Skip to main content
SpringerLink
Log in

Efficacy of Artificial Neural Networks (ANN) as a Tool for Predictive Analytics

  • Chapter
  • First Online:
Analytics Enabled Decision Making

  • 419 Accesses

ABSTRACT

Predictive analytics could also be defined as the application of statistical techniques and mathematical modeling to anticipate the future performance and expected return on investments. Predictive analytics examines the most recent and the historical data to see if the same pattern is likely to reoccur or not. This gives an opportunity to businessmen and financial investors to make an appropriate decision about their investments and expected returns. Ever since the development of ANN technique, researchers have tried to create a number of predictive models using ANN. The chapter is focused on defining predictive analytics and the tools used in predictive analytics, with a special orientation on Artificial Neural Networks. The objective of the chapter is to establish ANN as an effective technique for making appropriate predictions and thereby contributing toward the decision-making in various spheres using the outcomes from various researches. The chapter also aims to explain the step-by-step process of ANN in outcome prediction with the help of example.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Al-Shayea, Q. K., El-Refae, G. A., & El-Itter, S. F. (2010). Neural networks in bank insolvency prediction. International Journal of Computer Science and Network Security (IJCSNS), 10(5), 240–245.

    Google Scholar 

  • Anyaeche, C. O., & Ighravwe, D. E. (2013). Predicting performance measures using linear regression and neural network: A comparison. African Journal of Engineering Research, 1(3), 84–89.

    Google Scholar 

  • Chiang, W. C., Enke, D., Wu, T., & Wang, R. (2016). An adaptive stock index trading decision support system. Expert Systems with Applications, 59, 195–207.

    Article  Google Scholar 

  • Chien, C. F., & Chen, L. F. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications, 34(1), 280–290.

    Article  Google Scholar 

  • Dagmar, B., & TreÅ¡l, J. (2011). Financial forecasting using neural networks. China-USA Business Review, 10(3), 169–175.

    Google Scholar 

  • Golnaraghi, S., Zangenehmadar, Z., Moselhi, O., & Alkass, S. (2019). Application of Artificial Neural Network(s) in predicting formwork labour productivity. Artificial Intelligence Applications in Civil Engineering. https://doi.org/10.1155/2019/5972620

    Article  Google Scholar 

  • Gupta, A., & Sarangi, P. K. (2012). Electrical load forecasting using genetic algorithm based back-propagation method. Journal of Engineering and Applied Sciences, 7(8), 1017–1020.

    Google Scholar 

  • Huang, M.-J., Tsou, Y.-L., & Lee, S.-C. (2006). Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge. Knowledge-Based Systems, 19(6), 396–403.

    Article  Google Scholar 

  • Jantan, H., Hamdan, A., & Othman, Z. (2009). Knowledge discovery techniques for talent forecasting in human resource application. World Academy of Science, Engineering and Technology, 3(2), 620–628.

    Google Scholar 

  • Khan, Z. H., Alin, T. S., & Hussain, M. A. (2011). Price prediction of share market using artificial neural network (ANN). International Journal of Computer Applications, 22(2), 42–47.

    Article  Google Scholar 

  • LeVee, G. S. (1993). The key to understanding the forecasting process. Journal of Business Forecasting, 11(4), 12–16.

    Google Scholar 

  • Mumtaz, B., Ufuk, & Emin, G. (2011). Estimating financial failure of the Turkish banks using artificial neural networks. New Orleans International Academic Conference, New Orleans, Louisiana USA.

    Google Scholar 

  • Niazkar, H. R., & Niazkar, M. (2020). Application of artificial neural networks to predict the COVID-19 outbreak. Glob Health Res Policy, 5(1), 50. https://doi.org/10.1186/s41256-020-00175-y

    Article  Google Scholar 

  • Pant, M., Bano, S., & Sarangi, P. K. (2014). Future trend in Indian automobile industry: A statistical approach. Apeejay-Journal of Management Sciences and Technology, 1(2), 28–32.

    Google Scholar 

  • Penpece, D., & Elma, O. E. (2014). Predicting sales revenue by using artificial neural network in grocery retailing industry: A case study in Turkey. International Journal of Trade, Economics and Finance, 5(5), 435–440.

    Article  Google Scholar 

  • Punnoose, R., & Ajit, P. (2016). Prediction of employee turnover in organizations using machine learning algorithms. International Journal of Advanced Research in Artificial Intelligence, 5(9).

    Google Scholar 

  • Ranjan, S., Singh, I. Dua, S., & Sood, S. (2018). Sentiment analysis of stock blog network communities for prediction of stock price trends. Indian Journal of Finance, 12(12). https://doi.org/10.17010/ijf/2018/v12i12/139888

  • Reza, M., & Mehdi, A. (2013). Assessment of customer credit through combined clustering of artificial neural networks, genetics algorithm and bayesian, probabilities. International Journal of Computer Science and Information Security (IJCSIS), 11(12), 156–165.

