An Intelligent Smart Parking System Using Convolutional Neural Network

Alsheikhy, Ahmed A.; Shawly, Tawfeeq; Said, Yahia; Lahza, Husam

doi:10.1155/2022/7571716

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…But it failed to minimize congestion issue in Seattle city. A new Convolutional Neural Network (CNN) was introduced in [5] for vehicle parking and management. However, the error rate in the prediction of vehicle parking was not minimized.…”

Section: Literature Studymentioning

confidence: 99%

Cox Regressive Outlier Robust Incremental Extreme Learning Machine based IoT aware Smart Car Parking and Route Optimization for Intelligent Transportation

V. Subapriya

2024

jes

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An Intelligent Transport System (ITS) is a wide-ranging framework that influences advanced technologies to improve efficiency of transportation networks. An ITS integrated with an IoT enhances the capabilities of transportation networks. Owing to quick development of vehicles, traffic congestion during road network is major problems. Therefore, intelligent traffic control system is required to create smart transportation through optimization of vehicle route, and so on. In this paper, Cox Regressive Fuzzified Outlier Robust Incremental Extreme Learning Machine (CRFORIELM) is developed for smart ITS. The IoT technology in IT’S is used to collect traffic data, vehicle information, Parking slot occupancy data. After data collection process, proposed CRFORIELM technique includes two major processing steps namely parking slot availability detection and traffic aware route optimization. The extreme learning machine includes three types of layers such as input, three hidden layers as well as output layer. Initial hidden layer predicts the parking slot availability using Cox regression is performed. With the parking lot availability predictions, integrate this information into a route optimization algorithm. This algorithm considers the real-time traffic conditions as well as parking availability for detecting optimal route by applying fuzzy triangular membership function in the second hidden layer. Finally, nearest route path is effectively determined to minimize travel time in intelligent transport system. Experimental analysis indicates that the CRFORIELM technique achieved 2%, 5%, 10% improvement in accuracy, sensitivity, specificity and reduced error rates, route detection time by 39%, and 36%, compared to existing methods.

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Section: Literature Studymentioning

confidence: 99%

Cox Regressive Outlier Robust Incremental Extreme Learning Machine based IoT aware Smart Car Parking and Route Optimization for Intelligent Transportation

V. Subapriya

2024

jes

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“…The AlexNet network model consists of five convolutional layers, three pooling layers, three fully connected layers, and a dropout layer added to prevent overfitting [40]. The first and second convolutional layers have convolutional kernel sizes of 11 × 11 and 5 × 5, respectively.…”

Section: Modified Deep Cnn Model For Forest Fire Recognitionmentioning

confidence: 99%

A Forest Fire Recognition Method Based on Modified Deep CNN Model

Zheng,

Zou,

Gao

et al. 2024

Forests

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Controlling and extinguishing spreading forest fires is a challenging task that often leads to irreversible losses. Moreover, large-scale forest fires generate smoke and dust, causing environmental pollution and posing potential threats to human life. In this study, we introduce a modified deep convolutional neural network model (MDCNN) designed for the recognition and localization of fire in video imagery, employing a deep learning-based recognition approach. We apply transfer learning to refine the model and adapt it for the specific task of fire image recognition. To combat the issue of imprecise detection of flame characteristics, which are prone to misidentification, we integrate a deep CNN with an original feature fusion algorithm. We compile a diverse set of fire and non-fire scenarios to construct a training dataset of flame images, which is then employed to calibrate the model for enhanced flame detection accuracy. The proposed MDCNN model demonstrates a low false alarm rate of 0.563%, a false positive rate of 12.7%, a false negative rate of 5.3%, and a recall rate of 95.4%, and achieves an overall accuracy of 95.8%. The experimental results demonstrate that this method significantly improves the accuracy of flame recognition. The achieved recognition results indicate the model’s strong generalization ability.

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