Intelligent Transportation Systems in Smart City: A Systematic Survey

Hassan, Muhammad Abul; Javed, Roofia; Farhatullah,; Granelli, Fabrizio; Gen, Xin; Rizwan, Muhammad; Ali, Sadia; Junaid, Hazrat; Ullah, Sana

doi:10.1109/icrai57502.2023.10089543

Cited by 13 publications

(2 citation statements)

References 65 publications

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“…In recent years, in order to make smart cities [ 7 ], researchers have invented a number of technologies. As a result of this idea, smart sports such as smart cricket have become very important.…”

Section: Related Workmentioning

confidence: 99%

Deep Transfer Learning‐Based Foot No‐Ball Detection in Live Cricket Match

Das

Mahmud

Islam

et al. 2023

Computational Intelligence and Neuroscience

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Automation in every part of life has become a frequent situation because of the rapid advancement of technology, mostly driven by AI technology, and has helped facilitate improved decision-making. Machine learning and the deep learning subset of AI provide machines with the capacity to make judgments on their own through a continuous learning process from vast amounts of data. To decrease human mistakes while making critical choices and to improve knowledge of the game, AI-based technologies are now being implemented in numerous sports, including cricket, football, basketball, and others. Out of the most globally popular games in the world, cricket has a stronghold on the hearts of its fans. A broad range of technologies are being discovered and employed in cricket by the grace of AI to make fair choices as a method of helping on-field umpires because cricket is an unpredictable game, anything may happen in an instant, and a bad judgment can dramatically shift the game. Hence, a smart system can end the controversy caused just because of this error and create a healthy playing environment. Regarding this problem, our proposed framework successfully provides an automatic no-ball detection with 0.98 accuracy which incorporates data collection, processing, augmentation, enhancement, modeling, and evaluation. This study starts with collecting data and later keeps only the main portion of bowlers’ end by cropping it. Then, image enhancement technique are implied to make the image data more clear and noise free. After applying the image processing technique, we finally trained and tested the optimized CNN. Furthermore, we have increased the accuracy by using several modified pretrained model. Here, in this study, VGG16 and VGG19 achieved 0.98 accuracy and we considered VGG16 as the proposed model as it outperformed considering recall value.

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Section: Related Workmentioning

confidence: 99%

Deep Transfer Learning‐Based Foot No‐Ball Detection in Live Cricket Match

Das

Mahmud

Islam

et al. 2023

Computational Intelligence and Neuroscience

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“…Recently, smart cities have gained significant traction as urban areas embrace advanced technologies to improve efficiency, sustainability, and quality of life [1,2]. To integrate diverse urban systems and improve the quality of services provided to residents, smart cities incorporate cutting-edge technologies such as the Internet of Things (IoT), big data analytics, and artificial intelligence [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Secure and Privacy-Preserving Intrusion Detection and Prevention in the Internet of Unmanned Aerial Vehicles

Ntizikira,

Lei,

Alblehai

et al. 2023

Sensors

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In smart cities, unmanned aerial vehicles (UAVS) play a vital role in surveillance, monitoring, and data collection. However, the widespread integration of UAVs brings forth a pressing concern: security and privacy vulnerabilities. This study introduces the SP-IoUAV (Secure and Privacy Preserving Intrusion Detection and Prevention for UAVS) model, tailored specifically for the Internet of UAVs ecosystem. The challenge lies in safeguarding UAV operations and ensuring data confidentiality. Our model employs cutting-edge techniques, including federated learning, differential privacy, and secure multi-party computation. These fortify data confidentiality and enhance intrusion detection accuracy. Central to our approach is the integration of deep neural networks (DNNs) like the convolutional neural network-long short-term memory (CNN-LSTM) network, enabling real-time anomaly detection and precise threat identification. This empowers UAVs to make immediate decisions in dynamic environments. To proactively counteract security breaches, we have implemented a real-time decision mechanism triggering alerts and initiating automatic blacklisting. Furthermore, multi-factor authentication (MFA) strengthens access security for the intrusion detection system (IDS) database. The SP-IoUAV model not only establishes a comprehensive machine framework for safeguarding UAV operations but also advocates for secure and privacy-preserving machine learning in UAVS. Our model’s effectiveness is validated using the CIC-IDS2017 dataset, and the comparative analysis showcases its superiority over previous approaches like FCL-SBL, RF-RSCV, and RBFNNs, boasting exceptional levels of accuracy (99.98%), precision (99.93%), recall (99.92%), and F-Score (99.92%).

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