Smart Engineering: Harnessing Intelligent Systems for Enhanced Performance
Keywords:
Smart Engineering, Intelligent Systems, Artificial Intelligence, Predictive Maintenance, Machine Learning, Internet of Things, Real-Time Analytics, System OptimizationAbstract
The fast development of intelligent systems has set the way for smart engineering revolution application in engineering, which altogether makes up the basis of smart engineering. This is an interdisciplinary field, which combines AI, ML, IoT with real-time data analytics to maximize systems performance, to reduce human errors, and to leverage predictive maintenance. The methodologies, applications, and performance implication of the smart engineering practices in the different sectors such as manufacturing, construction, energy, and transportation are discussed in this paper. We outline an in-depth summary of the existing research, explain our methodological approach of implementation of intelligent systems on an engineering framework, and review findings from practical implementations. The results verify strong enhancement of performance, cost, and decision making accuracy implicating the need to merge smart technologies into contemporary engineering implementation.
Downloads
References
M. H. Warsi and T. N. Kumar, “Advances in energy harnessing techniques for smart highways: a review,” Electrical Engineering, vol. 106, no. 5, pp. 6389–6408, Apr. 2024, doi: 10.1007/s00202-024-02379-8.
K. Dulaj, A. Alhammadi, I. Shayea, A. A. El-Saleh, and M. Alnakhli, “Harnessing machine learning for intelligent networking in 5G technology and beyond: advancements, applications and challenges,” IEEE Open Journal of Intelligent Transportation Systems, p. 1, Jan. 2025, doi: 10.1109/ojits.2025.3564361.
A. Waqar, “Intelligent decision support systems in construction engineering: An artificial intelligence and machine learning approaches,” Expert Systems With Applications, vol. 249, p. 123503, Feb. 2024, doi: 10.1016/j.eswa.2024.123503.
A. Adetunla, E. Akinlabi, T. C. Jen, and S.-S. Ajibade, “Harnessing the Power of Artificial intelligence in Materials Science: An Overview,” 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG), pp. 1–6, Apr. 2024, doi: 10.1109/seb4sdg60871.2024.10630185.
A. Chitkeshwar, “Revolutionizing Structural Engineering: Applications of machine learning for enhanced performance and safety,” Archives of Computational Methods in Engineering, Apr. 2024, doi: 10.1007/s11831-024-10117-3.
A. Wahid, J. G. Breslin, and M. A. Intizar, “TCRSCANET: Harnessing Temporal convolutions and recurrent Skip component for enhanced RUL estimation in mechanical systems,” Human-Centric Intelligent Systems, vol. 4, no. 1, pp. 1–24, Jan. 2024, doi: 10.1007/s44230-023-00060-0.
K. Meng, C. Masouros, K.-K. Wong, A. P. Petropulu, and L. Hanzo, “Integrated Sensing and Communication Meets Smart Propagation Engineering: Opportunities and challenges,” IEEE Network, p. 1, Jan. 2025, doi: 10.1109/mnet.2025.3527130.
A. Harika, G. Balan, H. P. Thethi, A. Rana, K. V. Rajkumar, and M. A. Al-Allak, “Harnessing the power of artificial intelligence for disaster response and crisis management,” 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE), pp. 1237–1243, May 2024, doi: 10.1109/ic3se62002.2024.10593506.
K. Yu, W. Chen, D. Deng, Q. Wu, and J. Hao, “Advancements in battery monitoring: harnessing fiber grating sensors for enhanced performance and reliability,” Sensors, vol. 24, no. 7, p. 2057, Mar. 2024, doi: 10.3390/s24072057.
D. K. Pandey and R. Mishra, “Towards sustainable agriculture: Harnessing AI for global food security,” Artificial Intelligence in Agriculture, vol. 12, pp. 72–84, Apr. 2024, doi: 10.1016/j.aiia.2024.04.003.
M. K. Abdel-Fattah et al., “Quantitative evaluation of soil quality using principal component analysis: The case study of El-Fayoum Depression Egypt,” Sustainability, vol. 13, no. 4, p. 1824, Feb. 2021, doi: 10.3390/su13041824.
I. Ahmad et al., “Deep learning based detector YOLOV5 for identifying insect pests,” Applied Sciences, vol. 12, no. 19, p. 10167, Oct. 2022, doi: 10.3390/app121910167.
R. Akhter and S. A. Sofi, “Precision agriculture using IoT data analytics and machine learning,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 8, pp. 5602–5618, Jun. 2021, doi: 10.1016/j.jksuci.2021.05.013.
C. Anilkumar et al., “Gene based markers improve precision of genome-wide association studies and accuracy of genomic predictions in rice breeding,” Heredity, vol. 130, no. 5, pp. 335–345, Feb. 2023, doi: 10.1038/s41437-023-00599-5.
T. A. Shaikh, T. Rasool, and F. R. Lone, “Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming,” Computers and Electronics in Agriculture, vol. 198, p. 107119, Jun. 2022, doi: 10.1016/j.compag.2022.107119.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.