Development of Hybridized Algorithms for a Customer Information Profiling System
DOI:
https://doi.org/10.63561/jca.v2i4.1082Keywords:
MapReduce, Hybridized Algorithms, Back Propagation, Neural Network, Machine LearningAbstract
Accurate customer information profiling remains a critical challenge for banks and businesses due to the massive volume of customer data and the need for timely, targeted service delivery. The conventional Back Propagation Neural Network (BPNN) algorithm suffers from high computational time, limiting its efficiency in large-scale data analysis. To address this limitation, this study developed a hybridized MapReduce Back Propagation Neural Network (MRBPNN) technique for customer information profiling. A dataset comprising 2,000 customers’ demographic and socioeconomic records, each with 35 attributes and 5 status updates, was obtained from selected banks in Edo State. The data were divided into training and testing sets and evaluated using a ten-fold cross-validation approach. The hybrid MRBPNN model was implemented in MATLAB R2023a and compared with the conventional BPNN algorithm based on false positive rate (FPR), precision, recall, F-measure, accuracy, and computational time. Experimental results revealed that MRBPNN achieved superior performance with an FPR of 5.83%, accuracy of 94.59%, precision of 99.31%, recall of 94.64%, F-measure of 96.92%, and computational time of 13.23 seconds. In contrast, BPNN recorded an FPR of 15.61%, accuracy of 84.97%, precision of 97.96%, recall of 85.04%, F-measure of 91.05%, and computational time of 26.69 seconds. The findings demonstrate that the developed MRBPNN significantly reduces computational time and enhances classification accuracy compared to BPNN, making it an efficient and scalable technique for customer information profiling in financial institutions.
References
Annamalai, M., & Muthiah, P. B. (2022). An early prediction of tumor in heart by cardiac masses classification in echocardiogram images using robust back propagation neural network classifier. Brazilian Archives of Biology and Technology.
AlFayez, F., & Bouhamed, H. (2023). Machine learning and uLBP histograms for posture recognition of dependent people via Big Data Hadoop and Spark platform. International Journal of Computers Communications & Control, 18(1).
Arockiam, J. M., & Pushpanathan, A. C. (2023). MapReduce-iterative support vector machine classifier: Novel fraud detection systems in healthcare insurance industry. International Journal of Electrical and Computer Engineering (IJECE), 13(1), 756–769.
Awasthy, N., & Valivarthi, N. (2023). Evolution of Hadoop and big data trends in smart world. In Sustainable computing: Transforming Industry 4.0 to Society 5.0 (pp. 99–127). Springer International Publishing.
Bergui, M., Hourri, S., Najah, S., & Nikolov, N. S. (2024). Predictive modelling of MapReduce job performance in cloud environments using machine learning techniques. Journal of Big Data, 11(1), Article 98.
Biswas, P. K., & Liu, S. (2022). A hybrid recommender system for recommending smartphones to prospective customers. Expert Systems with Applications, 208.
Chandrasekar, J., & Ponnusamy, A. (2025). Hybrid jellyfish search sine cosine optimisation-based deep learning for big data classification using MapReduce framework on epileptic seizure data. International Journal of Intelligent Information and Database Systems, 17(1), 1–31.
Cui, Y., Liu, H., Wang, Q., Zheng, Z., Wang, H., Yue, Z., Ming, Z., Wen, M., Feng, L., & Yao, M. (2022). Investigation on the ignition delay prediction model of multi-component surrogates based on back propagation neural network. Combustion and Flame, 237, 111852.
Dampfhoffer, M., Mesquida, T., Valentian, A., & Anghel, L. (2023). Backpropagation-based learning techniques for deep spiking neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems, 35(9), 11906–11921.
Dong, H., & Wang, M. (2025). Forecasting behavior and reverse engineering of self-vibrating string-mass systems utilizing a backpropagation neural network. Journal of Vibration Engineering & Technologies, 13(7), 501.
Gao, J., Wang, J., Xu, Z., Wang, C., & Yan, S. (2023). Multiaxial fatigue prediction and uncertainty quantification based on back propagation neural network and Gaussian process regression. International Journal of Fatigue, 168.
Gazis, A., & Katsiri, E. (2022). Crowd tracking and monitoring middleware via Map-Reduce. International Journal of Parallel, Emergent and Distributed Systems, 37(3), 333–343.
Gattoju, S., & Nagalakshmi, V. (2023). Design of ChaApache framework for securing Hadoop application in big data. Multimedia Tools and Applications, 82(10), 15247–15269.
Hamdi, M., Bouhamed, H., Badreddine, F., & Alkanhel, R. I. (2024). Deep recurrent neural networks distributed on a Hadoop/Spark cluster for fall detection. International Journal of Computers Communications & Control, 19(3).
Hosseini, S., Poormirzaee, R., & Hajihassani, M. (2022). Application of reliability-based back-propagation causality-weighted neural networks to estimate air-overpressure due to mine blasting. Engineering Applications of Artificial Intelligence.
Jansen, B. J., Aldous, K. K., Salminen, J., Almerekhi, H., & Jung, S. G. (2024). Data preprocessing. In Understanding audiences, customers, and users via analytics: An introduction to the employment of web, social, and other types of digital people data (pp. 65–75). Springer Nature Switzerland.
Jonathan, L. E., Winston, A. G., & Chukwuemeka, P. (2025). Machine learning and morphometric analysis for runoff dynamics: Enhancing flood management and catchment prioritization in Bayelsa, Nigeria. Journal of Computational Systems and Applications.
Kumar, K. S., Manoj, H. K., & Geetha, D. E. (2023). Twitter data analysis using Hadoop and R and emotional analysis using optimized SVNN. Computer Systems Science & Engineering, 44(1).
Li, S., Wang, W., Lu, B., Du, X., Dong, M., Zhang, T., & Bai, Z. (2023). Long-term structural health monitoring for bridge based on back propagation neural network and long and short-term memory. Structural Health Monitoring, 22(4), 2325–2345.
Lin, J., & Dyer, C. (2022). Data-intensive text processing with MapReduce. Springer Nature.
