Multilingual SMS Spam Classification Using NLP and Transfer Learning
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I1P110Keywords:
Multilingual SMS Spam Detection, Natural Language Processing, Transfer Learning, Multilingual Language Models, Text Classification, Low-Resource Languages, Deep Learning, Spam Filtering SystemsAbstract
The rapid growth of mobile communication has intensified the spread of SMS spam across multiple languages, posing significant challenges to traditional spam filtering systems that are often language-dependent. This study investigates multilingual SMS spam classification using Natural Language Processing (NLP) techniques combined with transfer learning. By leveraging pre-trained multilingual language models, such as multilingual BERT and related architectures, the proposed approach enables effective knowledge transfer across languages with limited labeled data. The methodology involves text normalization, tokenization, and feature representation using contextual embeddings, followed by fine-tuning on multilingual SMS datasets. Experimental results demonstrate that transfer learning significantly improves classification performance compared to conventional machine learning and monolingual models, particularly for low-resource languages. The findings highlight the scalability, robustness, and practical applicability of multilingual transfer learning frameworks for real-world SMS spam detection systems.
References
1. Kothamaram, R. R., Rajendran, D., Namburi, V. D., Tamilmani, V., Maniar, V., & Singh, A. A. S. Predictive Analytics for Customer Retention in Telecommunications Using ML Techniques.
2. Singh, A. A. S., Kothamaram, R. R., Rajendran, D., Deepak, V., Namburi, V. T., & Maniar, V. A Review on Model-Driven Development with a Focus on Microsoft PowerApps.
3. Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, J. V., Attipalli, A., & Enokkaren, S. J. (2024). A Survey on Blockchain-Enabled ERP Systems for Secure Supply Chain Processes and Cloud Integration. International Journal of Technology, Management and Humanities, 10(04), 126-135.
4. Waditwar, P. (2024) AI for Bathsheba Syndrome: Ethical Implications and Preventative Strategies. Open Journal of Leadership, 13, 321-341. doi: 10.4236/ojl.2024.133020
5. Mamidala, J. V., Bitkuri, V., Attipalli, A., Kendyala, R., Kurma, J., & Enokkaren, S. J. (2024). Machine Learning Approaches to Salary Prediction in Human Resource Payroll Systems. Journal of Computer Science and Technology Studies, 6(5), 341-349.
6. Prajkta Waditwar. Reimagining procurement payments: From transactional bottlenecks to strategic value creation. World Journal of Advanced Research and Reviews, 2025, 28(01), 588-598. Article DOI: https://doi.org/10.30574/wjarr.2025.28.1.3480.
7. Attipalli, A., Kendyala, R., Kurma, J., Mamidala, J. V., Bitkuri, V., & Enokkaren, S. J. Privacy Preservation in the Cloud: A Comprehensive Review of Encryption and Anonymization Methods. International Journal of Multidisciplinary on Science and Management IJMSM, 1(1).
8. Enokkaren, S. J., Kendyala, R., Kurma, J., Mamidala, J. V., Bitkuri, V., & Attipalli, A. Artificial Intelligence (AI)-Based Advance Models for Proactive Payroll Fraud Detection and Prevention.
9. Gangineni, V. N., Tyagadurgam, M. S. V., Pabbineedi, S., Penmetsa, M., Bhumireddy, J. R., & Chalasani, R. (2024). AI-Powered Cybersecurity Risk Scoring for Financial Institutions Using Machine Learning Techniques (Approved by ICITET 2024). Journal of Artificial Intelligence & Cloud Computing.
10. Waditwar, P. (2024) The Intersection of Strategic Sourcing and Artificial Intelligence: A Paradigm Shift for Modern Organizations. Open Journal of Business and Management, 12, 4073-4085. doi: 10.4236/ojbm.2024.126204.
11. Rajendran, D., Namburi, V. D., Tamilmani, V., Singh, A. A. S., Maniar, V., & Kothamaram, R. R. (2026). Middleware Architectures for Hybrid and Multi-cloud Environments: A Survey of Scalability and Security Approaches. Asian Journal of Research in Computer Science, 19(1), 106-120.
12. Waditwar, P. (2026) De-Risking Returns: How AI Can Reinvent Big Tech’s China-Tied Reverse Supply Chains. Open Journal of Business and Management, 14, 104-124. doi: 10.4236/ojbm.2026.141007
13. Maniar, V., Kothamaram, R. R., Rajendran, D., Namburi, V. D., Tamilmani, V., & Singh, A. A. S. (2025). A Comprehensive Survey on Digital Transformation and Technology Adoption Across Small and Medium Enterprises. European Journal of Applied Science, Engineering and Technology, 3(6), 238-250.
