Ijraset Journal For Research in Applied Science and Engineering Technology
Authors: Mythili J, Keshini A, Priyadharshini R, Vyshnavi B
DOI Link: https://doi.org/10.22214/ijraset.2025.74680
Certificate: View Certificate
Adaptive web portals play an increasingly significant role in shaping decentralized community engagement, particularly in digital environments where inclusivity, accessibility, and sustained interaction are paramount. The architectural foundation of these portals—whether developed through full stack frameworks or content management systems (CMS)—directly influences their adaptability, scalability, and user experience. This study proposes a comparative investigation into how full stack and CMS-based portals mediate participation in decentralized communities. Two prototypes will be developed: one using a full stack framework and the other on a CMS platform. Both systems will implement identical baseline functionalities, enabling a controlled comparison of usability, customization, scalability, and inclusivity. Evaluation will combine technical metrics, such as load time and scalability, with user-centered measures, including task completion, accessibility perception, and engagement frequency. A diverse participant group will be recruited to reflect variations in digital literacy. The anticipated outcome is a nuanced understanding of architectural trade-offs and their socio-technical implications, with practical recommendations for hybrid models that balance customization with accessibility. This research aims to contribute to the discourse on digital inclusivity by providing design strategies that integrate semantic web technologies, AI-driven personalization, and participatory governance systems into adaptive portals for decentralized engagement.
The rise of decentralized communities has reshaped digital interaction, emphasizing collective ownership, distributed governance, and inclusive participation. Adaptive web portals, which respond dynamically to user needs, are central to supporting these communities. Portal architecture—whether built via full stack development or content management systems (CMS)—affects engagement, usability, inclusivity, and long-term sustainability. Full stack development offers deep customization, modularity, and advanced interactive features but requires significant technical expertise and resources. CMS platforms provide rapid deployment, ease of administration, and accessibility for non-technical users, though they have limits in scalability and advanced functionality.
Existing research highlights digital inclusivity, adaptive design, semantic web integration, and socio-technical engagement but lacks direct comparisons between full stack and CMS portals in decentralized community contexts. This study addresses that gap by developing two functionally equivalent prototypes—one full stack and one CMS—and evaluating them through technical testing and user-centered assessments across diverse digital literacy levels. The research aims to compare performance, usability, and inclusivity, explore socio-technical trade-offs, and propose hybrid approaches that combine the strengths of both architectures. Findings are expected to guide designers, policymakers, and community leaders in building adaptive, inclusive, and sustainable digital infrastructures.
This study has proposed a methodological framework for evaluating adaptive web portals developed through full stack frameworks and content management systems (CMS). The comparative approach outlined demonstrates that architectural choices in web development are not merely technical decisions but also socio-technical ones with direct implications for inclusivity, accessibility, and long-term sustainability. The results anticipated suggest that while full stack development offers greater scalability, extensibility, and technical control, it is accompanied by higher maintenance demands and a steeper learning curve that may exclude communities with limited resources. Conversely, CMS-based solutions emphasize rapid deployment and inclusivity, lowering technical barriers to entry but limiting customization and extensibility as community needs evolve. By embedding rigorous software testing techniques—spanning unit, integration, system, performance, and usability testing—this study highlights the importance of ensuring reliability and quality not only in technical terms but also in human-centered dimensions such as inclusivity and governance. In doing so, the research extends the traditional scope of software testing frameworks, encouraging the inclusion of socio-technical reliability as an evaluative lens. The study further underscores that no single approach provides a universal solution. Instead, adaptive portals may benefit from hybrid strategies that combine the accessibility of CMS platforms with the scalability and flexibility of full stack development. Such models could better align with the diverse governance models and digital literacy profiles of decentralized communities. The broader implication of this work is that adaptive web portals represent more than a technological innovation; they are infrastructures for collective participation, cultural exchange, and community governance. By advancing methodological approaches that balance technical rigor with inclusivity, this research contributes to the evolving discourse on digital platforms as socio-technical systems. Future research should explore hybrid models, semantic web integration, and AI-driven personalization to further refine adaptive web portal design. Ultimately, the contribution of this study lies in demonstrating that software testing methodologies can be expanded to evaluate not only the reliability of systems but also the trustworthiness and inclusivity that sustain long-term community engagement.
