Transfer learning will need to be used to build prediction models in target domains with no labelled data. Hierarchical Knowledge Alignment (HKA) is a new cross-domain transfer learning method proposed in this paper that enables knowledge transfer between source and target domains with different feature spaces and data distributions. HKA does so by initially learning domain-invariant high-level feature representations and subsequently gradually adapting domain-specific features by learning across different layers of deep neural networks. Top-down yields the best overall and specific information transfer with no negative transfer. The Cross-Industry Product Review (CIPR-484) corpus, which included 484 product categories from two different online shopping websites, was utilised in order to complete the evaluation of the model. Within the context of the paper, the TensorFlow and scikit-learn libraries were utilised in a Python environment for the purpose of developing and testing the model. HKA was able to demonstrate significant increases in target-domain classification performance in comparison to state-of-the-art domain adaptation techniques. Additionally, it enabled the construction of adaptive predictive smart systems in settings with minimal data.
Keywords
Transfer LearningDomain AdaptationKnowledge RepresentationPredictive ModellingDeep Learning.
References
G. Chiranjeevi, R. A. Reddy, R. Shyam, S. Sah, and R. B. Sulaiman, “Docker-based decentralised vulnerability assessment with port scanning powered by artificial intelligence,” FMDB Transactions on Sustainable Intelligent Networks, vol. 1, no. 4, pp. 220-241, 2024.
T. Manaka, T. Van Zyl, D. Kar, and A. Wade, “Multi-step transfer learning in natural language processing for the health domain,” Neural Processing Letters, vol. 56, no. 5, pp. 1-26, 2024.
P. Yan, A. Abdulkadir, P. P. Luley, M. Rosenthal, G. A. Schatte, and B. F. Grewe, “A comprehensive survey of deep transfer learning for anomaly detection in industrial time series: methods, applications, and directions,” IEEE Access, vol. 12, no. 12, pp. 3768-3789, 2024.
V. Kadiresan, S. S. Hameed, and B. Subramaniam, “Empathising the effect of mobile coupon promotions on social shopping behaviour,” FMDB Transactions on Sustainable Humanities and Society, vol. 1, no. 1, pp. 30-38, 2024.
R. Yang, T. F. Tan, W. Lu, A. J. Thirunavukarasu, D. S. W. Ting, and N. Liu, “Large language models in health care: development, applications, and challenges,” Health Care Science, vol. 2, no. 4, pp. 255-263, 2023.
Z. Wang, J. Zhang, K. Ma, M. D. Butala, H. Tang, and H. Wang, “Toward unsupervised domain adaptation fault diagnosis: a multi-source multitarget method,” IEEE Sensors Journal, vol. 25, no. 1, pp. 1994-2007, 2025.
K. Jasper, R. Neha, and A. Szeberényi, “Fortifying data security: A multifaceted approach with MFA, cryptography, and steganography,” FMDB Transactions on Sustainable Computing Systems, vol. 1, no. 2, pp. 98-111, 2023.
M. A. K. Raiaan, M. S. H. Mukta, K. Fatema, N. M. Fahad, S. Sakib, and M. M. J. Mim, “A review on large language models: architectures, applications, taxonomies, open issues and challenges,” IEEE Access, vol. 12, no. 2, pp. 26839-26874, 2024.
S. Hajduk, P. S. Venkateswaran, M. T. V. J. Paul, and P. Divya, “A study on the influence of perceived quality on brand trust and loyalty towards FMCG products,” FMDB Transactions on Sustainable Technoprise Letters, vol. 2, no. 1, pp. 42-52, 2024.
Y. Liu and A. Deng, “A universal domain adaptation method based on self-supervised clustering for machinery fault diagnosis,” in Proceedings of the 2024 30th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), IEEE, Leeds, United Kingdom, 2024.
A. K. R. Ayyadapu, “A hybrid machine learning model for predictive analytics in big data frameworks,” AVE Trends in Intelligent Computing Systems, vol. 2, no. 1, pp. 27-38, 2025.
D. K. Jasper, A. S. V. Raja, R. Neha, S. S. Rajest, R. Regin, and B. Senapati, “Secure identity: A comprehensive approach to identity and access management,” FMDB Transactions on Sustainable Computing Systems, vol. 1, no. 4, pp. 171-189, 2023.