    Google Scholar 

  • Saad, M. D. (2013). A methodology to improve cash demand forecasting for ATM network. International Journal of Computer and Electrical Engineering, 5(4), 78–92.

    Google Scholar 

  • Sarangi, P. K., Chawla, M., Ghosh, P., Singh, S., & Singh, P. K. (2021a). FOREX trend analysis using machine learning techniques: INR vs USD currency exchange rate using ANN-GA hybrid approach. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2020.10.960

  • Sarangi, P. K., & Sarangi, P. (2010a). Load forecasting using artificial neural network: Performance evaluation with different numbers of hidden neurons. IUP Journal of Information Technology, 6(1), 34–42.

    Google Scholar 

  • Sarangi, P. K., & Sarangi, P. (2010b). Short-term load forecasting using neural network technology. IUP Journal of Computer Sciences, 4(2), 15–23.

    Google Scholar 

  • Sarangi, P. K., Sinha, D., Sinha, S., & Dubey, M. (2019). Financial modeling using ANN technologies: Result analysis with different network architectures and parameters. Indian Journal of Research in Capital Markets, 6(1). https://doi.org/10.17010/ijrcm/2019/v6/i1/144039

  • Sarangi, P. K., Sinha, D., Sinha, S., & Mittal, N. (2021b). Machine learning approach for the prediction of consumer food price index. 2021b 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 1–6. https://doi.org/10.1109/ICRITO51393.2021.9596527

  • Sarker, I. H., Kayes, A. S. M., & Watters, P. (2019). Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage. Journal of Big Data, 6(57).

    Google Scholar 

  • Shahid, N., Rappon, T., & Berta, W. (2019). Applications of artificial neural networks in health care organizational decision-making: A scoping review. PLoS One, 14(2).

    Google Scholar 

  • Singh, N., & Singh, R. (2009). Short term load forecasting using artificial neural network: A comparison with genetic algorithm implementation. Journal of Engineering and Applied Sciences, 4(9), 88–93. https://doi.org/10.1109/ICNGIS.2016.7854003

    Article  Google Scholar 

  • Singh, S., & Sarangi, P. K. (2014). Growth rate of Indian spices exports: Past trend and future prospects. Apeejay Journal of Management Sciences and Technology, 2(1), 29–34.

    Google Scholar 

  • Singla, C., & Sahoo, A. K. (2019). Modelling consumer price index: An empirical analysis using expert modeler. Journal of Technology Management for Growing Economies, 10(1), 43–50. https://doi.org/10.15415/jtmge.2019.101004

  • Soni, U., Sinha, N., Swami, Y., & Deshwal, P. (2018). A comparison study between ANN and ANFIS for the prediction of employee turnover in an organization. 203–206. https://doi.org/10.1109/GUCON.2018.8674886

  • Strohmeier, S., & Piazza, F. (2013). Domain driven data mining in human resource management: A review of current research. Expert Systems with Applications, 40(7), 2410–2420.

    Article  Google Scholar 

  • Thiesing, F. M., & Vornberger, O. (1997). Sales forecasting using neural networks. Proceedings ICNN’97, IEEE, Houston, Texas, Vol. 4, pp. 2125–2128.

    Google Scholar 

  • Tung, K.-Y., Huang, I.-C., Chen, S.-L., & Shih, C.-T. (2005). Mining the generation Xer’s job attitudes by artificial neural network and decision tree: Empirical evidence in Taiwan. Expert Systems and Applications, 29(4), 783–794.

    Article  Google Scholar 

  • Weber, J. A. (2002). Managing the marketing budget in a cost-constrained environment. Industrial Marketing Management, 31, 705–717.

    Article  Google Scholar 

  • Zahra, P., & Mohsin, S. R. (2014). Comparing the capabilities of neural networks and data envelopment analysis in predicting corporate profitability. Technical Paper (RRAMT), 40, 1–111.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CHRIST (Deemed to be University), Delhi NCR, India

    Deepti Sinha & Sachin Sinha

  2. Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

    Pradeepta Kumar Sarangi

Authors
  1. Deepti Sinha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pradeepta Kumar Sarangi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sachin Sinha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deepti Sinha .

Editor information

Editors and Affiliations

  1. Symbiosis Centre for Management and Human Resource Development (SCMHRD), Symbiosis International (Deemed University), Pune, Maharashtra, India

    Vinod Sharma

  2. East Nimar Education Society, Indore, Madhya Pradesh, India

    Chandan Maheshkar

  3. CHRIST (Deemed to be University), Ghaziabad, Uttar Pradesh, India

    Jeanne Poulose

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sinha, D., Sarangi, P.K., Sinha, S. (2023). Efficacy of Artificial Neural Networks (ANN) as a Tool for Predictive Analytics. In: Sharma, V., Maheshkar, C., Poulose, J. (eds) Analytics Enabled Decision Making. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-19-9658-0_6

Download citation

Publish with us

Policies and ethics

Search

Navigation