Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety

Safarov, Furkat; Akhmedov, Farkhod; Abdusalomov, Akmalbek Bobomirzaevich; Nasimov, Rashid; Cho, Young Im

doi:10.3390/s23146459

Cited by 37 publications

(8 citation statements)

References 34 publications

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“…To evaluate and analyze the effectiveness of attack detection cases, we compared the proposed approach with recently published attack detection methods. To perform this task, we employed widely used estimation metrics (precision, recall, and F1), as detailed in these publications [56][57][58][59][60]. To classify the results obtained, the following cases were distinguished:…”

Section: Discussionmentioning

confidence: 99%

Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection

Safarov,

Basak,

Nasimov

et al. 2023

Future Internet

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In the rapidly evolving landscape of internet usage, ensuring robust cybersecurity measures has become a paramount concern across diverse fields. Among the numerous cyber threats, denial of service (DoS) and distributed denial of service (DDoS) attacks pose significant risks, as they can render websites and servers inaccessible to their intended users. Conventional intrusion detection methods encounter substantial challenges in effectively identifying and mitigating these attacks due to their widespread nature, intricate patterns, and computational complexities. However, by harnessing the power of deep learning-based techniques, our proposed dense channel-spatial attention model exhibits exceptional accuracy in detecting and classifying DoS and DDoS attacks. The successful implementation of our proposed framework addresses the challenges posed by imbalanced data and exhibits its potential for real-world applications. By leveraging the dense channel-spatial attention mechanism, our model can precisely identify and classify DoS and DDoS attacks, bolstering the cybersecurity defenses of websites and servers. The high accuracy rates achieved across different datasets reinforce the robustness of our approach, underscoring its efficacy in enhancing intrusion detection capabilities. As a result, our framework holds promise in bolstering cybersecurity measures in real-world scenarios, contributing to the ongoing efforts to safeguard against cyber threats in an increasingly interconnected digital landscape. Comparative analysis with current intrusion detection methods reveals the superior performance of our model. We achieved accuracy rates of 99.38%, 99.26%, and 99.43% for Bot-IoT, CICIDS2017, and UNSW_NB15 datasets, respectively. These remarkable results demonstrate the capability of our approach to accurately detect and classify various types of DoS and DDoS assaults. By leveraging the inherent strengths of deep learning, such as pattern recognition and feature extraction, our model effectively overcomes the limitations of traditional methods, enhancing the accuracy and efficiency of intrusion detection systems.

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Section: Discussionmentioning

confidence: 99%

Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection

Safarov,

Basak,

Nasimov

et al. 2023

Future Internet

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“…These issues could be resolved by expanding the size of the dataset. We aim to retrain our model with a more diverse dataset to overcome these issues [ 45 , 46 , 47 ].…”

Section: Limitationsmentioning

confidence: 99%

Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches

Avazov

Jamil

Muminov

et al. 2023

Sensors

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Fire incidents occurring onboard ships cause significant consequences that result in substantial effects. Fires on ships can have extensive and severe wide-ranging impacts on matters such as the safety of the crew, cargo, the environment, finances, reputation, etc. Therefore, timely detection of fires is essential for quick responses and powerful mitigation. The study in this research paper presents a fire detection technique based on YOLOv7 (You Only Look Once version 7), incorporating improved deep learning algorithms. The YOLOv7 architecture, with an improved E-ELAN (extended efficient layer aggregation network) as its backbone, serves as the basis of our fire detection system. Its enhanced feature fusion technique makes it superior to all its predecessors. To train the model, we collected 4622 images of various ship scenarios and performed data augmentation techniques such as rotation, horizontal and vertical flips, and scaling. Our model, through rigorous evaluation, showcases enhanced capabilities of fire recognition to improve maritime safety. The proposed strategy successfully achieves an accuracy of 93% in detecting fires to minimize catastrophic incidents. Objects having visual similarities to fire may lead to false prediction and detection by the model, but this can be controlled by expanding the dataset. However, our model can be utilized as a real-time fire detector in challenging environments and for small-object detection. Advancements in deep learning models hold the potential to enhance safety measures, and our proposed model in this paper exhibits this potential. Experimental results proved that the proposed method can be used successfully for the protection of ships and in monitoring fires in ship port areas. Finally, we compared the performance of our method with those of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.

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“…By contrast, our proposed method aims to generate a dataset based on given risk factors. In the future, by analyzing the dependence of risk factors and information in the dataset using these two methods, it will be possible to develop an algorithm that determines the relationship between them, which will be an important tool for diagnosis [ 37 , 38 , 39 , 40 , 41 , 42 , 43 ].…”

Section: Future Workmentioning

confidence: 99%

New Approach for Generating Synthetic Medical Data to Predict Type 2 Diabetes

Tagmatova,

Abdusalomov,

Nasimov

et al. 2023

Bioengineering

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The lack of medical databases is currently the main barrier to the development of artificial intelligence-based algorithms in medicine. This issue can be partially resolved by developing a reliable high-quality synthetic database. In this study, an easy and reliable method for developing a synthetic medical database based only on statistical data is proposed. This method changes the primary database developed based on statistical data using a special shuffle algorithm to achieve a satisfactory result and evaluates the resulting dataset using a neural network. Using the proposed method, a database was developed to predict the risk of developing type 2 diabetes 5 years in advance. This dataset consisted of data from 172,290 patients. The prediction accuracy reached 94.45% during neural network training of the dataset.

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Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety

Cited by 37 publications

References 34 publications

Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection

Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection

Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches

New Approach for Generating Synthetic Medical Data to Predict Type 2 Diabetes

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