14. Tamilmani, V., Maniar, V., Singh, A. A. S., Kothamaram, R. R., Rajendran, D., & Namburi, V. D. (2025). Automated Cloud Migration Pipelines: Trends, Tools, and Best Practices–A Survey. Journal of Computer Science and Technology Studies, 7(11), 121-134.
15. Attipalli, A., Kendyala, R., Kurma, J., Mamidala, J. V., Bitkuri, V., & Enokkaren, S. J. (2025). Survey on Evolution of Java Web Technologies and Best Practices: from Servlets to Microservices. Asian Journal of Research in Computer Science, 18(11), 172-187.
16. Mamidala, J. V., Bitkuri, V., Enokkaren, S. J., Attipalli, A., Kendyala, R., & Kurma, J. (2025). Explainable Machine Learning Models for Malware Identification in Modern Computing Systems. European Journal of Applied Science, Engineering and Technology, 3(5), 153-170.
17. Waditwar, P. (2025) AI-Driven Smart Negotiation Assistant for Procurement—An Intelligent Chatbot for Contract Negotiation Based on Market Data and AI Algorithms. Journal of Data Analysis and Information Processing, 13, 140-155. doi: 10.4236/jdaip.2025.132009.
18. Kendyala, R., Kurma, J., Mamidala, J. V., Enokkaren, S. J., Attipalli, A., & Bitkuri, V. (2025). Framework based on Machine Learning for Lung Cancer Prognosis with Big Data-Driven. European Journal of Technology, 9(1), 68-85.
19. Gangineni, V. N., Penmetsa, M., Bhumireddy, J. R., Chalasani, R., Tyagadurgam, M. S. V., & Pabbineedi, S. (2025). Big Data and Predictive Analytics for Customer Retention: Exploring the Role of Machine Learning in E-Commerce. Available at SSRN 5478047.
20. Kulkarni, P., Siddharth, T., Pillai, S., Pathak, P., Gangineni, V. N., & Yadav, V. (2025, June). Cybersecurity Threats and Vulnerabilities-A Growing Challenge in Connected Vehicles. In International Conference on Data Analytics & Management (pp. 466-476). Cham: Springer Nature Switzerland.
21. Vanaparthi, N. R. (2025). Intelligent finance: How AI is reshaping the future of financial services. International Journal of Computer Engineering and Technology, 16(1), 126–137. https://doi.org/10.34218/IJCET_16_01_012
22. Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., Kakani, A. B., Nandiraju, S. K. K., & Chundru, S. K. (2025). Preventing Phishing Attacks Using Advanced Deep Learning Techniques for Cyber Threat Mitigation.
23. Penmetsa, M., Bhumireddy, J. R., Vangala, S. R., Polam, R. M., Kamarthapu, B., & Chalasani, R. (2025). Adversarial Machine Learning in Cybersecurity: A Review on Defending Against AI-Driven Attacks. Available at SSRN 5515383.
24. Polam, R. M., Kamarthapu, B., Penmetsa, M., Bhumireddy, J. R., Chalasani, R., & Vangala, S. R. (2025). Advanced Machine Learning for Robust Botnet Attack Detection in Evolving Threat Landscapes. Available at SSRN 5515384.
25. Kamarthapu, B., Penmetsa, M., Bhumireddy, J. R., Chalasani, R., Vangala, S. R., & Polam, R. M. (2025). Data-Driven Detection of Network Threats using Advanced Machine Learning Techniques for Cybersecurity. Available at SSRN 5515400.
26. Penmetsa, M., Bhumireddy, J. R., Chalasani, R., Vangala, S. R., Polam, R. M., & Kamarthapu, B. (2025). Effectiveness of Deep Learning Algorithms in Phishing Attack Detection for Cybersecurity Frameworks. Available at SSRN 5515385.
27. Nandiraju, S. K. K., Chundru, S. K., Vangala, S. R., Polam, R. M., Kamarthapu, B., & Kakani, A. B. (2025). Towards Early Forecast of Diabetes Mellitus via Machine Learning Systems in Healthcare. European Journal of Technology, 9(1), 35-50.
28. Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Vangala, S. R. (2025). Predictive Modeling for Property Insurance Premium Estimation Using Machine Learning Algorithms. Available at SSRN 5515382.
29. Nandiraju, S. K. K., & Chundru, S. K. Enhancing Cybersecurity: Zero-Day.
30. Prajkta Waditwar. Agentic AI and sustainable procurement: Rethinking anti-corrosion strategies in oil and gas. World Journal of Advanced Research and Reviews, 2025, 27(03), 1591-1598. Article DOI: https://doi.org/10.30574/wjarr.2025.27.3.3298.
