat and is composed of two modes: seeking mode and tracing mode. (Results) The results showed that our method achieves a sensitivity of 91.84 ± 1.66%, a specificity of 92.40 ± 1.22%, and an accuracy of 92.13 ± 0.70%. Using grid searching approach, we found the classification performance is the best, when decomposition level is assigned with 2 and the mixture ratio is assigned with a value of 0.8. (Conclusion) The CSO is superior to four bioinspired algorithms: genetic algorithm, immune genetic algorithm, particle swarm optimization, and chaotic self-adaptive particle swarm optimization. In addition, our proposed alcoholism identification system is superior to four state-of-the-art alcoholism detection approaches.


Alcohol user disorder Pattern recognition Cat swarm optimization Two-layer feedforward neural network Wavelet entropy Magnetic resonance imaging Cross validation 



This paper is financially supported by Natural Science Foundation of China (61602250), Natural Science Foundation of Jiangsu Province (BK20150983), Project of Science and Technology of Henan Province (172102210272), Program for Science & Technology Innovation Talents of Henan Province (174100510009), Open fund for Jiangsu Key Laboratory of Advanced Manufacturing Technology (HGAMTL1601, HGAMTL-1703), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01), Open Fund of Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology (17-259-05-011 K)., Henan Key Research and Development Project (182102310629), National key research and development plan (2017YFB1103202)

Compliance with ethical standards

Conflict of interest

The authors declare there is no conflict of interest with regard to this submission.


