scholarly journals Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks

2021 ◽  
Vol 13 (9) ◽  
pp. 5323
Author(s):  
Konstantinos Ioannou ◽  
Dimitrios Myronidis
Keyword(s):  
Satellite Imagery ◽  
Learning Algorithm ◽  
Automatic Detection ◽  
Free Access ◽  
Solar Photovoltaic ◽  
Deep Learning Algorithm ◽  
Novel Approach ◽  
The Status ◽  
High Resolution Satellite Imagery ◽  
First Time

The number of solar photovoltaic (PV) arrays in Greece has increased rapidly during the recent years. As a result, there is an increasing need for high quality updated information regarding the status of PV farms. This information includes the number of PV farms, power capacity and the energy generated. However, access to this data is obsolete, mainly due to the fact that there is a difficulty tracking PV investment status (from licensing to investment completion and energy production). This article presents a novel approach, which uses free access high resolution satellite imagery and a deep learning algorithm (a convolutional neural network—CNN) for the automatic detection of PV farms. Furthermore, in an effort to create an algorithm capable of generalizing better, all the current locations with installed PV farms (data provided from the Greek Energy Regulator Authority) in the Greek Territory (131,957 km2) were used. According to our knowledge this is the first time such an algorithm is used in order to determine the existence of PV farms and the results showed satisfying accuracy.

scholarly journals Automatic detection and tracking of marker seeds implanted in prostate cancer patients using a deep learning algorithm

2021 ◽  
Vol 46 (2) ◽  
pp. 80
Author(s):  
Prabhakar Ramachandran ◽  
Keya Amarsee ◽  
Andrew Fielding ◽  
Margot Lehman ◽  
Christopher Noble ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Cancer Patients ◽  
Learning Algorithm ◽  
Automatic Detection ◽  
Deep Learning Algorithm ◽  
Detection And Tracking

scholarly journals Testing a deep learning algorithm for automatic detection of prenatal ultrasound for under-resourced communities

2022 ◽  
Vol 226 (1) ◽  
pp. S353-S354
Author(s):  
Marika Toscano ◽  
Junior Arroyo ◽  
Ana C. Saavedra ◽  
Thomas J. Marini ◽  
Timothy M. Baran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Automatic Detection ◽  
Prenatal Ultrasound ◽  
Deep Learning Algorithm

scholarly journals Enhancing the Visibility of Delamination during Pulsed Thermography of Carbon Fiber-Reinforced Plates Using a Stacked Autoencoder

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2809 ◽  
Author(s):  
Changhang Xu ◽  
Jing Xie ◽  
Changwei Wu ◽  
Lemei Gao ◽  
Guoming Chen ◽  
...  
Keyword(s):  
Carbon Fiber ◽  
Learning Algorithm ◽  
Principal Component ◽  
Fiber Reinforced ◽  
Destructive Testing ◽  
Pulsed Thermography ◽  
Stacked Autoencoder ◽  
Deep Learning Algorithm ◽  
Novel Approach ◽  
Carbon Fiber Reinforced

The effectiveness of pulsed thermography (PT) for detecting delamination in carbon fiber-reinforced polymer (CFRP) plates has been widely verified. However, delaminations are usually characterized by weak visibility due to the influences of inspection factors and the delaminations with weak visibility are easily missed in real inspections. In this study, by introducing a deep learning algorithm—stacked autoencoder (SAE)—to PT, we propose a novel approach (SAE-PT) to enhance the visibility of delaminations. Based on the ability of SAE to learn unsupervised features from data, the thermal features of delaminations are extracted from the raw thermograms. The extracted features are then employed to construct SAE images, in which the visibility of delaminations is expected to be enhanced. To test the performance of SAE-PT, we inspected CFRP plates with prefabricated delaminations. By implementing SAE-PT on the raw inspection data, the delaminations were more clearly indicated in the constructed SAE images. We also compare SAE-PT to the widely used principal component thermography (PCT) method to further verify the validity of the proposed approach. The results reveal that compared to PCT, SAE-PT can show delaminations in CFRP with higher contrast. By effectively enhancing the delamination visibility, SAE-PT thus has potential for improving the inspection accuracy of PT for non-destructive testing (NDT) of CFRP.

scholarly journals Forewarned is forearmed: Using AI to detect SDR tendencies as personnel selection tools

2021 ◽  
Author(s):  
Tony Lee ◽  
Matthias Ziegler
Keyword(s):  
Deep Learning ◽  
Personnel Selection ◽  
Learning Algorithm ◽  
Job Applicants ◽  
Baseline Model ◽  
Deep Learning Algorithm ◽  
Novel Approach ◽  
Deep Image ◽  
Selection For ◽  
Social Desirable Responding

Current practices of personnel selection often use questionnaires and interviews to assess candidates’ personality, but the effectiveness of both approaches can be hampered if social desirable responding (SDR) occurs. Detecting biases like SDR is important to ensure valid personnel selection for any organization, yet current instruments for assessing SDR are either inefficient or insufficient. In this paper, we propose a novel approach to appraise job applicants’ SDR tendency by employing Artificial Intelligence (AI)-based techniques. Our study extracts thousands of image and voice features from the video presentation of 91 simulated applicants to train two deep learning models for predicting their SDR tendency. The result shows that our two models, namely the Deep Image Model and Deep Voice Model, can predict SDR tendency with 82.55% and 88.89% accuracy rate, respectively. The Deep Voice Model moreover outperformed the baseline model built on a popular deep learning algorithm ResNet by 4.35%. These findings suggest that organizations can use AI driven technologies to assess job applicants’ SDR tendency during recruitment and improve the performance of their personnel selection.

An Improved Deep Learning Algorithm for Diabetes Prediction

2022 ◽  
pp. 103-119
Author(s):  
Basetty Mallikarjuna ◽  
Supriya Addanke ◽  
Anusha D. J.
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Early Detection ◽  
Learning Algorithm ◽  
The Novel ◽  
Random Forest Algorithm ◽  
Deep Learning Algorithm ◽  
Novel Approach ◽  
Diabetes Prediction ◽  
Two Parameters

This chapter introduces the novel approach in deep learning for diabetes prediction. The related work described the various ML algorithms in the field of diabetic prediction that has been used for early detection and post examination of the diabetic prediction. It proposed the Jaya-Tree algorithm, which is updated as per the existing random forest algorithm, and it is used to classify the two parameters named as the ‘Jaya' and ‘Apajaya'. The results described that Pima Indian diabetes dataset 2020 (PIS) predicts diabetes and obtained 97% accuracy.

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