TARU PUBLICATIONS
Journal of Interdisciplinary Mathematics cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-012X·ISSN (Print): 0972-0502

Freq.: MONTHLY - Publishes the methodological and theoretical role of mathematics and mathematical applications underpinning scientific research.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
submissions@tarupublications.com
Open Access Research Article

Integrating AI-driven mathematical modeling for social and environmental impact assessment

, , , *

* Corresponding author · click or hover a name for details

pp. 637–644Vol. 29Issue 3March 2026DOI: 10.47974/JIM-2498XML
Received:
01 Apr 2025
Published Online:
18 Mar 2026
Article type:
Research Article
Language:
EN
Article no.:
JIM-2498
Pages:
637–644

Abstract

Through this research, a new method is presented that involves a sophisticated mathematical modeling and artificial intelligence (AI) in order to simplify the process of determining the influence that things have on people and the world. The proposed technique dynamically adjusts model parameters using AI-based techniques such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN) and predicts more effectively in challenging effect analysis. The framework is developed on a vector measure space, by which it is ensured that the maths is stable and converges rigorously. This assists individuals in making intelligent decisions that will result in sustainable development. The analysis indicates that orthogonal sets and sequences, as well as AI-based models, can be useful in approximating the non-linear relationships as well as errors that occur in the real world.  

Keywords

Subject Classifications

00A71

References

[1] D. Javeed, U. M. Badamasi, T. Iqbal, A. Umar, and C. O. Ndubuisi, “Threat detection using machine/deep learning in IoT environments,” International Journal of Computer Networks and Communications Security, vol. 8, pp. 59–65 (2020).
[2] K. H. Le, M. H. Nguyen, T. D. Tran, and N. D. Tran, “IMIDS: An intelligent intrusion detection system against cyber threats in IoT,” Electronics, vol. 11, pp. 524 (2022).
[3] F. M. Alrowais, S. Althahabi, S. S. Alotaibi, A. Mohamed, M. A. Hamza, and R. Marzouk, “Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment,” Computer Systems Science and Engineering, vol. 45, pp. 687–700 (2023).
[4] P. E. Bhakve, A. N. Jambhale, and V. S. Kumbhar, “Online Payment Fraud Detection using Machine Learning,” International Journal of Advanced Electronics and Communication Engineering (IJAECE), vol. 14, no. 1, pp. 1–7 (Apr. 2025).
[5] M. R. Labu and M. F. Ahammed, “Next-Generation Cyber Threat Detection and Mitigation Strategies: A Focus on Artificial Intelligence and Machine Learning,” Journal of Computer Science and Technology Studies, vol. 6, pp. 179–188 (2024).
[6] P. K. Shukla, S. V. Pandit, C. Gandhi, M. Alrizq, A. Alghamdi, P. K. Shukla, and A. Rizwan, “Effective privacy preserving model based on adversarial CNN with IBOA in the social IoT systems for CEC,” International Journal of Communication Systems, vol. 2024, pp. e5669 (2024).
[7] R. Chataut, A. Phoummalayvane, and R. Akl, “Unleashing the Power of IoT: A Comprehensive Review of IoT Applications and Future Prospects in Healthcare, Agriculture, Smart Homes, Smart Cities, and Industry 4.0,” Sensors, vol. 23, pp. 7194 (2023).
[8] L. Tawalbeh, F. Muheidat, M. Tawalbeh, and M. Quwaider, “IoT Privacy and Security: Challenges and Solutions,” Applied Sciences, vol. 10, pp. 4102 (2020).
[9] S.-H. Lee, Y.-L. Shiue, C.-H. Cheng, Y.-H. Li, and Y.-F. Huang, “Detection and Prevention of DDoS Attacks on the IoT,” Applied Sciences, vol. 12, pp. 12407 (2022).
[10] Q. Wang, P. Geng, H. Qiu, J. Zhang, and R. Zhao, “Development of intelligent automatic water intake system for open-pit mine,” Journal of Physics: Conference Series, vol. 2591, pp. 012057 (2023).
[11] A. Balaji, N. N. V. Gopi Chand, P. H. V. Hasmitha, and M. Viswa Prakash, “Power quality enhancement in renewable energy-based distributed generation using DPFC,” International Journal for Interdisciplinary Sciences and Engineering Applications (IJISEA), vol. 6, no. 2, pp. 46–51 (2025).
[12] P. Sahane, M. Gulhane, N. Rakesh, S. M. M. Naidu, M. Grover, and V. Mahajan, “Evaluating lightweight encryption schemes for resource-constrained devices in wireless sensor networks,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 5-A, pp. 1803–1812 (2025), doi: 10.47974/JDMSC-2180.

Views: 73Downloads: 10Citations: 0