A Social Bots Detection Model Based on Deep Learning Algorithm

2018 ◽  
Author(s):  
Heng Ping ◽  
Sujuan Qin
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Detection Model ◽  
Model Based ◽  
Deep Learning Algorithm

scholarly journals Global Cyclone and Anticyclone Detection Model Based on Remotely Sensed Wind Field and Deep Learning

2020 ◽  
Vol 12 (19) ◽  
pp. 3111
Author(s):  
Ming Xie ◽  
Ying Li ◽  
Kai Cao
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Wind Field ◽  
Learning Algorithm ◽  
Global Scale ◽  
False Alarms ◽  
Detection Model ◽  
Model Based ◽  
Number Of False Alarms ◽  
Deep Learning Algorithm

Cyclone detection is a classical topic and researchers have developed various methods of cyclone detection based on sea-level pressure, cloud image, wind field, etc. In this article, a deep-learning algorithm is incorporated with modern remote-sensing technology and forms a global-scale cyclone/anticyclone detection model. Instead of using optical images, wind field data obtained from Mean Wind Field-Advanced Scatterometer (MWF-ASCAT) is utilized as the dataset for model training and testing. The wind field vectors are reconstructed and fed to the deep-learning model, which is built based on a faster-region with convolutional neural network (faster-RCNN). The model consists of three modules: a series of convolutional and pooling layers as the feature extractor, a region proposal network that searches for the potential areas of cyclone/anticyclone within the dataset, and the classifier that classifies the proposed region as cyclone or anticyclone through a fully-connected neural network. Compared with existing methods of cyclone detection, the test results indicate that this model based on deep learning is able to reduce the number of false alarms, and at the same time, maintain high accuracy in cyclone detection. An application of this method is presented in the article. By processing temporally continuous data of wind field, the model is able to track the path of Hurricane Irma in September, 2017. The advantages and limitations of the model are also discussed in the article.

scholarly journals Application of Model-Based Deep Learning Algorithm in Fault Diagnosis of Coal Mills

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yifan Jian ◽  
Xianguo Qing ◽  
Yang Zhao ◽  
Liang He ◽  
Xiao Qi
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Mechanism Model ◽  
Model Based ◽  
Deep Learning Algorithm ◽  
Stacked Autoencoders ◽  
Different Types ◽  
Operation Status ◽  
Types Of Faults

The coal mill is one of the important auxiliary engines in the coal-fired power station. Its operation status is directly related to the safe and steady operation of the units. In this paper, a model-based deep learning algorithm for fault diagnosis is proposed to effectively detect the operation state of coal mills. Based on the system mechanism model of coal mills, massive fault data are obtained by analyzing and simulating the different types of faults. Then, stacked autoencoders (SAEs) are established by combining the said data with the deep learning algorithm. The SAE model is trained by the fault data, which provide it with the learning and identification capability of the characteristics of faults. According to the simulation results, the accuracy of fault diagnosis of coal mills based on SAE is high at 98.97%. Finally, the proposed SAEs can well detect the fault in coal mills and generate the warnings in advance.

scholarly journals Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yiran Feng ◽  
Xueheng Tao ◽  
Eung-Joo Lee
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Experimental Tests ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Multiple Objects ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Quality Sorting

In view of the current absence of any deep learning algorithm for shellfish identification in real contexts, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multiobject recognition and localization through a second-order detection network and replaces the original feature extraction module with DenseNet, which can fuse multilevel feature information, increase network depth, and avoid the disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects and enhancing the network detection accuracy under multiple objects. By constructing a real contexts shellfish dataset and conducting experimental tests on a vision recognition seafood sorting robot production line, we were able to detect the features of shellfish in different scenarios, and the detection accuracy was improved by nearly 4% compared to the original detection model, achieving a better detection accuracy. This provides favorable technical support for future quality sorting of seafood using the improved Faster R-CNN-based approach.

A deep learning based dynamic COD prediction model for urban sewage

2019 ◽  
Vol 5 (12) ◽  
pp. 2210-2218
Author(s):  
Zifei Wang ◽  
Yi Man ◽  
Yusha Hu ◽  
Jigeng Li ◽  
Mengna Hong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Urban Sewage ◽  
Model Based ◽  
Deep Learning Algorithm ◽  
Aeration Control

An influent COD prediction model based on the CNN-LSTM deep learning algorithm is proposed as the basis of aeration control in WWTPs.

scholarly journals Pattern Detection Model Using a Deep Learning Algorithm for Power Data Analysis in Abnormal Conditions

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1140
Author(s):  
Jeong-Hee Lee ◽  
Jongseok Kang ◽  
We Shim ◽  
Hyun-Sang Chung ◽  
Tae-Eung Sung
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Pattern Detection ◽  
Prediction Algorithm ◽  
Data Set ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Abnormal Pattern

Building a pattern detection model using a deep learning algorithm for data collected from manufacturing sites is an effective way for to perform decision-making and assess business feasibility for enterprises, by providing the results and implications of the patterns analysis of big data occurring at manufacturing sites. To identify the threshold of the abnormal pattern requires collaboration between data analysts and manufacturing process experts, but it is practically difficult and time-consuming. This paper suggests how to derive the threshold setting of the abnormal pattern without manual labelling by process experts, and offers a prediction algorithm to predict the potentials of future failures in advance by using the hybrid Convolutional Neural Networks (CNN)–Long Short-Term Memory (LSTM) algorithm, and the Fast Fourier Transform (FFT) technique. We found that it is easier to detect abnormal patterns that cannot be found in the existing time domain after preprocessing the data set through FFT. Our study shows that both train loss and test loss were well developed, with near zero convergence with the lowest loss rate compared to existing models such as LSTM. Our proposition for the model and our method of preprocessing the data greatly helps in understanding the abnormal pattern of unlabeled big data produced at the manufacturing site, and can be a strong foundation for detecting the threshold of the abnormal pattern of big data occurring at manufacturing sites.

scholarly journals Optimizing the Experimental Method for Stomata-Profiling Automation of Soybean Leaves Based on Deep Learning

Plants ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 2714
Author(s):  
Syada Nizer Sultana ◽  
Halim Park ◽  
Sung Hoon Choi ◽  
Hyun Jo ◽  
Jong Tae Song ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Imaging Data ◽  
Web Based ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Soybean Leaves ◽  
Deep Learning Model ◽  
Clearing Method ◽  
Good Substitute

Stomatal observation and automatic stomatal detection are useful analyses of stomata for taxonomic, biological, physiological, and eco-physiological studies. We present a new clearing method for improved microscopic imaging of stomata in soybean followed by automated stomatal detection by deep learning. We tested eight clearing agent formulations based upon different ethanol and sodium hypochlorite (NaOCl) concentrations in order to improve the transparency in leaves. An optimal formulation—a 1:1 (v/v) mixture of 95% ethanol and NaOCl (6–14%)—produced better quality images of soybean stomata. Additionally, we evaluated fixatives and dehydrating agents and selected absolute ethanol for both fixation and dehydration. This is a good substitute for formaldehyde, which is more toxic to handle. Using imaging data from this clearing method, we developed an automatic stomatal detector using deep learning and improved a deep-learning algorithm that automatically analyzes stomata through an object detection model using YOLO. The YOLO deep-learning model successfully recognized stomata with high mAP (~0.99). A web-based interface is provided to apply the model of stomatal detection for any soybean data that makes use of the new clearing protocol.

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