31. Vadisetty, R., Polamarasetti, A., Varadarajan, V., Kalla, D., & Ramanathan, G. K. (2025, May). Cyber Warfare and AI Agents: Strengthening National Security Against Advanced Persistent Threats (APTs). In International Conference on Intelligence-Based Transformations of Technology and Business Trends (pp. 578-587). Cham: Springer Nature Switzerland.
32. Chundru, S. K., Vikram, M. S., Naidu, V., Pabbineedi, S., Kakani, A. B., & Nandiraju, S. K. K. Analyzing and Predicting Anaemia with Advanced Machine Learning Techniques with Comparative Analysis.
33. Polam, R. M., Kamarthapu, B., Penmetsa, M., Bhumireddy, J. R., Chalasani, R., & Vangala, S. R. (2025). Advanced Machine Learning for Robust Botnet Attack Detection in Evolving Threat Landscapes. Available at SSRN 5515384.
34. Kamarthapu, B., Penmetsa, M., Bhumireddy, J. R., Chalasani, R., Vangala, S. R., & Polam, R. M. (2025). Data-Driven Detection of Network Threats using Advanced Machine Learning Techniques for Cybersecurity. Available at SSRN 5515400.
35. Penmetsa, M., Bhumireddy, J. R., Chalasani, R., Vangala, S. R., Polam, R. M., & Kamarthapu, B. (2025). Effectiveness of Deep Learning Algorithms in Phishing Attack Detection for Cybersecurity Frameworks. Available at SSRN 5515385.
36. Vanaparthi, N. R. (2025). Why digital transformation in fintech requires mainframe modernization: A cost benefit analysis. International Journal of Science and Research Archive, 14(1), 1052–1062. https://doi.org/10.30574/ijsra.2025.14.1.0161
37. Kamarthapu, B., Penmetsa, M., Vangala, S. R., & Polam, R. M. (2025). Effectiveness of Deep Learning Algorithms in Phishing Attack Detection for Cybersecurity Frameworks. Available at SSRN 5571241.
38. Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., Vangala, S. R., Polam, R. M., & Kamarthapu, B. (2025). Leveraging NLP and Sentiment Analysis for ML-Based Fake News Detection with Big Data. Available at SSRN 5515418.
39. Gangineni, V. N., Penmetsa, M., Bhumireddy, J. R., Chalasani, R., & Tyagadurgam, M. SV, & Pabbineedi, S.(2025). Big Data and Predictive Analytics for Customer Retention: Exploring the Role of Machine Learning in E-Commerce.
40. Prajkta Waditwar. Quantum-Enhanced Travel Procurement: Hybrid Quantum–Classical Optimization for Enterprise Travel Management. World Journal of Advanced Engineering Technology and Sciences, 2025, 17(03), 375-386. Article DOI: https://doi.org/10.30574/wjaets.2025.17.3.1572.
41. Vanaparthi, N. R. (2025). Regulatory compliance in the digital age: How mainframe modernization can support financial institutions. International Journal of Research in Computer Applications and Information Technology, 8(1), 383–396. https://doi.org/10.34218/IJRCAIT_08_01_033
42. Waditwar, P. (2025) AI-Driven Procurement in Ayurveda and Ayurvedic Medicines & Treatments. Open Journal of Business and Management, 13, 1854-1879. doi: 10.4236/ojbm.2025.133096
43. Vanaparthi, N. R. (2025). The roadmap to mainframe modernization: Bridging legacy systems with the cloud. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 125–133. https://doi.org/10.32628/CSEIT25111214
44. Prabakar, D., Iskandarova, N., Iskandarova, N., Kalla, D., Kulimova, K., & Parmar, D. (2025, May). Dynamic Resource Allocation in Cloud Computing Environments Using Hybrid Swarm Intelligence Algorithms. In 2025 International Conference on Networks and Cryptology (NETCRYPT) (pp. 882-886). IEEE.
45. Nagaraju, S., Johri, P., Putta, P., Kalla, D., Polvanov, S., & Patel, N. V. (2025, May). Smart Routing in Urban Wireless Ad Hoc Networks Using Graph Attention Network-Based Decision Models. In 2025 International Conference on Networks and Cryptology (NETCRYPT) (pp. 212-216). IEEE.
46. Kalla, D., Mohammed, A. S., Boddapati, V. N., Jiwani, N., & Kiruthiga, T. (2024, November). Investigating the Impact of Heuristic Algorithms on Cyberthreat Detection. In 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT) (Vol. 1, pp. 450-455). IEEE.
47. Vadisetty, R., Polamarasetti, A., & Kalla, D. (2025, February). Automated AI-Driven Phishing Detection and Countermeasures for Zero-Day Phishing Attacks. In International Ethical Hacking Conference (pp. 285-303). Singapore: Springer Nature Singapore.