[1] Al-Badi, A., Tarhini, A., & Al-Sawaei, S. (2017). Utilizing social media to encourage domestic tourism in Oman. International Journal of Business and Management, 12(4), 84–94. https://doi.org/10.5539/ijbm.v12n4p84 [2] Balalaie, A., Heydarnoori, A., & Jamshidi, P. (2016). Microservices architecture enables DevOps: Migration to a cloud-native architecture. IEEE Software, 33(3), 42–52. https://doi.org/10.1109/MS.2016.64 [3] Bozkurt, A. (2020). Educational technology research patterns in the pre- and post-COVID-19 era: A bibliometric analysis. Asian Journal of Distance Education, 15(1), 183–209. [4] Fowler, M., & Lewis, J. (2014). Microservices: A definition of this new architectural term. ThoughtWorks. https://martinfowler.com/articles/microservices.html [5] Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Keele University. [6] Nash, C. (2019). Accessibility and digital inclusion: Implications for higher education. Research in Learning Technology, 27, 1–13. https://doi.org/10.25304/rlt.v27.2114 [7] Pressman, R. S., & Maxim, B. R. (2020). Software engineering: A practitioner’s approach (9th ed.). McGraw-Hill Education. [8] Sharma, R., & Sehgal, R. (2019). Comparative analysis of CMS and framework-based web applications. International Journal of Computer Applications, 178(6), 10–15. https://doi.org/10.5120/ijca2019918733 [9] Sommerville, I. (2016). Software engineering (10th ed.). Pearson. [10] Zhou, Y., Leung, H., & Zhang, B. (2018). A systematic review of software fault prediction studies. Information and Software Technology, 95, 41–61. https://doi.org/10.1016/j.infsof.2017.11.013 [11] J. Viji Gripsy, “Biological software for recognition of specific regions in organisms,” Bioscience Biotechnology Research Communications, vol. 13, no. 1, pp. —, Mar. 2020. doi: 10.21786/bbrc/13.1/54. [12] J. Viji Gripsy and A. Jayanthiladevi, “Energy hole minimization in wireless mobile ad hoc networks using enhanced expectation-maximization,” in Proc. 2023 9th Int. Conf. Adv. Comput. Commun. Syst. (ICACCS), Mar. 2023, pp. 1012–1019. doi: 10.1109/ICACCS57279.2023.10112728 [13] J. Viji Gripsy and A. Jayanthiladevi, “Energy optimization and dynamic adaptive secure routing for MANET and sensor network in IoT,” in Proc. 2023 7th Int. Conf. Comput. Methodol. Commun. (ICCMC), Feb. 2023, pp. 1283–1290. doi: 10.1109/iccmc56507.2023.10083519. [14] S. Karpagavalli, J. V. Gripsy, and K. Nandhini, “WITHDRAWN: Speech assistive Tamil learning mobile applications for learning disability children,” Materials Today: Proceedings, Feb. 2021. doi: 10.1016/j.matpr.2021.01.050. [15] J. Viji Gripsy, “Trust-based secure route discovery method for enhancing security in mobile ad-hoc networks,” Int. J. Sci., Eng. Technol., vol. 13, no. 1, Jan. 2025. doi: 10.61463/ijset.vol.13.issue1.147. [16] J. Viji Gripsy, N. A. Selvakumari, L. Sheeba, and B. Senthil Kumaran, “Transforming student engagement through AI, AR, VR, and chatbots in education,” in Chatbots in Educational Leadership and Management, Feb. 2025, pp. 73–100. doi: 10.4018/979-8-3693-8734-4.ch004. [17] A. S. Vijendran and J. V. Gripsy, “Enhanced secure multipath routing scheme in mobile ad hoc and sensor networks,” in Proc. 2nd Int. Conf. Current Trends Eng. Technol. (ICCTET), Jul. 2014. doi: 10.1109/icctet.2014.6966289. [18] K. V. Greeshma and J. V. Gripsy, “RadientFusion-XR: A hybrid LBP–HOG model for COVID-19 detection using machine learning,” Biotechnol. Appl. Biochem., Jul. 2025. doi: 10.1002/bab.70020. [19] T. Divya and J. V. Gripsy, “Lung disease classification using deep learning 1-D convolutional neural network,” Int. J. Data Min., Model. Manage., 2025. doi: 10.1504/ijdmmm.2025.10066898. [20] J. Viji Gripsy, “Hybrid deep learning framework for crop yield prediction and weather impact analysis,” Int. J. Res. Appl. Sci. Eng. Technol., Aug. 2025. doi: 10.22214/ijraset.2025.73800. [21] J. Viji Gripsy and K. R. Kanchana, “Relaxed hybrid routing to prevent consecutive attacks in mobile ad-hoc networks,” Int. J. Internet Protocol Technol., vol. 16, no. 2, 2023. doi: 10.1504/ijipt.2023.131292. [22] J. Viji Gripsy, M. Sowmya, N. Sharmila Banu, D. Kumar, and B. Senthilkumaran, “Qualitative research methods for professional competencies in educational leadership,” in Research Methods for Educational Leadership and Management, May 2025, pp. 213–236. doi: 10.4018/979-8-3693-9425-0.ch009. [23] J. Viji Gripsy and A. Jayanthiladevi, “Optimizing