LDA-Based Topic Modeling Sentiment Analysis Using Topic/Document/Sentence (TDS) Model

Islam

Nasimov

et al. 2023

Self Cite

With an increase in both global warming and the human population, forest fires have become a major global concern. This can lead to climatic shifts and the greenhouse effect, among other adverse outcomes. Surprisingly, human activities have caused a disproportionate number of forest fires. Fast detection with high accuracy is the key to controlling this unexpected event. To address this, we proposed an improved forest fire detection method to classify fires based on a new version of the Detectron2 platform (a ground-up rewrite of the Detectron library) using deep learning approaches. Furthermore, a custom dataset was created and labeled for the training model, and it achieved higher precision than the other models. This robust result was achieved by improving the Detectron2 model in various experimental scenarios with a custom dataset and 5200 images. The proposed model can detect small fires over long distances during the day and night. The advantage of using the Detectron2 algorithm is its long-distance detection of the object of interest. The experimental results proved that the proposed forest fire detection method successfully detected fires with an improved precision of 99.3%.

Section: Resultsmentioning

confidence: 99%

An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach

Islam

Nasimov

et al. 2023

Self Cite

“…In our previous studies [ 52 , 53 , 54 , 55 , 56 , 57 , 58 ], we computed metrics such as the F-measure (FM), precision, and recall. The FM is the weighted average that balances the measurements between the precision and recall rates.…”

Section: Implementation and Resultsmentioning

confidence: 99%

Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach

Dilnoza

Mukhiddinov

et al. 2023

Self Cite

Most facial recognition and face analysis systems start with facial detection. Early techniques, such as Haar cascades and histograms of directed gradients, mainly rely on features that had been manually developed from particular images. However, these techniques are unable to correctly synthesize images taken in untamed situations. However, deep learning’s quick development in computer vision has also sped up the development of a number of deep learning-based face detection frameworks, many of which have significantly improved accuracy in recent years. When detecting faces in face detection software, the difficulty of detecting small, scale, position, occlusion, blurring, and partially occluded faces in uncontrolled conditions is one of the problems of face identification that has been explored for many years but has not yet been entirely resolved. In this paper, we propose Retina net baseline, a single-stage face detector, to handle the challenging face detection problem. We made network improvements that boosted detection speed and accuracy. In Experiments, we used two popular datasets, such as WIDER FACE and FDDB. Specifically, on the WIDER FACE benchmark, our proposed method achieves AP of 41.0 at speed of 11.8 FPS with a single-scale inference strategy and AP of 44.2 with multi-scale inference strategy, which are results among one-stage detectors. Then, we trained our model during the implementation using the PyTorch framework, which provided an accuracy of 95.6% for the faces, which are successfully detected. Visible experimental results show that our proposed model outperforms seamless detection and recognition results achieved using performance evaluation matrices.

“…Because the source codes and datasets for these approaches are not publicly available, we used the results in their publications for comparison; however, we are unsure of their veracity. As in our previous studies [ 31 , 32 , 33 , 34 , 35 ], we calculated the F-measure (FM), precision, and recall. The FM score is a weighted average that equalizes the measurements of the recall rates and precision.…”

Section: Resultsmentioning

confidence: 99%

Improved Real-Time Fire Warning System Based on Advanced Technologies for Visually Impaired People

Mukhiddinov

Kutlimuratov

et al. 2022

Early fire detection and notification techniques provide fire prevention and safety information to blind and visually impaired (BVI) people within a short period of time in emergency situations when fires occur in indoor environments. Given its direct impact on human safety and the environment, fire detection is a difficult but crucial problem. To prevent injuries and property damage, advanced technology requires appropriate methods for detecting fires as quickly as possible. In this study, to reduce the loss of human lives and property damage, we introduce the development of the vision-based early flame recognition and notification approach using artificial intelligence for assisting BVI people. The proposed fire alarm control system for indoor buildings can provide accurate information on fire scenes. In our proposed method, all the processes performed manually were automated, and the performance efficiency and quality of fire classification were improved. To perform real-time monitoring and enhance the detection accuracy of indoor fire disasters, the proposed system uses the YOLOv5m model, which is an updated version of the traditional YOLOv5. The experimental results show that the proposed system successfully detected and notified the occurrence of catastrophic fires with high speed and accuracy at any time of day or night, regardless of the shape or size of the fire. Finally, we compared the competitiveness level of our method with that of other conventional fire-detection methods to confirm the seamless classification results achieved using performance evaluation matrices.