  1. 1.
    Abou El-Ela AA et al. (2016) Optimal placement and sizing of distributed generation units using different cat swarm optimization algorithms. in 18th Eighteenth International Middle East Power Systems Conference (MEPCON). Cairo, EGYPT: IEEE. p. 975–981Google Scholar
  2. 2.
    Alweshah M, Abdullah S (2015) Hybridizing firefly algorithms with a probabilistic neural network for solving classification problems. Appl Soft Comput 35:513–524CrossRefGoogle Scholar
  3. 3.
    Atangana A (2018) Application of stationary wavelet entropy in pathological brain detection. Multimed Tools Appl 77(3):3701–3714CrossRefGoogle Scholar
  4. 4.
    de Carvalho CAM et al (2016) Morphological and immunohistochemical analysis of apoptosis in the cerebellum of rats subjected to focal cerebral ischemia with or without alcoholism model. Acta Cir Bras 31(9):629–637CrossRefGoogle Scholar
  5. 5.
    Chen Y (2017) A Feature-Free 30-Disease Pathological Brain Detection System by Linear Regression Classifier. CNS Neurol Disord Drug Targets 16(1):5–10CrossRefGoogle Scholar
  6. 6.
    Chen Y, Chen X-Q (2016) Sensorineural hearing loss detection via discrete wavelet transform and principal component analysis combined with generalized eigenvalue proximal support vector machine and Tikhonov regularization. Multimed Tools Appl 77(3):3775–3793CrossRefGoogle Scholar
  7. 7.
    Chen Y, Lu H (2018) Wavelet energy entropy and linear regression classifier for detecting abnormal breasts. Multimed Tools Appl 77(3):3813–3832CrossRefGoogle Scholar
  8. 8.
    Chen S, Yang J-F, Phillips P (2015) Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine. Int J Imaging Syst Technol 25(4):317–327CrossRefGoogle Scholar
  9. 9.
    Chen RM, Yang SC, Wang CM (2017) MRI brain tissue classification using unsupervised optimized extenics-based methods. Comput Electr Eng 58:489–501CrossRefGoogle Scholar
  10. 10.
    Chu SC, Tsai PW, Pan JS (2006) Cat Swarm Optimization. in 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI). Guilin, P R CHINA: Springer-Verlag Berlin. p. 854–858Google Scholar
  11. 11.
    Darvishvand L, Kamkari B, Kowsary F (2018) Optimal design approach for heating irregular-shaped objects in three-dimensional radiant furnaces using a hybrid genetic algorithm-artificial neural network method. Eng Optim 50(3):452–470MathSciNetCrossRefGoogle Scholar
  12. 12.
    Du S (2016) Multi-objective path finding in stochastic networks using a biogeography-based optimization method. SIMULATION 92(7):637–647CrossRefGoogle Scholar
  13. 13.
    Fabijanska A (2017) Corneal Endothelium Image Segmentation Using Feedforward Neural Network. in Federated Conference on Computer Science and Information Systems (FedCSIS). Prague, Czech Republic: IEEE. p. 629–637Google Scholar
  14. 14.
    Feng JH et al (2017) A novel chaos optimization algorithm. Multimed Tools Appl 76(16):17405–17436CrossRefGoogle Scholar
  15. 15.
    Gao ML et al (2016) Research of resistivity imaging using neural network based on immune genetic algorithm. Chin J Geophys-Chin Ed 59(11):4372–4382Google Scholar
  16. 16.
    Gupta L et al (2017) Wavelet Entropy of BOLD Time Series: An Application to Rolandic Epilepsy. J Magn Reson Imaging 46(6):1728–1737CrossRefGoogle Scholar
  17. 17.
    Han L (2018) Identification of Alcoholism based on wavelet Renyi entropy and three-segment encoded Jaya algorithm. Complexity 2018:3198184MathSciNetzbMATHGoogle Scholar
  18. 18.
    Hou X-X (2017) Alcoholism detection by medical robots based on Hu moment invariants and predator-prey adaptive-inertia chaotic particle swarm optimization. Comput Electr Eng 63:126–138CrossRefGoogle Scholar
  19. 19.
    Huo YK et al (2017) Simultaneous total intracranial volume and posterior fossa volume estimation using multi-atlas label fusion. Hum Brain Mapp 38(2):599–616MathSciNetCrossRefGoogle Scholar
  20. 20.
    Huo YK et al (2018) Robust Multicontrast MRI Spleen Segmentation for Splenomegaly Using Multi-Atlas Segmentation. IEEE Trans Biomed Eng 65(2):336–343CrossRefGoogle Scholar
  21. 21.
    Jenitta A, Ravindran RS (2017) Image Retrieval Based on Local Mesh Vector Co-occurrence Pattern for Medical Diagnosis from MRI Brain Images. J Med Syst 41(10):157CrossRefGoogle Scholar
  22. 22.
    Jia W (2017) Three-Category Classification of Magnetic Resonance Hearing Loss Images Based on Deep Autoencoder. J Med Syst 41(10):165CrossRefGoogle Scholar
  23. 23.
    Jiang Y (2017) Exploring a smart pathological brain detection method on pseudo Zernike moment. Multime Tools Appl.
  24. 24.
    Jiang YY (2017) Cerebral Micro-Bleed Detection Based on the Convolution Neural Network With Rank Based Average Pooling. IEEE Access 5:16576–16583CrossRefGoogle Scholar
  25. 25.
