Developing Cost-Effective AI Algorithms for Resource-Constrained Devices - Hind Khlaid Hameed (International Journal of Computers and Informatics, 2025)
Resource-restrained gadgets pose sizable challenges for deploying synthetic intelligence (AI) packages, which include restricted computational power, reminiscence, and electricity resources. This research pursuits to broaden value-effective AI algorithms that cope with these limitations whilst retaining high overall performance and accuracy. The examine leverages superior optimization strategies, such as version pruning, quantization, and dynamic strength control, to layout light-weight models appropriate for low-strength environments. Experiments conducted on gadgets like the Raspberry Pi 4 and NVIDIA Jetson Nano screen giant improvements in inference time, electricity efficiency, and accuracy compared to conventional processes. The proposed algorithms acquire up to 50% reduction in energy consumption and 20% improvement in accuracy at the same time as lowering typical computational charges. These findings reveal the feasibility of deploying green AI solutions on constrained hardware without compromising on functionality or nice. The practical implications of this paintings make bigger to various applications, along with real-time healthcare monitoring, clever agriculture, and commercial IoT systems. The have a look at concludes by means of highlighting areas for destiny studies, which includes improving algorithmic adaptability and expanding trying out to embody diverse eventualities. This work gives a sturdy basis for advancing the deployment of AI in resource-restricted settings, bridging the gap between technological innovation and practical implementation.