A New Reliable System For Managing Virtual Cloud Network

Alshathri, Samah; Talaat, Fatma M.; Nasr, Aida A.

doi:10.32604/cmc.2022.026547

Cited by 15 publications

(3 citation statements)

References 42 publications

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“…In the future work, we can use OCNN [37] to achieve better results as it achieved a good performance in [38][39][40][41][42][43][44]. We can also use correlation methods like [45].…”

Section: Discussionmentioning

confidence: 99%

An improved fire detection approach based on YOLO-v8 for smart cities

Talaat

ZainEldin

2023

Neural Comput & Applic

240

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Fires in smart cities can have devastating consequences, causing damage to property, and endangering the lives of citizens. Traditional fire detection methods have limitations in terms of accuracy and speed, making it challenging to detect fires in real time. This paper proposes an improved fire detection approach for smart cities based on the YOLOv8 algorithm, called the smart fire detection system (SFDS), which leverages the strengths of deep learning to detect fire-specific features in real time. The SFDS approach has the potential to improve the accuracy of fire detection, reduce false alarms, and be cost-effective compared to traditional fire detection methods. It can also be extended to detect other objects of interest in smart cities, such as gas leaks or flooding. The proposed framework for a smart city consists of four primary layers: (i) Application layer, (ii) Fog layer, (iii) Cloud layer, and (iv) IoT layer. The proposed algorithm utilizes Fog and Cloud computing, along with the IoT layer, to collect and process data in real time, enabling faster response times and reducing the risk of damage to property and human life. The SFDS achieved state-of-the-art performance in terms of both precision and recall, with a high precision rate of 97.1% for all classes. The proposed approach has several potential applications, including fire safety management in public areas, forest fire monitoring, and intelligent security systems.

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“…In the future work, we can use OCNN [37] to achieve better results as it achieved a good performance in [38][39][40][41][42][43][44]. We can also use correlation methods like [45].…”

Section: Discussionmentioning

confidence: 99%

An improved fire detection approach based on YOLO-v8 for smart cities

Talaat

ZainEldin

2023

Neural Comput & Applic

240

View full text Add to dashboard Cite

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“…The potential of optimizing the CNN model for traffic prediction tasks is also worth exploring, as it could further improve the accuracy and efficiency of the proposed TPT. In the future, the proposed algorithm can be used with OCNN [ 27 , 29 – 32 , 34 ] and make use of Resnet [ 13 ].…”

Section: Discussionmentioning

confidence: 99%

Exploring the effects of pandemics on transportation through correlations and deep learning techniques

Gamel

Hassan

El-Rashidy

et al. 2023

Multimed Tools Appl

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The COVID-19 pandemic has had a significant impact on human migration worldwide, affecting transportation patterns in cities. Many cities have issued "stay-at-home" orders during the outbreak, causing commuters to change their usual modes of transportation. For example, some transit/bus passengers have switched to driving or car-sharing. As a result, urban traffic congestion patterns have changed dramatically, and understanding these changes is crucial for effective emergency traffic management and control efforts. While previous studies have focused on natural disasters or major accidents, only a few have examined pandemic-related traffic congestion patterns. This paper uses correlations and machine learning techniques to analyze the relationship between COVID-19 and transportation. The authors simulated traffic models for five different networks and proposed a Traffic Prediction Technique (TPT), which includes an Impact Calculation Methodology that uses Pearson's Correlation Coefficient and Linear Regression, as well as a Traffic Prediction Module (TPM). The paper's main contribution is the introduction of the TPM, which uses Convolutional Neural Network to predict the impact of COVID-19 on transportation. The results indicate a strong correlation between the spread of COVID-19 and transportation patterns, and the CNN has a high accuracy rate in predicting these impacts.

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“…Future research may involve testing the model's effectiveness in real-world scenarios and exploring its scalability to accommodate larger datasets. In the future, the proposed algorithm can be used with OCNN [36][37][38][39][40][41][42][43] and make use of Resnet [44]. ERNN can be used for stress detection as in [45].…”

Section: Discussionmentioning

confidence: 99%

Explainable Enhanced Recurrent Neural Network for lie detection using voice stress analysis

Talaat

2023

Multimed Tools Appl

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Lie detection is a crucial aspect of human interactions that affects everyone in their daily lives. Individuals often rely on various cues, such as verbal and nonverbal communication, particularly facial expressions, to determine if someone is truthful. While automated lie detection systems can assist in identifying these cues, current approaches are limited due to a lack of suitable datasets for testing their performance in real-world scenarios. Despite ongoing research efforts to develop effective and reliable lie detection methods, this remains a work in progress. The polygraph, voice stress analysis, and pupil dilation analysis are some of the methods currently used for this task. In this study, we propose a new detection algorithm based on an Enhanced Recurrent Neural Network (ERNN) with Explainable AI capabilities. The ERNN, based on long short-term memory (LSTM) architecture, was optimized using fuzzy logic to determine the hyperparameters. The LSTM model was then created and trained using a dataset of audio recordings from interviews with a randomly selected group. The proposed ERNN achieved an accuracy of 97.3%, which is statistically significant for the problem of voice stress analysis. These results suggest that it is possible to detect patterns in the voices of individuals experiencing stress in an explainable manner.

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A New Reliable System For Managing Virtual Cloud Network

Cited by 15 publications

References 42 publications

An improved fire detection approach based on YOLO-v8 for smart cities

An improved fire detection approach based on YOLO-v8 for smart cities

Exploring the effects of pandemics on transportation through correlations and deep learning techniques

Explainable Enhanced Recurrent Neural Network for lie detection using voice stress analysis

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