Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural N...Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning strategy.The modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in accuracy.It showcases its superiority over conventional detection algorithms in different road image scenarios.Although constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational efficiency.ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge computing.The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.展开更多
The importance of cybersecurity in contemporary society cannot be inflated,given the substantial impact of networks on various aspects of daily life.Traditional cybersecurity measures,such as anti-virus software and f...The importance of cybersecurity in contemporary society cannot be inflated,given the substantial impact of networks on various aspects of daily life.Traditional cybersecurity measures,such as anti-virus software and firewalls,safeguard networks against potential threats.In network security,using Intrusion Detection Systems(IDSs)is vital for effectively monitoring the various software and hardware components inside a given network.However,they may encounter difficulties when it comes to detecting solitary attacks.Machine Learning(ML)models are implemented in intrusion detection widely because of the high accuracy.The present work aims to assess the performance of machine learning algorithms in the context of intrusion detection,providing valuable insights into their efficacy and potential for enhancing cybersecurity measures.The main objective is to compare the performance of the well-knownML models using the UNSW-NB15 dataset.The performance of the models is discussed in detail with a comparison using evaluation metrics and computational performance.展开更多
基金supported by the National Natural Science Foundation of China(61170147)Scientific Research Project of Zhejiang Provincial Department of Education in China(Y202146796)+2 种基金Natural Science Foundation of Zhejiang Province in China(LTY22F020003)Wenzhou Major Scientific and Technological Innovation Project of China(ZG2021029)Scientific and Technological Projects of Henan Province in China(202102210172).
文摘Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning strategy.The modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in accuracy.It showcases its superiority over conventional detection algorithms in different road image scenarios.Although constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational efficiency.ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge computing.The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.
文摘The importance of cybersecurity in contemporary society cannot be inflated,given the substantial impact of networks on various aspects of daily life.Traditional cybersecurity measures,such as anti-virus software and firewalls,safeguard networks against potential threats.In network security,using Intrusion Detection Systems(IDSs)is vital for effectively monitoring the various software and hardware components inside a given network.However,they may encounter difficulties when it comes to detecting solitary attacks.Machine Learning(ML)models are implemented in intrusion detection widely because of the high accuracy.The present work aims to assess the performance of machine learning algorithms in the context of intrusion detection,providing valuable insights into their efficacy and potential for enhancing cybersecurity measures.The main objective is to compare the performance of the well-knownML models using the UNSW-NB15 dataset.The performance of the models is discussed in detail with a comparison using evaluation metrics and computational performance.
