scholarly journals Center Pivot Irrigation Systems and Where to Find Them: A Deep Learning Approach to Provide Inputs to Hydrologic and Economic Models

2021 ◽  
Vol 3 ◽  
Author(s):  
Daniel Cooley ◽  
Reed M. Maxwell ◽  
Steven M. Smith
Keyword(s):  
Deep Learning ◽  
Coupled System ◽  
Learning Approach ◽  
Technology Choice ◽  
Hydrological Models ◽  
Irrigation Systems ◽  
Technology Center ◽  
Irrigation Technology ◽  
Center Pivot

Availability and quality of administrative data on irrigation technology varies greatly across jurisdictions. Technology choice, however, will influence the parameters of coupled human-hydrological systems. Equally, changing parameters in the coupled system may drive technology adoption. Here we develop and demonstrate a deep learning approach to locate a particularly important irrigation technology—center pivot irrigation systems—throughout the Ogallala Aquifer. The model does not rely on super computers and thus provides a model for an accessible baseline to train and deploy on other geographies. We further demonstrate that accounting for the technology can improve the insights in both economic and hydrological models.

scholarly journals Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization

2021 ◽  
Vol 3 (3) ◽  
pp. 234-248
Author(s):  
N. Bhalaji
Keyword(s):  
Time Series ◽  
Cloud Computing ◽  
Deep Learning ◽  
Extreme Points ◽  
Learning Approach ◽  
Load Estimation ◽  
Learning Method ◽  
Network Computing ◽  
Noise Interference

In recent days, we face workload and time series issue in cloud computing. This leads to wastage of network, computing and resources. To overcome this issue we have used integrated deep learning approach in our proposed work. Accurate prediction of workload and resource allocation with time series enhances the performance of the network. Initially the standard deviation is reduced by applying logarithmic operation and then powerful filters are adopted to remove the extreme points and noise interference. Further the time series is predicted by integrated deep learning method. This method accurately predicts the workload and sequence of resource along with time series. Then the obtained data is standardized by a Min-Max scalar and the quality of the network is preserved by incorporating network model. Finally our proposed method is compared with other currently used methods and the results are obtained.

scholarly journals Deep Learning Approach for COVID-19 Diagnosis Using X-Ray Images

2020 ◽  
Author(s):  
Muntasir Al-Asfoor
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Normal Subjects ◽  
Learning Approach ◽  
Computer Aided Detection ◽  
X Ray ◽  
Deep Learning Algorithm ◽  
Chest X Ray ◽  
The Times

Abstract During the times of pandemics, faster diagnosis plays a key role in the response efforts to contain the disease as well as reducing its spread. Computer-aided detection would save time and increase the quality of diagnosis in comparison with manual human diagnosis. Artificial Intelligence (AI) through deep learning is considered as a reliable method to design such systems. In this research paper, an AI based diagnosis approach has been suggested to tackle the COVID-19 pandemic. The proposed system employs a deep learning algorithm on chest x-ray images to detect the infected subjects. An enhanced Convolutional Neural Network (CNN) architecture has been designed with 22 layers which is then trained over a chest x-ray dataset. More after, a classification component has been introduced to classify the x-ray images into two categories (Covid-19 and not Covid-19) of infection. The system has been evaluated through a series of observations and experimentation. The experimental results have shown a promising performance in terms of accuracy. The system has diagnosed Covid-19 with accuracy of 95.7% and normal subjects with accuracy of 93.1 while it showed 96.7 accuracy on Pneumonia.

scholarly journals Mapping Center Pivot Irrigation Systems in the Southern Amazon from Sentinel-2 Images

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 298
Author(s):  
Jiwen Tang ◽  
Damien Arvor ◽  
Thomas Corpetti ◽  
Ping Tang
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Image Classification ◽  
Water Consumption ◽  
Hough Transform ◽  
Classification Model ◽  
Irrigation Systems ◽  
Mato Grosso ◽  
Center Pivot ◽  
Sentinel 2

Irrigation systems play an important role in agriculture. Center pivot irrigation systems are popular in many countries as they are labor-saving and water consumption efficient. Monitoring the distribution of center pivot irrigation systems can provide important information for agricultural production, water consumption and land use. Deep learning has become an effective method for image classification and object detection. In this paper, a new method to detect the precise shape of center pivot irrigation systems is proposed. The proposed method combines a lightweight real-time object detection network (PVANET) based on deep learning, an image classification model (GoogLeNet) and accurate shape detection (Hough transform) to detect and accurately delineate center pivot irrigation systems and their associated circular shape. PVANET is lightweight and fast and GoogLeNet can reduce the false detections associated with PVANET, while Hough transform can accurately detect the shape of center pivot irrigation systems. Experiments with Sentinel-2 images in Mato Grosso achieved a precision of 95% and a recall of 95.5%, which demonstrated the effectiveness of the proposed method. Finally, with the accurate shape of center pivot irrigation systems detected, the area of irrigation in the region was estimated.

scholarly journals A Deep Learning Approach to Mobile Camera Image Signal Processing

