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Integrating graphology and machine learning for accurate prediction of personality: a novel approach

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Abstract

The problems related to unique personality identification are faced by various fields like psychology, criminal justice, and forensics investigations when hiring employees and assigning responsibilities. Presently, this identification is made manually by appointing a graphologist, which involves a cost. A person’s unique personality is predicted using graphological analyses, which helps identify the person’s truth or uniqueness. This quality allows the various organisations to understand the person’s mental status, analyse the person’s handwriting, assign the work to newly appointed employees or existing employees, and save the employer’s time. The personality prediction is made using the person’s handwriting; the parameters taken from handwriting are the slant of the words, the margin left by the writer, the baseline, and the size of the letter. The proposed work gives optimal values of input parameters for better accuracy in personality prediction. The proposed solution is implemented using different steps, which include preprocessing and segmentation, extraction of image features, trait acquisition, training and testing of the model, and personality prediction. The proposed work solves the problems using a support vector machine with a classifier with an accuracy of 95.05% and the personality traits of the writer, which is better than existing work. Apart from this, in the image preprocessing steps, the optimal inversion value was 255 pixels. For dilation, the kernel value of 5100 was taken. For line segmentation of an image, the optimal anchor value lies between 5000 and 7000.

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References

  1. Alafif T et al (2022) GEAD: generating and evaluating handwritten eastern Arabic digits using generative adversarial networks. Int J Inf Technol 14(1):165–173. https://doi.org/10.1007/s41870-021-00814-8

    Article  Google Scholar 

  2. Asra S, Shubhangi D. C (2017) “Human behavior recognition based on hand written cursives by SVM classifier,” in 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), pp. 260–268. https://doi.org/10.1109/ICEECCOT.2017.8284679.

  3. Aulia, MR, Djamal EC (2020) “Personality Identification Based on Handwritten Signature Using Convolutional Neural Networks,” in Proceedings of the 5 th NA International Conference on Industrial Engineering and Operations Management, pp. 1761–1772

  4. Ayyadevara SA, Teja PNVSR, Kumar MR (2018) “Handwritten Character Recognition Using Unique Feature Extraction Technique,” in 2018 3rd IEEE international conference on recent trends in electronics, Information & Communication Technology (RTEICT), pp. 1239–1243. https://doi.org/10.1109/RTEICT42901.2018.9012248

  5. Bandhu KC, Litoriya R, Bag M, Barwaniwala A, Garg M (2022) “Blockchain and smart contract enabled smart and secure electronic voting system,” Int J Electron Gov

  6. Bandhu KC, Litoriya R, Lowanshi P, Jindal M, Chouhan L, Jain S (2022) “Making drug supply chain secure traceable and efficient: a Blockchain and smart contract based implementation,” Multimed Tools Appl, https://doi.org/10.1007/s11042-022-14238-4

  7. Başaran S, Ejimogu OH (2021) A neural network approach for predicting personality from Facebook data. SAGE Open 11(3):215824402110321. https://doi.org/10.1177/21582440211032156

    Article  Google Scholar 

  8. Champa HN, AnandaKumar KR (2010) “Automated Human Behavior Prediction through Handwriting Analysis,” in 2010 First International Conference on Integrated Intelligent Computing, pp. 160–165. https://doi.org/10.1109/ICIIC.2010.29

  9. Champa HN, AnandaKumar DKR (2010) Artificial neural network for human behavior prediction through handwriting analysis. Int J Comput Appl 2(2):36–41. https://doi.org/10.5120/629-878

    Article  Google Scholar 

  10. Chandra MA, Bedi SS (2021) Survey on SVM and their application in image classification. Int J Inf Technol 13(5):1–11. https://doi.org/10.1007/s41870-017-0080-1

    Article  Google Scholar 

  11. Chitlangia A, Malathi G (2019) Handwriting analysis based on histogram of oriented gradient for predicting personality traits using SVM. Procedia Comput Sci 165:384–390. https://doi.org/10.1016/j.procs.2020.01.034

    Article  Google Scholar 

  12. Computer Vision and Artificial Intelligence (n.d.) “IAM Handwriting Database,” fki. https://fki.tic.heia-fr.ch/databases/iam-handwriting-database. Accessed 15 Feb 2022

