Skip to main content

Advertisement

SpringerLink
Log in

Automation of human behaviors and its prediction using machine learning

  • Technical Paper
  • Published:
Microsystem Technologies Aims and scope Submit manuscript

Abstract

Prediction is a method of detecting a person's behavior toward online buying by evaluating publically available evaluations on the web. Understanding expressive human communication involves a simultaneous examination of speech and gestures since human behavior is communicated through a combination of verbal and nonverbal channels. Machine learning algorithms are utilized in this work to extract evaluations from the net and categorize these into five classes, namely, highly favorable, favorable, neutrality, bad, and strongly negative, for the prediction of human behavior. A person's behavior is analyzed, and the experimental comparison is made to machine learning methodologies. Numerous classifiers are employed on manuscript transcripts in this work to determine the accuracy, exactness, recollection, and f1-score, which are also represented in terms of muddle matrices. In this research studies a new technique is proposed using Ludwig classifier and it has been discovered that deep learning employing the Ludwig classifier achieves near-perfect accuracy about 99.9%. The outcomes are presented to demonstrate the preceding point.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Alazab M, Venkatraman S, Watters P, Alazab M et al (2011) Zero-day malware detection based on supervised learning algorithms of API call signatures. In: Proceedings of the 9-th Australasian Data Mining Confere (AusDM'11), vol 121. Australian Computer Society Inc., Ballarat, Australia, pp 171–182. https://doi.org/10.5555/2483628.2483648. ISBN: 9781921770029

    Chapter  Google Scholar 

  • Alkali Y, Routray I, Whig P (2022) Study of various methods for reliable, efficient and Secured IoT using Artificial Intelligence 1(2):1–7. Available at SSRN 4020364

  • Anand M, Velu A, Whig P (2022) Prediction of loan behaviour with machine learning models for secure banking. J Comput Sci Eng (JCSE) 3(1):1–13

    Article  Google Scholar 

  • Arun Velu PW (2021) Impact of Covid Vaccination on the Globe using data analytics. Int J Sustain Dev Comput Sci 3(2):1–13

    Google Scholar 

  • Asopa P, Purohit P, Nadikattu RR, Whig P (2021) Reducing carbon footprint for sustainable development of smart cities using IoT. In: 2021 Third International Conference on intelligent communication technologies and virtual mobile networks (ICICV), vol 5, pp 361–367

  • Av-test institute, Germany (2019) https://www.av-test.org/en/statistics/malware/ 2(5): 1–10. Accessed 20 Oct 2019

  • Bhargav R, Whig P (2021) More insight on data analysis of titanic data set. Int J Sustain Dev Comput Sci 3(4):1–10

    Google Scholar 

  • Bhatia V, Whig P (2013a) Modeling and simulation of electrical load control system using RF technology. Int J Multidiscipl Sci Eng 4(2):44–47

    Article  Google Scholar 

  • Bhatia V, Whig P (2013b) A secured dual tune multi frequency-based smart elevator control system. Int J Res Eng Adv Technol 4(1):2319–1163

    Google Scholar 

  • Chopra G, Whig P (2022a) Energy efficient scheduling for internet of vehicles. Int J Sustain Dev Comput Sci 4(1):1–10

    Google Scholar 

  • Chopra G, Whig P (2022b) A clustering approach based on support vectors. Int J Mach Learn Sustain Dev 4(1):21–30

    Google Scholar 

  • Fischer EA (2014) Cybersecurity issues and challenges: in brief. Congr Res Serv 5:1–10

    Google Scholar 

  • George N, Muiz K, Whig P, Velu A (2021) Framework of perceptive artificial intelligence using natural language processing (PAIN) 1–10. Artif Comput Intell/Publ Online

  • Gupta BB, Tewari A, Jain AK, Agrawal DP (2017) Fighting against phishing attacks: state of the art and future challenges. Neural Comput Appl 28(12):3629–3654

    Article  Google Scholar 

  • IBM Security Report (2019) https://www.ibm.com/security/data-breach 2(3):1–10. Accessed 20 Oct 2019

  • Jiwani N, Gupta K, Whig P (2021) Novel healthcare framework for cardiac arrest with the application of AI using ANN. In: 2021 5th International Conference on information systems and computer networks (ISCON), vol 2, no 5, pp 1–5

  • Juniper Research (2019) https://www.juniperresearch.com/ 1(2):1–15. Accessed 20 Oct 2019

  • Li S, Da Xu L, Zhao S (2015) The internet of things: a survey. Inform System Front 17(2):243–259

    Article  Google Scholar 

  • Mamza ES (2021) Use of AIOT in health system. Int J Sustain Dev Comput Sci 3(4):21–30

    Google Scholar 

  • McIntosh T, Jang-Jaccard J, Watters P, Susnjak T (2019) The inadequacy of entropy-based ransomware detection. In: International Conference on neural information processing. Springer, New York, pp 181–189

    Chapter  Google Scholar 

  • Nadikattu RR, Bhandari R, Whig P (2021) Improved pattern of adaptive rood-pattern search algorithm for motion estimation in video compression. In: Singh J, Kumar S, Choudhury U (eds) Innovations in cyber physical systems, Lecture Notes in Electrical Engineering, vol 788. Springer, Singapore, pp 441–448. https://doi.org/10.1007/978-981-16-4149-7_39

