Deep Learning Model for Vehicle Lane-Change Detection in Phantom Traffic Jam Using CCTV Images

2021 ◽  
Vol 8 (4) ◽  
pp. 9-13
Author(s):  
Kyuseok Kim
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Learning Model ◽  
Lane Change ◽  
Traffic Jam ◽  
Deep Learning Model

scholarly journals Change Detection in Unmanned Aerial Vehicle Images for Progress Monitoring of Road Construction

Buildings ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 150
Author(s):  
Dongyeob Han ◽  
Suk Bae Lee ◽  
Mihwa Song ◽  
Jun Sang Cho
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Construction Projects ◽  
Progress Monitoring ◽  
Road Construction ◽  
Learning Model ◽  
Construction Technology ◽  
Construction Site ◽  
Time Series Images ◽  
Deep Learning Model

Currently, unmanned aerial vehicles are increasingly being used in various construction projects such as housing developments, road construction, and bridge maintenance. If a drone is used at a road construction site, elevation information and orthoimages can be generated to acquire the construction status quantitatively. However, the detection of detailed changes in the site owing to construction depends on visual video interpretation. This study develops a method for automatic detection of the construction area using multitemporal images and a deep learning method. First, a deep learning model was trained using images of the changing area as reference. Second, we obtained an effective application method by applying various parameters to the deep learning process. The application of the time-series images of a construction site to the selected deep learning model enabled more effective identification of the changed areas than the existing pixel-based change detection. The proposed method is expected to be very helpful in construction management by aiding in the development of smart construction technology.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

Detection of peripherally inserted central catheter (PICC) in chest X-ray images: A multi-task deep learning model

2020 ◽  
Vol 197 ◽  
pp. 105674
Author(s):  
Dingding Yu ◽  
Kaijie Zhang ◽  
Lingyan Huang ◽  
Bonan Zhao ◽  
Xiaoshan Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Peripherally Inserted Central Catheter ◽  
Central Catheter ◽  
X Ray ◽  
Chest X Ray ◽  
Deep Learning Model

Very short-term forecasting of wind power generation using hybrid deep learning model

2021 ◽  
Vol 296 ◽  
pp. 126564
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Deep Learning Model
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