    Kumar Y, Sahoo G (2017) An Improved Cat Swarm Optimization Algorithm Based on Opposition-Based Learning and Cauchy Operator for Clustering. J Inf Process Syst 13(4):1000–1013Google Scholar
  26. 26.
    Li MS et al (2013) Solubility Prediction of Gases in Polymers based on Chaotic Self-adaptive Particle Swarm Optimization Artificial Neural Networks. Acta Chim Sin 71(7):1053–1058CrossRefGoogle Scholar
  27. 27.
    Li G et al (2018) Modeling of ash agglomerating fluidized bed gasifier using back propagation neural network based on particle swarm optimization. Appl Therm Eng 129:1518–1526CrossRefGoogle Scholar
  28. 28.
    Liu SG et al (2016) A novel label learning algorithm for face recognition. Signal Process 124:141–146CrossRefGoogle Scholar
  29. 29.
    Liu SG et al (2017) Improved sparse representation method for image classification. IET Comput Vis 11(4):319–330CrossRefGoogle Scholar
  30. 30.
    Lu S, Lu Z (2018) A pathological brain detection system based on kernel based ELM. Multimed Tools Appl 77(3):3715–3728CrossRefGoogle Scholar
  31. 31.
    Monnig MA (2012) Observed power and projected sample sizes to detect white matter atrophy in neuroimaging of alcohol use disorders. Alcohol-Clin Exp Res 36:272A–272ACrossRefGoogle Scholar
  32. 32.
    Mostafa A et al (2017) Liver segmentation in MRI images based on whale optimization algorithm. Multimed Tools Appl 76(23):24931–24954CrossRefGoogle Scholar
  33. 33.
    Murano T, Hagihara H, Miyakawa T (2016) Transcriptomic immaturity of hippocampus and prefrontal cortex in patients with alcoholism. Int J Neuropsychopharmacol 19:170–171Google Scholar
  34. 34.
    Nie XH, Wang W, Nie HY (2017) Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT. Computational Intelligence and Neuroscience, Article ID. 1583847Google Scholar
  35. 35.
    Ong HH et al (2018) Genetic polymorphisms of alcohol-metabolizing enzymes and their association with alcoholism risk, personality and anthropometric traits among Malaysian university students. Psychol Health Med 23(2):160–170CrossRefGoogle Scholar
  36. 36.
    Pan H, Zhang C, Tian Y (2014) RGB-D image-based detection of stairs, pedestrian crosswalks and traffic signs. J Vis Commun Image Represent 25(2):263–272CrossRefGoogle Scholar
  37. 37.
    Piersanti S, Orlandi A (2018) Genetic Algorithm Optimization for the Total Radiated Power of a Meandered Line by Using an Artificial Neural Network. IEEE Trans Electromagn Compat 60(4):1014–1017CrossRefGoogle Scholar
  38. 38.
    Shahrabi J, Khameneh SM (2017) Development of a hybrid system of artificial neural networks and artificial bee colony algorithm for prediction and modeling of customer choice in the market. J Fundam Appl Sci 9:154–183CrossRefGoogle Scholar
  39. 39.
    Shoaib M et al (2018) A Comparative Study of Various Hybrid Wavelet Feedforward Neural Network Models for Runoff Forecasting. Water Resour Manag 32(1):83–103CrossRefGoogle Scholar
  40. 40.
    Subramaniam S, Radhakrishnan M (2016) Neural Network with Bee Colony Optimization for MRI Brain Cancer Image Classification. Int Arab J Inf Technol 13(1):118–124Google Scholar
  41. 41.
    Sun P (2016) Preliminary research on abnormal brain detection by wavelet-energy and quantum-behaved PSO. Technol Health Care 24(s2):S641–S649CrossRefGoogle Scholar
  42. 42.
    Wei L, Yang J (2016) Fitness-scaling adaptive genetic algorithm with local search for solving the Multiple Depot Vehicle Routing Problem. SIMULATION 92(7):601–616CrossRefGoogle Scholar
  43. 43.
    Wolber N et al (2018) The Simplest Idea Is the Best Idea. J Neurosci Nurs 50(1):22–23Google Scholar
  44. 44.
    Xun YQ et al. (2016) Ant Colony Based on Cat Swarm Optimization and Application in Picking Robot Path Planning. in 7th International Conference on Software Engineering and Service Science. Beijing, China: IEEE. p. 162–165Google Scholar
  45. 45.
    Yang J (2017) Pathological brain detection in MRI scanning via Hu moment invariants and machine learning. J Exp Theor Artif Intell 29(2):299–312CrossRefGoogle Scholar
  46. 46.
    Zhan TM, Chen Y (2016) Multiple Sclerosis Detection Based on Biorthogonal Wavelet Transform, RBF Kernel Principal Component Analysis, and Logistic Regression. IEEE Access 4:7567–7576MathSciNetCrossRefGoogle Scholar
  47. 47.
    Zhou X-X et al (2016) Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection. SIMULATION 92(9):827–837CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHenan Polytechnic UniversityJiaozuoPeople’s Republic of China
  2. 2.Department of InformaticsUniversity of LeicesterLeicesterUK
  3. 3.Department of PsychiatryNanjing Medical UniversityNanjingChina
  4. 4.National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaPeople’s Republic of China
  5. 5.School of Digital MediaJiangnan UniversityWuxiChina

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