    Chapter  Google Scholar 

  • Rainie L, Anderson J, Connolly J (2014) Cyber attacks likely to increase. Digital Life 2025 5(2):1–25

    Google Scholar 

  • Ruchin CM, Whig P (2015) Design and simulation of dynamic UART using scan path technique (USPT). Int J Electric Electron Comput Sci Eng 10(2):1–14

    Google Scholar 

  • Sharma A, Kumar A, Whig P (2015) On the performance of CDTA based novel analog inverse low pass filter using 0.35 µm CMOS parameter. Int J Sci Technol Manag 4(1):594–601

    Google Scholar 

  • Shaw A (2009) Data breach: from notification to prevention using PCI DSS. Colum Soc Probs 43:517–524

    Google Scholar 

  • Sinha R, Prashar S, Whig P (2015) Effect of output error on fuzzy interface for VDRC of second-order systems. Int J Comput Appl 125(13):11–16

    Google Scholar 

  • Sun N, Zhang J, Rimba P, Gao S, Zhang LY, Xiang Y (2018) Data-driven cybersecurity incident prediction: a survey. IEEE Commun Surv Tutor 21(2):1744–1772

    Article  Google Scholar 

  • Velu A, Whig P (2021) Protect personal privacy and wasting time using Nlp: a comparative approach using Ai. Vivekananda J Res 10:42–52

    Google Scholar 

  • Velu A, Whig P (2022) Studying the impact of the COVID vaccination on the world using data analytics. Vivekananda J Res 10(1):147–160

    Google Scholar 

  • Whig P (2019a) Machine learning based techniques for communication and signal processing problem. Int J Mach Learn Sustain Dev 1(3):1–10

    Google Scholar 

  • Whig P (2019b) Prediction of smart building indoor temperature using IoT and machine learning. Int J Mach Learn Sustain Dev 1(4):1–10

    Google Scholar 

  • Whig P, Ahmad SN (2012) Performance analysis of various readout circuits for monitoring quality of water using analog integrated circuits. Int J Intell Syst Appl (IJISA) 4(11):91–98

    Google Scholar 

  • Whig P, Ahmad SN (2013a) A novel pseudo PMOS integrated CC-ISFET device for water quality monitoring. J Integr Circ Syst Publ 8(2):1–6

    Google Scholar 

  • Whig P, Ahmad SN (2013b) A Novel Pseudo NMOS Integrated ISFET device for water quality monitoring. Active Passive Compon 1(1):258

    Google Scholar 

  • Whig P, Ahmad SN (2016a) Simulation and performance analysis of low power quasi floating gate PCS model. Int J Intell Eng Syst 9(2):8–13

    Google Scholar 

  • Whig P, Ahmad SN (2016b) Ultraviolet photo catalytic oxidation (UVPCO) sensor for air and surface sanitizers using CS amplifier. Glob J Res Eng 16(6):1–13

    Google Scholar 

  • Whig P, Ahmad SN (2014a) Simulation of linear dynamic macro model of photo catalytic sensor in SPICE. Compel Int J Comput Math Electric Electron Eng 33(1/2):611–629

    Article  Google Scholar 

  • Whig P, Ahmad SN (2014b) Development of economical ASIC for PCS for water quality monitoring. J Circ Syst Comput 23(6):1–13

    Article  Google Scholar 

  • Whig P, Rupani A (2020) Novel economical social distancing smart device for COVID19. Int J Electric Eng Technol 2(1):1–10

    Google Scholar 

  • Whig P, Nadikattu RR, Velu A (2022) COVID-19 pandemic analysis using application of AI. In: Healthcare monitoring and data analysis using IoT: technologies and applications, vol 10, no 1, pp 1–15

Download references

Author information

Authors and Affiliations

  1. Sardar Vallabhbhai National Institute of Technology, Surat, India

    Hruthika Jupalle

  2. Department of Computer Science, Jazan University, Jazan, Kingdom of Saudi Arabia

    Shama Kouser

  3. Vivekananda Institute of Professional Studies Technical Campus, New Delhi, India

    Ashima Bhatnagar Bhatia & Pawan Whig

  4. Department of Computer Science, Jamia Hamdard, New Delhi, India

    Naved Alam

  5. University of the Cumberland, Williamsburg, USA

    Rahul Reddy Nadikattu

Authors
  1. Hruthika Jupalle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shama Kouser
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashima Bhatnagar Bhatia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Naved Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rahul Reddy Nadikattu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Pawan Whig
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pawan Whig.

Ethics declarations

Conflict of interest

Author A has received research grants from Company A. Author B has received a speaker honorarium from Company X and owns stock in Company Y. Author C is a member of committee Z. OR if no conflict exists: Author A declares that he/she has no conflict of interest. Author B declares that he/she has no conflict of interest. There is no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jupalle, H., Kouser, S., Bhatia, A.B. et al. Automation of human behaviors and its prediction using machine learning. Microsyst Technol 28, 1879–1887 (2022). https://doi.org/10.1007/s00542-022-05326-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00542-022-05326-4

Search

Navigation