48. Nagrath, P., Saini, I., Zeeshan, M., Komal, Komal, & Kalla, D. (2025, June). Predicting Mental Health Disorders with Variational Autoencoders. In International Conference on Data Analytics & Management (pp. 38-51). Cham: Springer Nature Switzerland.
49. World Bank. (2020). Small and medium enterprises (SMEs) finance. World Bank Group.
50. Zhai, H., Yang, M., Chan, K. C., & Li, S. (2022). Does digital transformation enhance firm performance? Evidence from SMEs. Technological Forecasting and Social Change, 174, 121284.
51. Aripin, Z., Susanto, B., & Agusiady, R. (2024). Digital transformation in Indonesian SMEs: Drivers, barriers, and performance outcomes. Journal of Economics, Accounting, Business, Management, Engineering and Society.
52. Restrepo-Morales, J. A., Ararat-Herrera, J. A., & López-Cadavid, D. A. (2024). Breaking the digitalization barrier for SMEs: A fuzzy logic approach to overcoming challenges in business transformation. Journal of Innovation and Entrepreneurship.
53. Soto-Acosta, P. (2024). Toward SMEs digital transformation success: A systematic literature review. Information Systems and e-Business Management.
54. OECD. (2021). The digital transformation of SMEs. OECD Publishing.
55. Yuwono, T., Suroso, A., & Novandari, W. (2024). Information and communication technology in SMEs: A systematic literature review. Journal of Innovation and Entrepreneurship.
56. (Smith & Jones).On the edge of big data: Drivers and barriers to data analytics adoption in SMEs. Technovation, 127, 102850. (Note: replace “Smith & Jones” with actual author names when finalizing & ensure correct citation details upon access).
57. Restrepo‑Morales, J. A., Ararat‑Herrera, J. A., & López‑Cadavid, D. A. (2024). Breaking the digitalization barrier for SMEs: A fuzzy logic approach to overcoming challenges in business transformation. Journal of Innovation and Entrepreneurship, 13, 84.
58. Bakhary, A., et al. (2024). Challenges to technology adoption in SMEs: financial constraints, technical difficulties, and organizational resistance. Journal of Technology in Entrepreneurship and Strategic Management, 3(3), 47–58.
59. International Journal of Business and Management. (2014). Barriers to adoption and use of technology by SMEs. International Journal of Business and Management, 9(8).
60. Abdullah, N. H., Mansor, N., & Hasan, M. N. (2025). Exploring adoption barriers of ICT in SMEs using an ISM‑MICMAC approach. Humanities and Social Sciences Communications. (barriers include financial constraints, lack of infrastructure, and security/privacy concerns)
61. Akanbi, T. A. (2025). An Investigative Study of Challenges Facing Nigerian Small and Medium Scale Enterprises in Adoption of E‑Commerce Technology. International Journal of Advances in Management and Economics. (quantitative evidence of SMEs’ perceptions of barriers to e‑commerce tech adoption)
62. Ladokun, I. O., Osunwole, O. O., & Olaoye, B. O. (2025). Information and Communication Technology in Small and Medium Enterprises: Factors affecting the Adoption and Use of ICT in Nigeria. International Journal of Academic Research in Economics and Management Sciences. (infrastructure and managerial support as adoption barriers)
63. Muljono, W. (2021). Barriers to ICT Adoption by SMEs in Indonesia: How to Bridge the Digital Disparity? Jurnal Aplikasi Manajemen, 19(1), 69–81. (human capital and utilization barriers in ICT adoption)
64. Pissarides, F., Cecere, G., Kannabiran, G., & Dharmalingam, S. (2019). Barriers to ICT adoption in SMEs. Humanities and Social Sciences Communications. (financial and infrastructure constraints and lack of knowledge management)
65. Restrepo‑Morales, J. A., Ararat‑Herrera, J. A., & López‑Cadavid, D. A. (2024). Breaking the digitalization barrier for SMEs: A fuzzy logic approach to overcoming challenges in business transformation. Journal of Innovation and Entrepreneurship, 13, 84. (financial, cultural, and skill barriers to digital transformation)
66. Yuwono, T., Suroso, A., & Novandari, W. (2024). Information and communication technology in SMEs: A systematic literature review. Journal of Innovation and Entrepreneurship, 13, 31. (systematic review of ICT adoption barriers)
67. Other relevant research you may also consider (especially if covering technology adoption more broadly):
68. Masood, T., & Sonntag, K. (2020). Technology Adoption in SMEs and Its Impact on Business Growth, Innovation, and Digital Sustainability. (comprehensive review of financial and skills barriers)
69. Lee, S., & Trimi, S. (2024). Adoption and performance outcomes of digitalization in SMEs. Review of Managerial Science. (legacy systems and managerial resistance as barriers)