  13. Durga L, Deepu R (2021) Ensemble deep learning to classify specific types of t and i patterns in graphology. Glob Transit Proc 2(2):287–293. https://doi.org/10.1016/j.gltp.2021.08.025

    Article  Google Scholar 

  14. Farooq H, Naaz S (2020) Performance analysis of biometric recognition system based on palmprint. Int J Inf Technol 12(4):1281–1289. https://doi.org/10.1007/s41870-018-0230-0

    Article  Google Scholar 

  15. Garg A, Jindal MK, Singh A (2022) Degraded offline handwritten Gurmukhi character recognition: study of various features and classifiers. Int J Inf Technol 14(1):145–153. https://doi.org/10.1007/s41870-019-00399-3

    Article  Google Scholar 

  16. Gavrilescu M, Vizireanu N (2018) Predicting the big five personality traits from handwriting. EURASIP J Image Video Process 2018(1):57. https://doi.org/10.1186/s13640-018-0297-3

    Article  Google Scholar 

  17. Ghosh S (2021) Identifying click baits using various machine learning and deep learning techniques. Int J Inf Technol 13(3):1235–1242. https://doi.org/10.1007/s41870-020-00473-1

    Article  Google Scholar 

  18. Gupta R Pandey P, Litoriya R Srivastava S (2022) “Ethical and Legal Aspectss of Using Blockchain Technology for 5G-based Healthcare Systems,” in Blockchain for 5G Healthcare Applications Security and privacy solutions , S. Tanwar, Ed. IET, pp. 35–47

  19. Jain V, Jain A, Chauhan A, Kotla SS, Gautam A (2021) American sign language recognition using support vector machine and convolutional neural network. Int J Inf Technol 13(3):1193–1200. https://doi.org/10.1007/s41870-021-00617-x

    Article  Google Scholar 

  20. Joseph FJJ (2020) Effect of supervised learning methodologies in offline handwritten Thai character recognition. Int J Inf Technol 12(1):57–64. https://doi.org/10.1007/s41870-019-00366-y

  21. Joshi P, Agarwal A, Dhavale A, Suryavanshi R, Kodolikar S (2015) Handwriting analysis for detection of personality traits using machine learning approach. Int J Comput Appl 130(15):40–45. https://doi.org/10.5120/ijca2015907189

  22. Khanday AMUD, Khan QR, Rabani ST (2021) Identifying propaganda from online social networks during COVID-19 using machine learning techniques. Int J Inf Technol 13(1):115–122. https://doi.org/10.1007/s41870-020-00550-5

    Article  Google Scholar 

  23. Litoriya R, Soner S, Lovanshi M (2020) Preventing road accidents caused by drunk driving using Machine Learning & Internet of Things. Int J Adv Sci Technol 29(3):11060–11070. http://sersc.org/journals/index.php/IJAST/article/view/28001

  24. Litoriya R, Arora A, Bajaj R, Gulati A (2022) “Adoption of Blockchain Technology in the Indian Business Market: Obstacles and Opportunities,” pp. 211–236. https://doi.org/10.1007/978-3-030-89546-4_11

  25. Malviya S, Dave S, Bandhu KC, Litoriya R (n.d.) “A cryptographic security mechanism for dynamic groups for public cloud environments,” J Autom Mob Robot Intell Syst

  26. Niranjan D, Kavya M, Neethi KT, Prarthan KM, Manjuprasad B (2021) Machine learning based analysis of pulse rate using Panchamahabhutas and Ayurveda. Int J Inf Technol 13(4):1667–1670. https://doi.org/10.1007/s41870-021-00690-2

    Article  Google Scholar 

  27. Oliveira LS, Justino E, Freitas C, Sabourin R (n.d.) “The Graphology Applied to Signature Verification”

  28. Pandey P, Litoriya R (2020) Fuzzy cognitive mapping analysis to recommend machine learning based effort estimation technique for web applications. Int J Fuzzy Syst 22(4):1212–1223. https://doi.org/10.1007/s40815-020-00815-y

