scholarly journals ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6842 ◽  
Author(s):  
Javier Arellano-Verdejo ◽  
Hugo E. Lazcano-Hernandez ◽  
Nancy Cabanillas-Terán
Keyword(s):  
Neural Network ◽  
Recurrent Neural Networks ◽  
Algal Blooms ◽  
Deep Neural Network ◽  
Quintana Roo ◽  
Negative Consequences ◽  
Learning Techniques ◽  
Mexican Caribbean ◽  
Modis Imagery ◽  
Novel Algorithm

Recently, Caribbean coasts have experienced atypical massive arrivals of pelagic Sargassum with negative consequences both ecologically and economically. Based on deep learning techniques, this study proposes a novel algorithm for floating and accumulated pelagic Sargassum detection along the coastline of Quintana Roo, Mexico. Using convolutional and recurrent neural networks architectures, a deep neural network (named ERISNet) was designed specifically to detect these macroalgae along the coastline through remote sensing support. A new dataset which includes pixel values with and without Sargassum was built to train and test ERISNet. Aqua-MODIS imagery was used to build the dataset. After the learning process, the designed algorithm achieves a 90% of probability in its classification skills. ERISNet provides a novel insight to detect accurately algal blooms arrivals.

scholarly journals ERISNet: Deep learning network for Sargassum detection along the coastline of the Mexican Caribbean

2018 ◽  
Author(s):  
Javier Arellano-Verdejo ◽  
Hugo-Enrique Lazcano-Hernandez ◽  
Nancy Cabanillas-Terán
Keyword(s):  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Algal Blooms ◽  
Quintana Roo ◽  
Negative Consequences ◽  
Learning Network ◽  
Learning Techniques ◽  
Mexican Caribbean ◽  
Modis Imagery ◽  
Deep Learning Network

Recently, Caribbean coasts have experienced atypical massive arrivals of pelagic Sargassum with negative consequences both ecologically and economically. Based on deep learning techniques, this study proposes a novel algorithm for floating and accumulated pelagic Sargassum detection along the coastline of Quintana Roo, Mexico. Using convolutional and recurrent neural networks architectures, a deep learning network (named ERISNet) was designed specifically to detect this macroalgae along the coastline through remote sensing support. A new dataset which includes pixels values with and without Sargassum was built to training and testing ERISNet. Aqua-MODIS imagery was used to build the dataset. After the learning process, the designed algorithm achieves a 90 % of probability in its classification skills. ERISNet provides a baseline for automated systems to accurately and efficiently monitor algal blooms arrivals.

scholarly journals ERISNet: Deep learning network for Sargassum detection along the coastline of the Mexican Caribbean

2018 ◽  
Author(s):  
Javier Arellano-Verdejo ◽  
Hugo-Enrique Lazcano-Hernandez ◽  
Nancy Cabanillas-Terán
Keyword(s):  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Algal Blooms ◽  
Quintana Roo ◽  
Negative Consequences ◽  
Learning Network ◽  
Learning Techniques ◽  
Mexican Caribbean ◽  
Modis Imagery ◽  
Deep Learning Network

Recently, Caribbean coasts have experienced atypical massive arrivals of pelagic Sargassum with negative consequences both ecologically and economically. Based on deep learning techniques, this study proposes a novel algorithm for floating and accumulated pelagic Sargassum detection along the coastline of Quintana Roo, Mexico. Using convolutional and recurrent neural networks architectures, a deep learning network (named ERISNet) was designed specifically to detect this macroalgae along the coastline through remote sensing support. A new dataset which includes pixels values with and without Sargassum was built to training and testing ERISNet. Aqua-MODIS imagery was used to build the dataset. After the learning process, the designed algorithm achieves a 90 % of probability in its classification skills. ERISNet provides a baseline for automated systems to accurately and efficiently monitor algal blooms arrivals.

scholarly journals An Efficient Method for Detection of DDoS Attacks on the Web Using Deep Learning Algorithms

2021 ◽  
Vol 10 (4) ◽  
pp. 2821-2829
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Ddos Attacks ◽  
Problem Statement ◽  
Neural Network Approach ◽  
Learning Techniques ◽  
Attack Data ◽  
Deep Learning Neural Network

Recently, DDoS attacks is the most significant threat in network security. Both industry and academia are currently debating how to detect and protect against DDoS attacks. Many studies are provided to detect these types of attacks. Deep learning techniques are the most suitable and efficient algorithm for categorizing normal and attack data. Hence, a deep neural network approach is proposed in this study to mitigate DDoS attacks effectively. We used a deep learning neural network to identify and classify traffic as benign or one of four different DDoS attacks. We will concentrate on four different DDoS types: Slowloris, Slowhttptest, DDoS Hulk, and GoldenEye. The rest of the paper is organized as follow: Firstly, we introduce the work, Section 2 defines the related works, Section 3 presents the problem statement, Section 4 describes the proposed methodology, Section 5 illustrate the results of the proposed methodology and shows how the proposed methodology outperforms state-of-the-art work and finally Section VI concludes the paper.

scholarly journals Evaluation of Deep Neural Network in Directional Prediction of Forex Market

2020 ◽  
Author(s):  
Hamid Hassanpour
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Software Package ◽  
Deep Neural Network ◽  
Behavior Prediction ◽  
Market Behavior ◽  
Learning Techniques

This is a paper regarding application of deep neural network in prediction of Forex market. It utilized advanced deep learning techniques and software package in order ti evaluate capability of deep neural network in market behavior prediction.

scholarly journals Human Activity Recognition Based on Deep Learning Techniques

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 15
Author(s):  
Manuel Gil-Martín ◽  
Marcos Sánchez-Hernández ◽  
Rubén San-Segundo
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Deep Neural Network ◽  
Human Activity Recognition ◽  
Support Vector ◽  
Daily Life Activities ◽  
Learning Techniques ◽  
The Fourier Transform

