An Informative Perturbation Network for the Design of Colonel Adaptive AI Systems

An Informative Perturbation Network for the Design of Colonel Adaptive AI Systems Vanaparthi Kiranmai Dr.A. Manikandan

Continual learning presents a nontrivial problem in artificial intelligence, making the creation of adaptive algorithms that can retain prior knowledge across a range of tasks critical. This paper examines the practicality of different strategies for solving incremental tasks and the object recognition problem on the CIFAR-100 dataset. New strategies, such as the Beneficial Perturbation Network (BPN) variants BD + EWC, PSP, and BD + PSP, which aim to improve the flexibility and efficiency of adaptive and robust solutions to continual learning problems, are designed in the study. The inability to adapt to the constantly changing conditions of the wireless mobile system is resolved, and security concerns, such as exposing the system, data, and users' private information, are minimized. An increasing focus on their performance in terms of accuracy and computing costs defines the trajectory of the study. Accuracy results show that BD+PSP surpassed the rest with 90.65% followed by PSP's 90.01% and BD+EWC's 89.95%. In addition, the model shows improvement in energy efficiency and reduced computation costs, enhancing its applicability to mobile ad hoc and vehicular networks. Cost assessments reflective of the workflow “cost per task per 4,039 bytes” indicate that BD+EWC maintains the lower boundary, whereas for PSP and BD+PSP, the boundaries are 10,897 bytes and 11,456 bytes, respectively. Accuracy progression of the increments within the CIFAR-100 range, shows, quite strikingly, that BD+PSP and some other techniques are dominant in knowledge retention while performing progressive tasks. The findings outlined in this paper indicate that the IPN framework has promising prospects for intelligent computing environments to facilitate dynamic resource allocation and restructuring. The analysis about techniques illustrates advances, especially in object recognition. In General, data underlie the primary effectiveness of bias-decoupled learning techniques, along with the auxiliary positive impact of the learning flexibility and strength of AI systems under continuous learning conditions. This type of information is essential for the design of algorithms which can readily accommodate real-world operations with dynamic and complicated processing sequences.
12 15 2025 12 15 2025 454 477 10.58346/JOWUA.2025.I4.026 https://jowua.com/wp-content/uploads/2025/12/2025.I4.026.pdf