An Optimized Approach for Maximizing Business Intelligence using Machine Learning

Main Article Content

Afsaruddin
Devendra Agrawal
Sandeep Kumar Nayak

Abstract

The subject of study known as business intelligence is responsible for the development of techniques and tools for the analysis of business information with the goal of assisting in the management and decision-making processes of corporations. In the current climate, business intelligence is essential to the process of formulating a strategy and carrying out operations that are data-driven. Throughout the many stages of the company operation, an organization will need assistance evaluating data and making decisions; a decision support system may provide this assistance by including business intelligence as an essential component. The fact that this enormous quantity of data is distributed over a number of different types of platforms, however, makes it a difficult challenge, in particular to understand the information that is actually relevant and to make efficient use of it for business intelligence. One of the most important challenges facing modern society is maximizing business intelligence through the application of machine learning. It offers a full analysis that is based on predictions and is extracted for Business Intelligence techniques along with current application fields. This anomalous gap has been pointed up, and solutions and future research areas have been offered to overcome it in order to create effective business strategies.

Article Details

How to Cite
Afsaruddin, A., Agrawal, D. ., & Nayak, S. K. . (2023). An Optimized Approach for Maximizing Business Intelligence using Machine Learning. International Journal on Recent and Innovation Trends in Computing and Communication, 11(8), 72–80. https://doi.org/10.17762/ijritcc.v11i8.7925
Section
Articles

References

U. Paschen, C. Pitt, and J. Kietzmann, “Artificial intelligence: building blocks and an innovation typology,” Business Horizons, vol. 63, no. 2, pp. 147–155, 2020.

View at: Publisher Site | Google Scholar

C. Ramalingam and P. Mohan, “An efficient applications cloud interoperability framework using I-anfis,” Symmetry, vol. 13, no. 2, p. 268, 2021.

View at: Publisher Site | Google Scholar

S. Neelakandan, M. A. Berlin, S. Tripathi, V. B. Devi, I. Bhardwaj, and N. Arulkumar, “IoT-based traffic prediction and traffic signal control system for smart city,” Soft Computing, vol. 25, no. 18, pp. 12241–12248, 2021, https://doi.org/10.1007/s00500-021-05896-x.

View at: Google Scholar

A. Rodan, A. Fayyoumi, H. Faris, J. Alsakran, and O. Al-Kadi, “Negative correlation learning for customer churn prediction: a comparison study,” The Scientific World Journal, vol. 2015, 2015.

View at: Google Scholar

Falak Khursheed, Mohammad Suaib, “A Survey on Importance of Requirement Traceability in Software Engineering” in International Journal of Engineering and Innovative Technology (IJEIT)Volume 5, Issue 4, pp 109-112, ISSN: 2277-3754 October 2015.

View at: Google Scholar

A. Keramati and S. M. S. Ardabili, “Churn analysis for an Iranian mobile operator,” Telecommunications Policy, vol. 35, no. 4, pp. 344–356, 2011.

View at: Publisher Site | Google Scholar

PratibhaTripathi, Mohammad Suaib, “Security Issues On Cloud Computing” in International Journal of Engineering Technology, Management and Applied Sciences Volume 2 Issue 6, pp 1-8 ISSN 2349-4476 November 2014.

View at: Publisher Site | Google Scholar

A. K. Ahmad, A. Jafar, and K. Aljoumaa, “Customer churn prediction in telecom using machine learning in big data platform,” Journal of Big Data, vol. 6, no. 1, pp. 28–24, 2019.

View at: Publisher Site | Google Scholar

D. Paulraj, “An automated exploring and learning model for data prediction using balanced CA-SVM,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 5, 2020.

View at: Google Scholar

C. Saravanakumar, R. Priscilla, B. Prabha, A. Kavitha, M. Prakash, and C. Arun, “An efficient on-demand virtual machine migration in cloud using common deployment model,” Computer Systems Science and Engineering, vol. 42, no. 1, pp. 245–256, 2022.

View at: Publisher Site | Google Scholar

H. Jain, A. Khunteta, and S. Srivastava, “Churn prediction in telecommunication using logistic regression and logit boost,” Procedia Computer Science, vol. 167, pp. 101–112, 2020.

View at: Publisher Site | Google Scholar

T. Xu, Y. Ma, and K. Kim, “Telecom churn prediction system based on ensemble learning using feature grouping,” Applied Sciences, vol. 11, no. 11, p. 4742, 2021.