M. L. Dominic, P. S. Venkateswaran, L. T. Reddi, S. Rangineni, R. Regin, and S. S. Rajest, “The synergy of management information systems and predictive analytics for marketing,” in Advances in Business Information Systems and Analytics, IGI Global, USA, pp. 49-63, 2023.
D. Wu, L. Nie, R. A. Mumtaz, and K. Agarwal, “A LLM-based hybrid-transformer diagnosis system in healthcare,” IEEE Journal of Biomedical and Health Informatics, vol. 29, no. 9, pp. 6428-6439, 2024.
B. T. Devi and R. Rajasekaran, “A comprehensive review on deepfake detection on social media data,” FMDB Transactions on Sustainable Computing Systems, vol. 1, no. 1, pp. 11-20, 2023.
J. A. Jeba, S. R. Bose, R. Regin, S. S. Rajest, and U. Kose, “In-depth analysis and implementation of advanced information gathering tools for cybersecurity enhancement,” FMDB Transactions on Sustainable Computer Letters, vol. 1, no. 2, pp. 130-146, 2023.
M. S. Azari, F. Flammini, S. Santini, and M. Caporuscio, “A systematic literature review on transfer learning for predictive maintenance in Industry 4.0,” IEEE Access, vol. 11, no. 1, pp. 12887-12910, 2023.
A. G. Ayana, J. Park, J.-W. Jeong, and S. Choe, “A novel multistage transfer learning for ultrasound breast cancer image classification,” Diagnostics, vol. 12, no. 1, p. 135, 2022.
N. A. Wani, R. Kumar, J. Bedi, and I. Rida, “Explainable AI-driven IoMT fusion: unravelling techniques, opportunities, and challenges with explainable AI in healthcare,” Information Fusion, vol. 110, no. 10, p. 102472, 2024.
S. Banala, “Enhancing system stability with advanced techniques in site reliability engineering,” AVE Trends in Intelligent Computing Systems, vol. 1, no. 2, pp. 66-76, 2024.
W. Zhang, Q. Xu, Y. Hu, C. Xu, and L. Luo, “Attention-based two-stage multi-sensor feature fusion method for bearing fault diagnosis,” IEEE Transactions on Industry Applications, vol. 60, no. 6, pp. 8709-8721, 2024.
T. Wang, D. Xu, B. Jiang, and X.-G. Yan, “Actuator fault detection and estimation for hydrofoil attitude control systems: a Gaussian mixture model-aided UKF approach,” IEEE Transactions on Instrumentation and Measurement, vol. 73, no. 9, pp. 1-14, 2024.
R. D. Bastareche, “Transformative marketing practices and impact on sales growth: The Knopper Enterprises experience,” FMDB Transactions on Sustainable Social Sciences Letters, vol. 2, no. 2, pp. 70-84, 2024.
R. S. Madhuranthakam, “Implementing data quality assurance frameworks in distributed data engineering workflows,” AVE Trends in Intelligent Computing Systems, vol. 1, no. 4, pp. 241-251, 2024.
M. S. Minu, S. S. Subashka Ramesh, R. Canessane, M. Al-Amin, and R. B. Sulaiman, “Experimental analysis of UAV networks using oppositional glowworm swarm optimisation and deep learning clustering and classification,” FMDB Transactions on Sustainable Computing Systems, vol. 1, no. 3, pp. 124-134, 2023.
S. Murugavel and F. Hernandez, “A comparative study between statistical and machine learning methods for forecasting retail sales,” FMDB Transactions on Sustainable Computer Letters, vol. 1, no. 2, pp. 76-102, 2023.
S. Panyaram, “Integrating artificial intelligence with big data for real-time insights and decision-making in complex systems,” FMDB Transactions on Sustainable Intelligent Networks, vol. 1, no. 2, pp. 85-95, 2024.
P. Pulivarthy, “Optimising large-scale distributed data systems using intelligent load balancing algorithms,” AVE Trends in Intelligent Computing Systems, vol. 1, no. 4, pp. 219-230, 2024.
M. A. Raj, M. A. Thinesh, S. S. M. Varmann, A. R. Pothu, and P. Paramasivan, “Ensemble-based phishing website detection using Extra Trees classifier,” AVE Trends in Intelligent Computing Systems, vol. 1, no. 3, pp. 142-156, 2024.
