Deep convolutional neural networks architecture for an efficient emergency vehicle classification in real-time traffic monitoring

Kherraki, Amine; Ouazzani, Rajae El

doi:10.11591/ijai.v11.i1.pp110-120

Cited by 33 publications

(22 citation statements)

References 28 publications

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“…Algorithms are trained to produce models using statistical approaches, revealing significant insights into data mining initiatives [19]. Following that, these insights drive decision-making within applications, with the goal of influencing important growth indicators [20].…”

Section: Machine Learningmentioning

confidence: 99%

Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT

Grari¹,

Idrissi²,

Boukabous³

et al. 2022

IJEECS

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<span lang="EN-US">The impact of wildfires, even following the fire's extinguishment, continues to affect harmfully public health and prosperity. Wildfires are becoming increasingly frequent and severe, and make the world's biodiversity in a growing serious danger. The fires are responsible for negative economic consequences for individuals, corporations, and authorities. Researchers are developing new approaches for detecting and monitoring wildfires, that make use of advances in computer vision, machine learning, and remote sensing technologies. IoT sensors help to improve the efficiency of detecting active forest fires. In this paper, we propose a novel approach for predicting wildfires, based on machine learning. It uses a regression model that we train over NASA's fire information for resource management system (FIRMS) dataset to predict fire radiant power in megawatts. The analysis of the obtained simulation results (more than 99% in the R2 metric) shows that the ensemble learning model is an effective method for predicting wildfires using an IoT device equipped with several sensors that could potentially collect the same data as the FIRMS dataset, such as smart cameras or drones.</span>

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Section: Machine Learningmentioning

confidence: 99%

Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT

Grari¹,

Idrissi²,

Boukabous³

et al. 2022

IJEECS

View full text Add to dashboard Cite

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“…Deep learning-based computer vision can recognize objects in images, recognize faces, and read text. It can also be used for automatic image tagging and classification [8].…”

Section: A Computer Vision (Cv)mentioning

confidence: 99%

Forest Fires Detection using Deep Transfer Learning

YANDOUZI¹,

GRARI²,

IDRISSI³

et al. 2022

IJACSA

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Forests are vital ecosystems composed of various plant and animal species that have evolved over years to coexist. Such ecosystems are often threatened by wildfires that can start either naturally, as a result of lightning strikes, or unintentionally caused by humans. In general, human-caused fires are more severe and expensive to fight because they are frequently located in inaccessible areas. Wildfires can spread quickly and become extremely dangerous, causing damage to homes and facilities, as well as killing people and animals. Early discovery of wildfires is vital to protect lives, property, and resources. Reinforced imaging technologies can play a key role to detect wildfires earlier. By applying deep learning (DL) over a dataset of images (collected using drones, planes, and satellites), we target to automate the forest fire detection. In this paper, we focus on building a DL model specifically to detect wildfires using transfer learning techniques from the best pretrained DL computer vision architectures available nowadays, such as VGG16,

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“…As a result of recent advancements, the field has attracted a great deal of interest, and with good cause. Notably, supervised and unsupervised learning both allow for this [27]. DL applications utilize an artificial neural network (ANN) to achieve this.…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

Review on maintenance of photovoltaic systems based on deep learning and internet of things

Hammoudi¹,

Idrissi²,

Boukabous³

et al. 2022

IJEECS

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Many solar plants have been installed globally, and they must be continuously protected and supervised to ensure their safety and reliability. Photovoltaic plants are susceptible to many defects and failures, and fault detection technology is used to protect and isolate them. Despite numerous inter-national standards, invisible photovoltaic defects continue to cause major is-sues. As a result, smart technologies like AI (Artificial Intelligence) and IoT are being developed for remote sensing, problem detection, and diagnosis of photovoltaic systems. Solar plants generate not only green electricity but also a lot of data, such as power output. With AI, a clear picture of electricity yields should be possible. The output of entire solar parks could be monitored and analyzed. The AI could also detect malfunctions within a solar park, according to the research. This would speed up and simplify maintenance work. Deep learning (DL) and IoT applications for photovoltaic plants are discussed. The most advanced techniques, such as DL, are discussed in terms of precision and accuracy. Incorporating DL and IoT approaches for fault detection and diagnosis into simple hardware, such as low-cost chips, maybe cost-effective and technically feasible for photovoltaic facilities located in remote locations.

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Deep convolutional neural networks architecture for an efficient emergency vehicle classification in real-time traffic monitoring

Cited by 33 publications

References 28 publications

Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT

Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT

Forest Fires Detection using Deep Transfer Learning

Review on maintenance of photovoltaic systems based on deep learning and internet of things

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