View at: Publisher Site | Google Scholar

View at: Publisher Site | Google Scholar

Mohammad Suaib, Dr. M. Akheela Khanum, “Web Page Personalization Techniques in the Purview of Page Ranking Methodology using Artificial Intelligence” in International Journal of Computer and Information Technology, Volume 8, Issue 6, ISSN: 2279-0764, November 2019.

Kumar Pandey, A. . ., Arivazhagan , D. ., Rane , S. ., M. Yadav , S. ., Nabilal , K. V. ., & Oberoi , A. . (2023). A Novel Digital Mark CP-ABE Access Control Scheme for Public Secure Efficient Cloud Storage Technique. International Journal of Intelligent Systems and Applications in Engineering, 11(3s), 100–103. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2536

D. Rb, in Proceedings of the International Conference on Innovative Computing & Communication (ICICC), Delhi, India, April 2021.

N. I. Mohammad, S. A. Ismail, M. N. Kama, O. M. Yusop, and A. Azmi, “Customer churn prediction in telecommunication industry using machine learning classifiers,” in Proceedings of the 3rd International Conference on Vision, Image and Signal Processing, pp. 1–7, Columbia, Canada, August 2019.

View at: Google Scholar

Siddiqui, F., & Mohammad Suaib. (2023). Machine Learning Approaches for Fake User and Spammer Detection: A Comprehensive Review and Future Perspectives. International Journal of Engineering and Management Research, 13(3), 23–46. https://doi.org/10.31033/ijemr.13.3.4

D. Manzano-Machob, ‘‘The architecture of a churn prediction system based on stream mining,’’ in Proc. Artif. Intell. Res. Develop., 16th Int. Conf. Catalan Assoc. Artif. Intell., vol. 256, Oct. 2013, p. 157.

Reddy, A. ., & Waheeb , M. Q. . (2022). Enhanced Pre-Processing Based Cardiac Valve Block Detection Using Deep Learning Architectures. Research Journal of Computer Systems and Engineering, 3(1), 84–89. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/47

T. Kotler, Marketing Management: Analysis, Planning, Implementation and Control. London, U.K.: Prentice-Hall, 1994.

F. F. Reichheld and W. E. Sasser, Jr., ‘‘Zero defections: Quality comes to services,’’ Harvard Bus. Rev., vol. 68, no. 5, pp. 105–111, 1990.

J. Hadden, A. Tiwari, R. Roy, and D. Ruta, ‘‘Computer assisted customer churn management: State-of-the-art and future trends,’’ Comput. Oper. Res., vol. 34, no. 10, pp. 2902–2917, Oct. 2007.

H.-S. Kim and C.-H. Yoon, ‘‘Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market,’’ Telecommun. Policy, vol. 28, nos. 9–10, pp. 751–765, Nov. 2004.

Y. Huang and T. Kechadi, ‘‘An effective hybrid learning system for telecommunication churn prediction,’’ Expert Syst. Appl., vol. 40, no. 14, pp. 5635–5647, Oct. 2013.

J. Vijaya and E. Sivasankar, “Computing efficient features using rough set theory combined with ensemble classification techniques to improve the customer churn prediction in telecommunication sector,” Computing, vol. 100, no. 8, pp. 839–860, 2018.

Kamalraj, S. Neelakandan, M. Ranjith Kumar, V. Chandra ShekharRao, R. Anand, and H. Singh, “Interpretable filter based convolutional neural network (IF-CNN) for glucose prediction and classification using PD-SS algorithm,” Measurement, vol. 183, Article ID 109804, 2021.

Hussain Bukhari, S. N. . (2021). Data Mining in Product Cycle Prediction of Company Mergers . International Journal of New Practices in Management and Engineering, 10(03), 01–05. https://doi.org/10.17762/ijnpme.v10i03.127

R. Sharma and P. Kumar Panigrahi, “A neural network based approach for predicting customer churn in cellular network services,” International Journal of Computer Application, vol. 27, no. 11, pp. 26–31, 2011.

A. Qureshi, A. S. Rehman, A. M. Qamar, and A. Kamal, “Telecommunication subscribers’ churn prediction model using machine learning,” in Proceedings of the 8th International Conference on Digital Information Management (ICDIM ’13), pp. 131–136, Islamabad, Pakistan, September 2013.