A one-armed CNN for exoplanet detection from light curves

Visser, Koko; Bosma, Bas; Postma, Eric

doi:10.48550/arxiv.2105.06292

Cited by 3 publications

(3 citation statements)

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“…It is different from classical image recognition, where we define the image features by ourselves. But in CNN, we deal with the raw image data in the pixel form (where each image is represented in the form of an array of pixel values), train the model, and extract the features automatically for better classification and detection (Visser et al (2021)). We can illustrate the working of CNN with an example.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Identification and Classification of Skin Diseases using Deep Learning Techniques

Pai¹,

Pai²,

M³

et al. 2023

Preprint

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Health is the most important aspect of a human’s life. Humans are covered by the largest organ of their body, the skin. Protecting the body from germs and regulating body temperature are the basic functionalities of the skin. If the skin is not protected, then it may lead to skin diseases caused by bacterial infections, fungal infections, etc. The cost involved in diagnosing skin diseases may not seem cost-effective to everyone. Moreover, dermatologists need to use their professional experience and may have to invest more time in diagnosing skin diseases which usually involves the use of images captured using a dermoscope. The proposed methodology adopts Deep Learning techniques for identifying and classifying skin diseases caused by bacteria and fungi by making use of non-dermoscopic images. The problem statement is an image classification task. The dataset consists of five classes which include two classes of bacterial skin disease namely Cellulitis and Impetigo, and two classes of fungi skin diseases namely Ringworm and Sporotrichosis. Healthy Skin images are the fifth class in the dataset. The Convolutional Neural Network (CNN) is known to be the most suitable deep learning algorithm for image processing applications. The power of the transfer learning technique is utilized by applying the fine-tuned version of VGG16 architecture. A web application is developed using Streamlit to provide a smooth user interface for displaying the predicted result of a skin image. The web app facilitates a user to either upload an image or captures a real-time image. The developed solution achieves an accuracy of 86% and an F1-score of 85%. Such a deep learning model will assist dermatologists in decision making, reduce the time required for diagnosis, and also minimizes the cost involved by a significant amount.

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Identification and Classification of Skin Diseases using Deep Learning Techniques

Pai¹,

Pai²,

M³

et al. 2023

Preprint

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“…Moreover, other researchers started to further explore artificial neural networks (ANN) and convolutional neural networks (CNN) [24]. The results provided by [25][26][27][28][29][30][31], among others, show that CNNs were a better choice than the previous ML techniques for vetting planetary candidates extracted from transit-like signals in different light curves. In the first two studies [25,26], the authors used simulated light curves as the inputs of their CNN, whereas others [29] used human-vetted Kepler TCEs.…”

Section: Introductionmentioning

confidence: 99%

One-Dimensional Convolutional Neural Networks for Detecting Transiting Exoplanets

Alvarez

Díez-Alonso

Sánchez

et al. 2023

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The transit method is one of the most relevant exoplanet detection techniques, which consists of detecting periodic eclipses in the light curves of stars. This is not always easy due to the presence of noise in the light curves, which is induced, for example, by the response of a telescope to stellar flux. For this reason, we aimed to develop an artificial neural network model that is able to detect these transits in light curves obtained from different telescopes and surveys. We created artificial light curves with and without transits to try to mimic those expected for the extended mission of the Kepler telescope (K2) in order to train and validate a 1D convolutional neural network model, which was later tested, obtaining an accuracy of 99.02% and an estimated error (loss function) of 0.03. These results, among others, helped to confirm that the 1D CNN is a good choice for working with non-phased-folded Mandel and Agol light curves with transits. It also reduces the number of light curves that have to be visually inspected to decide if they present transit-like signals and decreases the time needed for analyzing each (with respect to traditional analysis).

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“…In recent years, there has been a remarkable increase in the use of supervised, unsupervised, and transfer learning techniques in science and technology. In the field of astronomy, machine learning has proven to be quite beneficial in resolving problems such as distinguishing stars from galaxies (Odewahn et al 1992;Weir et al 1995;Kim & Brunner 2017), classification of type-Ia supernovae (Möller et al 2016;Hosseinzadeh et al 2020), gravitational lens detection (Jacobs et al 2017;Schaefer et al 2018;Lanusse et al 2018), finding optical transients, radio sources and exoplanets (Cabrera-Vives et al 2017;Aniyan & Thorat 2017;Visser et al 2021). Deep learning is also used to model the dynamics of interacting galaxies and mergers in various redshift surveys and cosmological simulations (Ackermann et al 2018;Bottrell et al 2019;Prakash et al 2020;Bickley et al 2021).…”

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confidence: 99%

Identification of Grand-design and Flocculent Spirals from SDSS using Convolutional Neural network

Sarkar¹,

Narayanan²,

Banerjee³

et al. 2022

Preprint

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Spiral galaxies can be classified into the Grand-designs and Flocculents, based on the nature of their spiral arms. The Grand-designs exhibit almost continuous, high contrast spiral arms and are believed to be driven by density waves; the Flocculents, on the other hand, have patchy or discontinuous spiral features and are mostly stochastic in origin. We construct a convolutional neural network (CNN) model that classifies spirals into Grand-designs and Flocculents, with a testing accuracy of 97.2%. We then use the above model for classifying 1, 220 new spirals from the SDSS. Out of these, 721 are identified as Flocculents, the rest being Grand-designs. The mean asymptotic rotational velocity of our sample Grand-designs and Flocculents are 218 km s −1 and 145 km s −1 respectively, while their respective de Vaucouleur morphological type indices are 2.6 and 4.7. This possibly indicates that Grand-designs are mostly ordinary high surface brightness galaxies like our Milky Way, while Flocculents are intermediate-mass low surface brightness galaxies.

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A one-armed CNN for exoplanet detection from light curves

Cited by 3 publications

References 0 publications

Identification and Classification of Skin Diseases using Deep Learning Techniques

Identification and Classification of Skin Diseases using Deep Learning Techniques

One-Dimensional Convolutional Neural Networks for Detecting Transiting Exoplanets

Identification of Grand-design and Flocculent Spirals from SDSS using Convolutional Neural network

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