2020 ◽  
Author(s):  
Jose Ivson S. Silva ◽  
Gabriel G. Carvalho ◽  
Marcel Santana Santos ◽  
Diego J. C. Santiago ◽  
Lucas Pontes De Albuquerque ◽  
...  
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Learning Approach ◽  
Image Capture ◽  
Camera Image ◽  
Mobile Cameras ◽  
Physical Resources ◽  
Deep Learning Model

The quality of the images obtained from mobile cameras has been an important feature for modern smartphones. The camera Image Signal Processing (ISP) is a significant procedure when generating high-quality images. However, the existing algorithms in the ISP pipeline need to be tuned according to the physical resources of the image capture, limiting the final image quality. This work aims at replacing the camera ISP pipeline with a deep learning model that can better generalize the procedure. A Deep Neural Network based on the UNet architecture was employed to process RAW images into RGB. Pre-processing stages were applied, and some resources for training were added incrementally. The results demonstrated that the test images were obtained efficiently, indicating that the replacement of traditional algorithms by deep models is indeed a promising path.

scholarly journals Deep learning approach to obtain collaborative filtering neighborhoods

2021 ◽  
Author(s):  
Jesús Bobadilla ◽  
Ángel González-Prieto ◽  
Fernando Ortega ◽  
Raúl Lara-Cabrera
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Nearest Neighbors ◽  
Learning Approach ◽  
Localization Algorithm ◽  
Generative Process ◽  
Open Datasets

AbstractIn the context of recommender systems based on collaborative filtering (CF), obtaining accurate neighborhoods of the items of the datasets is relevant. Beyond particular individual recommendations, knowing these neighbors is fundamental for adding differentiating factors to recommendations, such as explainability, detecting shilling attacks, visualizing item relations, clustering, and providing reliabilities. This paper proposes a deep learning architecture to efficiently and accurately obtain CF neighborhoods. The proposed design makes use of a classification neural network to encode the dataset patterns of the items, followed by a generative process that obtains the neighborhood of each item by means of an iterative gradient localization algorithm. Experiments have been conducted using five popular open datasets and five representative baselines. The results show that the proposed method improves the quality of the neighborhoods compared to the K-Nearest Neighbors (KNN) algorithm for the five selected similarity measure baselines. The efficiency of the proposed method is also shown by comparing its computational requirements with that of KNN.

scholarly journals A deep learning approach for predicting the quality of online health expert question-answering services

2017 ◽  
Vol 71 ◽  
pp. 241-253 ◽  
Author(s):  
Ze Hu ◽  
Zhan Zhang ◽  
Haiqin Yang ◽  
Qing Chen ◽  
Decheng Zuo
Keyword(s):  
Deep Learning ◽  
Question Answering ◽  
Learning Approach ◽  
Health Expert

scholarly journals A Novel Machine Learning Approach for Sentiment Analysis on Twitter Incorporating the Universal Language Model Fine-Tuning and SVM

2022 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Barakat AlBadani ◽  
Ronghua Shi ◽  
Jian Dong
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Detection Efficiency ◽  
Language Model ◽  
Fine Tuning ◽  
Support Vector ◽  
Learning Approach ◽  
Universal Language

Twitter sentiment detectors (TSDs) provide a better solution to evaluate the quality of service and product than other traditional technologies. The classification accuracy and detection performance of TSDs, which are extremely reliant on the performance of the classification techniques, are used, and the quality of input features is provided. However, the time required is a big problem for the existing machine learning methods, which leads to a challenge for all enterprises that aim to transform their businesses to be processed by automated workflows. Deep learning techniques have been utilized in several real-world applications in different fields such as sentiment analysis. Deep learning approaches use different algorithms to obtain information from raw data such as texts or tweets and represent them in certain types of models. These models are used to infer information about new datasets that have not been modeled yet. We present a new effective method of sentiment analysis using deep learning architectures by combining the “universal language model fine-tuning” (ULMFiT) with support vector machine (SVM) to increase the detection efficiency and accuracy. The method introduces a new deep learning approach for Twitter sentiment analysis to detect the attitudes of people toward certain products based on their comments. The extensive results on three datasets illustrate that our model achieves the state-of-the-art results over all datasets. For example, the accuracy performance is 99.78% when it is applied on the Twitter US Airlines dataset.

scholarly journals Sentiment Analysis in Spanish for Improvement of Products and Services: A Deep Learning Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Mario Andrés Paredes-Valverde ◽  
Ricardo Colomo-Palacios ◽  
María del Pilar Salas-Zárate ◽  
Rafael Valencia-García
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Deep Learning ◽  
Public Opinion ◽  
Customer Satisfaction ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Learning Approach ◽  
Complete Dataset

Sentiment analysis is an important area that allows knowing public opinion of the users about several aspects. This information helps organizations to know customer satisfaction. Social networks such as Twitter are important information channels because information in real time can be obtained and processed from them. In this sense, we propose a deep-learning-based approach that allows companies and organizations to detect opportunities for improving the quality of their products or services through sentiment analysis. This approach is based on convolutional neural network (CNN) and word2vec. To determine the effectiveness of this approach for classifying tweets, we conducted experiments with different sizes of a Twitter corpus composed of 100000 tweets. We obtained encouraging results with a precision of 88.7%, a recall of 88.7%, and an F-measure of 88.7% considering the complete dataset.

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