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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

David Mamani-Pari

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Review Open access Aug 2026

Parking space detection using computer vision: a systematic review of the literature

Introduction Automated parking-space detection is an increasingly important component of intelligent urban mobility because inefficient parking searches contribute to travel delays, congestion, fuel consumption, and emissions. Methods This systematic literature review, conducted using Kitchenham's methodology and the P...

Gary Fernando Yunganina Mamani, Guver Leon Cori Coarite, Jhon A. Chambi Vilca et al. · 0 citations
Open access Aug 2026

Comparative Evaluation of Deep Learning Object Detectors for Real-Time Parking Occupancy Detection Under Variable Lighting Conditions

Efficient parking space management in urban settings represents a growing challenge owing to the sustained increase in the vehicle fleet. This study presents a comparative evaluation of five object detection architectures —YOLOv8s, YOLOv11s, YOLOv12s, RT-DETR-L and Faster R-CNN—applied to real-time intelligent vehicle...

Fernando G. Yunganina Mamani, Guver L. Ccori Coarite, Jhon A. Chambi Vilca et al. · 0 citations
Open access Sep 2026

Predicting global horizontal irradiance using regression-based machine learning models in high-altitude environments

Global solar radiation is a fundamental component of the Earth's energy balance and plays a critical role in integrating renewable energy into electrical systems. However, its pronounced variability, particularly in high-altitude regions, constrains energy stability and complicates planning processes. This study evalua...

Milton Edward Humpiri-Flores, David Mamani-Pari, Danny Lévano et al. · 0 citations
Review Open access Aug 2026

Accuracy–latency trade-offs and absent embedded validation in deep learning driver drowsiness detection: a PRISMA systematic review

This systematic literature review synthesizes findings from 33 peer-reviewed studies published between January 2021 and October 2025 to examine the architectures, biomarkers, performance benchmarks, and deployment barriers of deep-learning-based drowsiness detection.

Christian Wilbert Salas Yupanqui, Cristhian Edy Llanque Tipo, Frank Diego Choquehuanca Huayhua et al. · 0 citations

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