secure routing for mobile ad-hoc and WSN in IoT through dynamic adaption and energy efficiency,” in Intelligent Wireless Sensor Networks and the Internet of Things, May 2024, pp. 45–65. doi: 10.1201/9781003474524-3. [24] A. S. Vijendran and J. Viji Gripsy, “RECT zone based location-aided routing for mobile ad hoc and sensor networks,” Asian J. Sci. Res., vol. 7, no. 4, pp. 472–481, Sep. 2014. doi: 10.3923/ajsr.2014.472.481. [25] T. Divya and J. Viji Gripsy, “Machine learning algorithm for lung cancer classification using ADASYN with standard random forest,” Int. J. Data Min. Bioinformatics, 2025. doi: 10.1504/ijdmb.2025.10065391. [26] J. Viji Gripsy and T. Divya, “Lung cancer prediction using combination of oversampling with standard random forest algorithm for imbalanced dataset,” in Algorithms for Intelligent Systems, 2024. doi: 10.1007/978-981-97-3191-6_1. [27] J. Viji Gripsy and K. R. Kanchana, “Relaxed hybrid routing to prevent consecutive attacks in mobile ad-hoc networks,” Int. J. Internet Protocol Technol., vol. 16, no. 2, 2023. doi: 10.1504/ijipt.2023.10056776. [28] J. V. Gripsy, N. A. Selvakumari, S. S. Hameed, and M. J. Begam, “Drowsiness detection in drivers: A machine learning approach using Hough circle classification algorithm for eye retina images,” in Applied Data Science and Smart Systems, Jun. 2024, pp. 202–208. doi: 10.1201/9781003471059-28. [29] A. S. Vijendran and J. Viji Gripsy, “Performance evaluation of ASMR with QRS and RZLSR routing scheme in mobile ad-hoc and sensor networks,” Int. J. Future Gener. Commun. Netw., vol. 7, no. 6, Dec. 2014. doi: 10.14257/ijfgcn.2014.7.6.05. [30] J. Viji Gripsy, R. Kowsalya, T. Thendral, A. SenthilKumar, J. T. Mesia Dhas, and L. Sheeba, “Integrating AI and blockchain for cybersecurity insurance in risk management for predictive analytics in insurance,” in Harnessing Data Science for Sustainable Insurance, Jul. 2025. doi: 10.4018/979-8-3373-1882-0.ch013. [31] R. Kowsalya, J. Viji Gripsy, C. V. Banupriya, and R. Sathya, “Social impact of technology for sustainable development: A digital distraction detection approach,” in Lecture Notes in Networks and Systems, 2025, pp. 245–256. doi: 10.1007/978-981-96-6063-6_22. [32] J. Viji Gripsy and M. Sasikala, “Nature-inspired optimized artificial bee colony for decision making in energy-efficient wireless sensor networks,” in Advances in Computational Intelligence and Robotics, May 2024, pp. 89–104. doi: 10.4018/979-8-3693-2073-0.ch006. [33] J. Viji Gripsy and A. S. Kavitha, “Survey on environmental issues of green computing,” Indian J. Appl. Res., vol. 4, no. 2, pp. 156–160, Oct. 2011. doi: 10.15373/2249555x/feb2014/34. [34] K. V. Greeshma and J. Viji Gripsy, “A review on classification and retrieval of biomedical images using artificial intelligence,” in Internet of Things, 2021, pp. 23–38. doi: 10.1007/978-3-030-75220-0_3. [35] J. Viji Gripsy, M. Sasikala, and R. Maneendhar, “Classification of cyber attacks in Internet of Medical Things using particle swarm optimization with support vector machine,” in Lecture Notes in Networks and Systems, 2024, pp. 301–315. doi: 10.1007/978-3-031-61929-8_26. [36] J. Viji Gripsy, B. Lukose, L. Sheeba, J. T. M. Dhas, R. Jayasree, and N. V. Brindha, “Enhancing cybersecurity insurance through AI and blockchain for proactive risk management,” in Advances in Computational Intelligence and Robotics, May 2025, pp. 349–376. doi: 10.4018/979-8-3373-1977-3.ch012. [37] M. Mehala and J. V. Gripsy, “Voice based medicine remainder alert application for elder people,” Int. J. Recent Technol. Eng. (IJRTE), vol. 8, no. 6, Mar. 2020, PP: 2284-2289 doi: 10.35940/ijrte.f7731.038620. [38] J. Viji Gripsy, “A hybrid RFR–BiLSTM framework for social media engagement and web traffic prediction,” Int. J. Sci. Res. Comput. Sci., Eng. Inf. Technol., Volume 11 , Issue 4, Aug. 2025. doi: 10.32628/cseit25111691. [39] G. Bharathi, R. N. M. Vidhya, J. V. Gripsy, J. Mythili, and D. Suganthi, “DRBRO–Dynamic reinforcement based route optimization for efficient route discovery in mobile ad-hoc networks,” Int. J. Res. Publ. Rev., vol. 6, Issue 2, Feb. 2025, pp 1804-1806. doi: 10.55248/gengpi.6.0225.0768.
Copyright © 2025 Mythili J, Keshini A, Priyadharshini R, Vyshnavi B. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET74680
Publish Date : 2025-10-17
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here
Submit Paper Online