M. P. Roja, M. Kuppam, S. K. R. Koduru, R. Byloppilly, S. D. Trivedi, and S. S. Rajest, “An aid of business intelligence in retailing services and experience using artificial intelligence,” in Cross-Industry AI Applications, IGI Global, USA, pp. 14-30, 2024.
A. R. Pothu, “Garlic Gate: Revolutionising application security with integrated artificial intelligence (AI) across SDLC, CI/CD, and advanced methodologies,” FMDB Transactions on Sustainable Computing Systems, vol. 2, no. 3, pp. 119-130, 2024.
R. Princy Reshma, S. Deepak, S. R. M. Tejeshwar, P. Deepika, and M. Saleem, “Online auction forecasting precision: Real-time bidding insights and price predictions with machine learning,” FMDB Transactions on Sustainable Technoprise Letters, vol. 1, no. 2, pp. 106-122, 2023.
S. S. Priscila, S. S. Rajest, S. N. Tadiboina, R. Regin, and A. S. András, “Analysis of machine learning and deep learning methods for superstore sales prediction,” FMDB Transactions on Sustainable Computer Letters, vol. 1, no. 1, pp. 1-11, 2023.
R. Regin, A. A. Khanna, V. Krishnan, M. Gupta, S. R. Rubin Bose, and S. S. Rajest, “Information design and unifying approach for secured data sharing using attribute-based access control mechanisms,” in Recent Developments in Machine and Human Intelligence, IGI Global, USA, pp. 256-276, 2023.
A. Thirunagalingam, “Bias detection and mitigation in data pipelines: Ensuring fairness and accuracy in machine learning,” AVE Trends in Intelligent Computing Systems, vol. 1, no. 2, pp. 116-127, 2024.
S. Singh, S. S. Rajest, S. Hadoussa, A. J. Obaid, and R. Regin, Data-Driven Intelligent Business Sustainability, IGI Global, USA, 2023, [Online]. Available: https://doi.org/10.4018/979-8-3693-0049-7.
G. Vadlakonda, “Advancing generative artificial intelligence (AI) through multimodal integration and contextual learning,” FMDB Transactions on Sustainable Computing Systems, vol. 2, no. 3, pp. 131-139, 2024.
M. Muthmainnah, A. Khang, A. J. Obaid, S. S. Rajest, and E. Hannah, Eds., AI-Driven Sustainable and Secure Smart Infrastructure Systems, IGI Global, USA, 2026, [Online]. Available: https://doi.org/10.4018/979-8-3373-2140-0.
A. J. Obaid, M. Muthmainnah, S. S. Rajest, and M. Baron, Eds., Multidisciplinary Advancements in Human-AI Augmentation, Advances in Computational Intelligence and Robotics, IGI Global, USA, 2025, [Online]. Available: https://doi.org/10.4018/979-8-3373-1987-2.
S. Singh, S. S. Rajest, S. Hadoussa, A. J. Obaid, and R. Regin, Eds., Data-Driven Decision Making for Long-Term Business Success, IGI Global, USA, 2023, [Online]. Available: https://doi.org/10.4018/979-8-3693-2193-5.
P. S. Venkateswaran, A. Nachimuthu, and R. Kandath, “Impact of product quality, ethical branding, and trust on brand loyalty and customer satisfaction in the body care industry,” FMDB Transactions on Sustainable Management Letters, vol. 3, no. 1, pp. 24-33, 2025.
R. Sharma, “Advancing research ecosystems through library-led open science advocacy and data stewardship,” FMDB Transactions on Sustainable Finance and Data Science, vol. 1, no. 1, pp. 52-64, 2026.
G. Wali, J. Christadoss, P. Sivathapandi, A. Kori, and C. Bulla, “Semantic lineage pipelines for automated data quality remediation in lakehouse architectures,” AVE Trends in Intelligent Computing Systems, vol. 3, no. 1, pp. 48-56, 2026.
A. S. Abdulbaqi, A. J. Obaid, and M. H. Abdulameer, “Smartphone-based ECG signals encryption for transmission and analyzing via IoMTs,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 24, no. 7, pp. 1979-1988, 2021.
R. Regin, M. K. Elaiyarani, R. Chandrasekhar, B. Sureshkumar, S. R. Bose, D. C. Pappa, S. S. Rajest, B. Singh, and S. Singh, “Data-driven predictive analytics for optimizing bank marketing systems,” in Advances in Computational Intelligence and Robotics, IGI Global, USA, pp. 203-222, 2026.