    Article  Google Scholar 

  29. Pandey P, Litoriya R (2020) An IoT assisted system for generating emergency alerts using routine analysis. Wirel Pers Commun 112(1):607–630. https://doi.org/10.1007/s11277-020-07064-0

    Article  Google Scholar 

  30. Pandey M, Litoriya R, Pandey P (2019) Novel approach for Mobile based app development incorporating MAAF. Wirel Pers Commun 107:1687–1708. https://doi.org/10.1007/s11277-019-06351-9

    Article  Google Scholar 

  31. Pandey M, Litoriya R, Pandey P (2019) Validation of existing software effort estimation techniques in context with Mobile software applications. Wirel Pers Commun 110:1659–1677. https://doi.org/10.1007/s11277-019-06805-0

    Article  Google Scholar 

  32. Pandey M, Litoriya R, Pandey P (2019) Novel approach for Mobile based app development incorporating MAAF. Wirel Pers Commun 107(4):1687–1708. https://doi.org/10.1007/s11277-019-06351-9

    Article  Google Scholar 

  33. Patil V, Mathur H (2020) “A Survey: Machine Learning Approach for Personality Analysis and Writer Identification through Handwriting,” in 2020 International Conference on Inventive Computation Technologies (ICICT), pp. 1–5. https://doi.org/10.1109/ICICT48043.2020.9112449.

  34. Patil AR, Subbaraman S (2021) Performance analysis of static hand gesture recognition approaches using artificial neural network, support vector machine and two stream based transfer learning approach. Int J Inf Technol 14:3781–3792. https://doi.org/10.1007/s41870-021-00831-7.

  35. Prasad S, Singh VK, Sapre A (2010) Handwriting analysis based on segmentation method for prediction of human personality using support vector machine. Int J Comput Appl 8(12):25–29. https://doi.org/10.5120/1256-1758

    Article  Google Scholar 

  36. Revathi A, Kaladevi R, Ramana K, Jhaveri RH, Rudra Kumar M, Kumar MSP (2022) Early Detection of Cognitive Decline Using Machine Learning Algorithm and Cognitive Ability Test. Secur Commun Netw 2022:1–13. https://doi.org/10.1155/2022/4190023

    Article  Google Scholar 

  37. Sharma N, Litoriya R, Sharma D, Singh HP (2019) Designing a Decision Support Framework for Municipal Solid Waste Management. Int J Emerg Technol 10(4):374–379

    Google Scholar 

  38. Sharma N, Litoriya R, Sharma A (2021) “Application and analysis of K-means algorithms on a decision support framework for municipal solid waste management,” in Advanced machine learning technologies and applications. AMLTA 2020. Advances in intelligent systems and computing, H. A., B. R., and D. A., Eds. Springer, Singapore, pp. 267–276. https://doi.org/10.1007/978-981-15-3383-9_24

  39. Singh K, Kumar S, Kaur P (2019) Support vector machine classifier based detection of fungal rust disease in pea plant (Pisam sativam). Int J Inf Technol 11(3):485–492. https://doi.org/10.1007/s41870-018-0134-z

    Article  Google Scholar 

  40. Soner S, Litoriya R, Pandey P (2021) Exploring Blockchain and smart contract Technology for Reliable and Secure Land Registration and record management. Wirel Pers Commun 121(1):2495–2509. https://doi.org/10.1007/s11277-021-08833-1

    Article  Google Scholar 

  41. Soner S, Litoriya R, Pandey P, “Integrating Blockchain technology with IoT and ML to avoid road accidents caused by drunk driving,” Wirel Pers Commun, 2022, https://doi.org/10.1007/s11277-022-09695-x.

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Authors and Affiliations

  1. Medi-Caps University, Indore, India

    Kailash Chandra Bandhu, Ratnesh Litoriya, Mihir Khatri, Milind Kaul & Prakhar Soni

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  1. Kailash Chandra Bandhu
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  2. Ratnesh Litoriya
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Correspondence to Ratnesh Litoriya.

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Bandhu, K.C., Litoriya, R., Khatri, M. et al. Integrating graphology and machine learning for accurate prediction of personality: a novel approach. Multimed Tools Appl 82, 46457–46481 (2023). https://doi.org/10.1007/s11042-023-15567-8

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