FindMySpot: AI & AR Based Parking System

SSRN Electronic Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

A new initiative has been endorsed to develop a user-friendly smart parking system that operates with minimal human intervention. The proposed leverages computer vision and AR/VR technology create seamless experience. Upon arrival, the scans vehicle's license plate verifies user's details using combination of advanced techniques. suitable spot is then located based on real-time availability, user guided slot through an immersive navigation system. also integrates robust security features, employing sensors cameras for comprehensive surveillance, including automated recognition emergency alerting mechanisms. This dual-layer ensures vehicle safety while minimizing Additionally, platform offers mobile application, featuring payment processing, customizable interfaces, community page enhanced interaction. Our comparative analysis highlights system's superiority over traditional methods, which often rely static maps manual searching, leading inefficiencies dissatisfaction. On contrary, our recommended solution provides experience easy, effective safe especially considering difficulties presented by cities, most notably in places like India where this not common. information provided via application easy-to-use interface handling specifics, involvement. Furthermore, it internet-based storage acts as central place data management verification users. Proposed AI-driven systems have found satisfactorily meet demands showing functioned optimally high levels precision time plus features working perfectly even under conditions.

Language: Английский

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Challenges in Pursuing AI Transparency

The American Journal of Bioethics, Journal Year: 2025, Volume and Issue: 25(3), P. 4 - 6

Published: Feb. 24, 2025

Language: Английский

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Machine Learning Models and Mathematical Approaches for Predictive IoT Smart Parking

Sensors, Journal Year: 2025, Volume and Issue: 25(7), P. 2065 - 2065

Published: March 26, 2025

This paper aims to create an innovative approach improving IoT-based smart parking systems by integrating machine learning (ML) and Artificial Intelligence (AI) with mathematical approaches in order increase the accuracy of availability predictions. Three regression-based ML models, random forest, gradient boosting, LightGBM, were developed their predictive capability was compared using data collected from three locations Skopje, North Macedonia 2019 2021. The main novelty this study is based on use autoregressive modeling strategies lagged features Z-score normalization improve time series forecasts. Bayesian optimization chosen for its ability efficiently explore hyperparameter space while minimizing RMSE. able capture temporal dependencies more effectively than other resulting lower RMSE values. LightGBM model produced R2 0.9742 0.1580, making it best prediction. Furthermore, system architecture also deployed which included real-time collection sensors placed at entry exit lots individual slots. integration ML, AI, IoT technologies improves efficiency management system, reduces traffic congestion and, most importantly, offers a scalable development urban mobility solutions.

Language: Английский

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Integrating Machine Learning and AI into IoT-Enabled Smart Parking

Published: Jan. 1, 2025

Language: Английский

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Bluetooth-Based Dynamic Nexus Mesh Communication Network for High-Density Urban Interaction Spaces

Sensors, Journal Year: 2025, Volume and Issue: 25(8), P. 2495 - 2495

Published: April 15, 2025

Traditional centralized network structures exhibit clear scalability and communication efficiency bottlenecks. This paper proposes a solution based on bidirectional unweighted heterogeneous graph, Dynamic Nexus Mesh Communication, designed to improve optimize user experience, particularly for high-density urban interaction spaces. DNMC reduces the reliance central super nodes in traditional networks by redistributing centrality while increasing betweenness of broader set nodes. Additionally, introduces multiple node types, improving both robustness network. Through series simulation experiments, we compared performance with that networks. The results show achieves an average delay 1.5824 s, representing 13.69% improvement over architectures. These findings demonstrate significantly outperforms terms efficiency, at larger scales, where exhibits enhanced stability scalability.

Language: Английский

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Enhancing Smart Parking Management through Machine Learning and AI Integration in IoT Environments

IntechOpen eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 23, 2024

The integration of Internet Things (IoT) technology has profoundly transformed urban life, particularly in the realm parking management. Smart systems harness capabilities IoT to optimize space utilization, alleviate congestion, and elevate user experience. This chapter delves into intricate process data collection within IoT-enabled smart environments, with a specific emphasis on seamless machine learning artificial intelligence (AI) techniques. By conducting comprehensive analysis various sources, algorithms, AI technologies, this elucidates how leverage intelligent enhance operational efficiency effectiveness. Through convergence IoT, learning, AI, are poised revolutionize mobility drive sustainable development.

Language: Английский

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