Deep learning techniques are being widely applied to Human Activity Recognition (HAR). This paper describes the implementation and evaluation of a HAR system for daily life activities using the accelerometer of an iPhone 6S. This system is based on a deep neural network including convolutional layers for feature extraction from accelerations and fully-connected layers for classification. Different transformations have been applied to the acceleration signals in order to find the appropriate input data to the deep neural network. This study has used acceleration recordings from the MotionSense dataset, where 24 subjects performed 6 activities: walking downstairs, walking upstairs, sitting, standing, walking and jogging. The evaluation has been performed using a subject-wise cross-validation: recordings from the same subject do not appear in training and testing sets at the same time. The proposed system has obtained a 9% improvement in accuracy compared to the baseline system based on Support Vector Machines. The best results have been obtained using raw data as input to a deep neural network composed of two convolutional and two max-pooling layers with decreasing kernel sizes. Results suggest that using the module of the Fourier transform as inputs provides better results when classifying only between dynamic activities.

scholarly journals Comparing traditional and deep-learning techniques of kinematic reconstruction for polarization discrimination in vector boson scattering

2020 ◽  
Vol 80 (12) ◽  
Author(s):  
M. Grossi ◽  
J. Novak ◽  
B. Kerševan ◽  
D. Rebuzzi
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Vector Boson ◽  
Higgs Mechanism ◽  
Beyond The Standard Model ◽  
Decay Channels ◽  
Leptonic Decays ◽  
Learning Techniques ◽  
The Standard Model ◽  
Vector Boson Scattering

AbstractMeasuring longitudinally polarized vector boson scattering in $$\mathrm {WW}$$ WW channel is a promising way to investigate unitarity restoration with the Higgs mechanism and to search for possible physics beyond the Standard Model. In order to perform such a measurement, it is crucial to develop an efficient reconstruction of the full $$\mathrm {W}$$ W boson kinematics in leptonic decays with the focus on polarization measurements. We investigated several approaches, from traditional ones up to advanced deep neural network structures, and we compared their abilities in reconstructing the $$\mathrm {W}$$ W boson reference frame and in consequently measuring the longitudinal fraction $$\mathrm {W}_{\text {L}}$$ W L in both semi-leptonic and fully-leptonic $$\mathrm {WW}$$ WW decay channels.

scholarly journals MajraDoc an Image based Disease Detection App for Agricultural Plants using Deep Learning Techniques

2021 ◽  
Author(s):  
Sara Saleh Alfozan ◽  
Mohamad Mahdi Hassan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Food Safety ◽  
Early Detection ◽  
Deep Neural Network ◽  
High Accuracy ◽  
Vital Role ◽  
Mobile App ◽  
Learning Techniques ◽  
Agricultural Plants

Infection of agricultural plants is a serious threat to food safety. It can severely damage plants' yielding capacity. Farmers are the primary victims of this threat. Due to the advancement of AI, image-based intelligent apps can play a vital role in mitigating this threat by quick and early detection of plants infections. In this paper, we present a mobile app in this regard. We have developed MajraDoc to detect some common diseases in local agricultural plants. We have created a dataset of 10886 images for ten classes of plants diseases to train the deep neural network. The VGG-19 network model was modified and trained using transfer learning techniques. The model achieved high accuracy, and the application performed well in predicting all ten classes of infections.

scholarly journals Weather Nowcasting Using Deep Learning Techniques

2021 ◽  
Author(s):  
Makhamisa Senekane ◽  
Mhlambululi Mafu ◽  
Molibeli Benedict Taele
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Weather Forecasting ◽  
Network Models ◽  
Short Term ◽  
Neural Network Models ◽  
Precipitation Forecasting ◽  
Learning Techniques

Weather variations play a significant role in peoples’ short-term, medium-term or long-term planning. Therefore, understanding of weather patterns has become very important in decision making. Short-term weather forecasting (nowcasting) involves the prediction of weather over a short period of time; typically few hours. Different techniques have been proposed for short-term weather forecasting. Traditional techniques used for nowcasting are highly parametric, and hence complex. Recently, there has been a shift towards the use of artificial intelligence techniques for weather nowcasting. These include the use of machine learning techniques such as artificial neural networks. In this chapter, we report the use of deep learning techniques for weather nowcasting. Deep learning techniques were tested on meteorological data. Three deep learning techniques, namely multilayer perceptron, Elman recurrent neural networks and Jordan recurrent neural networks, were used in this work. Multilayer perceptron models achieved 91 and 75% accuracies for sunshine forecasting and precipitation forecasting respectively, Elman recurrent neural network models achieved accuracies of 96 and 97% for sunshine and precipitation forecasting respectively, while Jordan recurrent neural network models achieved accuracies of 97 and 97% for sunshine and precipitation nowcasting respectively. The results obtained underline the utility of using deep learning for weather nowcasting.

HUMAN ROBOT INTERACTIVE INTENTION PREDICTION USING DEEP LEARNING TECHNIQUES

2021 ◽  
pp. 1-12
Author(s):  
Thang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
The Neural Network ◽  
Learning Techniques ◽  
Long Short Term Memory ◽  
Deep Learning Neural Network

In this research, we propose a method of human robot interactive intention prediction. The proposed algorithm makes use of a OpenPose library and a Long-short term memory deep learning neural network. The neural network observes the human posture in a time series, then predicts the human interactive intention. We train the deep neural network using dataset generated by us. The experimental results show that, our proposed method is able to predict the human robot interactive intention, providing 92% the accuracy on the testing set.

Sign in / Sign up

Export Citation Format

Share Document