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Enhancing Smart Cities with Dual-Branch Residual Networks for Urban Sound Classification

Researchers from China Jiliang University and Hangzhou Aihua Intelligent Technology Co., Ltd. propose a novel approach using dual-branch residual networks to enhance urban environmental sound classification in smart cities. By accurately identifying and classifying various sounds, this advanced system offers valuable insights for city management, security, environmental monitoring, traffic management, and urban planning, leading to more livable and sustainable urban environments.

2 Aug 2023

Hybrid Quantum Machine Learning for Cars: Navigating Natural Disasters Safely

This research paper introduces a novel approach using supervised hybrid quantum machine learning to improve emergency evacuation strategies for cars during natural disasters like earthquakes. The proposed method combines quantum and classical machine learning techniques, demonstrating superior accuracy and efficiency compared to conventional algorithms, and holds promise for real-world applications in dynamic environments.

2 Aug 2023

Revolutionizing Smart Transportation: Enhancing Incident Detection and Traffic Management with Hybrid Deep Learning

Researchers propose the Hybrid Deep Learning-based Automated Incident Detection and Management (HDL-AIDM) system, utilizing intelligent algorithms and deep learning techniques to enhance incident detection accuracy and optimize traffic management in smart transportation systems. The system combines the power of deep learning with data augmentation using Generative Adversarial Networks (GANs) and introduces an intelligent traffic management algorithm that dynamically adjusts traffic flow based on real-time incident detection data.

14 Jul 2023

Traffic Flow News and Research

Enhancing Smart Cities with Dual-Branch Residual Networks for Urban Sound Classification

Hybrid Quantum Machine Learning for Cars: Navigating Natural Disasters Safely

Revolutionizing Smart Transportation: Enhancing Incident Detection and Traffic Management with Hybrid Deep Learning

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