Evolution of novel activation functions in neural network training for astronomy data: habitability classification of exoplanets

Saha, Snehanshu; Nagaraj, Nithin; Mathur, Archana; Yedida, Rahul; Sneha, H R

doi:10.1140/epjst/e2020-000098-9

Cited by 21 publications

(23 citation statements)

References 66 publications

(107 reference statements)

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“…If there is no non-linear activation function, the deep CNN architecture will evolve into a single equivalent convolutional layer, and its performance will hardly be so. The ReLU activation function is used explicitly as a non-linear activation function, in contrast to other non-linear functions such as Sigmoid, because it has been observed from experience that the CNN using ReLU trains faster than the corresponding CNN [79]. Furthermore, the ReLU activation function is a one-to-one mathematical operation, as shown in Equation (6).…”

Section: An Overview Of Machine Learning In Agriculturementioning

confidence: 99%

Weed Detection in Rice Fields Using Remote Sensing Technique: A Review

et al. 2021

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This paper reviewed the weed problems in agriculture and how remote sensing techniques can detect weeds in rice fields. The comparison of weed detection between traditional practices and automated detection using remote sensing platforms is discussed. The ideal stage for controlling weeds in rice fields was highlighted, and the types of weeds usually found in paddy fields were listed. This paper will discuss weed detection using remote sensing techniques, and algorithms commonly used to differentiate them from crops are deliberated. However, weed detection in rice fields using remote sensing platforms is still in its early stages; weed detection in other crops is also discussed. Results show that machine learning (ML) and deep learning (DL) remote sensing techniques have successfully produced a high accuracy map for detecting weeds in crops using RS platforms. Therefore, this technology positively impacts weed management in many aspects, especially in terms of the economic perspective. The implementation of this technology into agricultural development could be extended further.

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Section: An Overview Of Machine Learning In Agriculturementioning

confidence: 99%

Weed Detection in Rice Fields Using Remote Sensing Technique: A Review

et al. 2021

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“…shown by Saha et al [57], sigmoid activation has a few limitations which impede its ability to reduce error in backpropagation some times. This includes not having a critical point and, therefore, being handicapped with the absence of global optima.…”

Section: Phase Ii: a Deeper Analysismentioning

confidence: 99%

“…For each of these cases, 80% of samples were used for training set and 20% for testing. Eliminating the need for parameters tuning, SBAF parameters were computed from the fixed point plots taken from [57] and were set to α = 0.5 and k = 0.91. The results of SBAF are compared with sigmoid and presented in Tables 9 and 10.…”

Section: Relevant Data: All Casesmentioning

confidence: 99%

Habitability classification of exoplanets: a machine learning insight

Basak

Mathur²,

Theophilus

et al. 2021

Eur. Phys. J. Spec. Top.

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We explore the efficacy of machine learning (ML) in characterizing exoplanets into different classes. The source of the data used in this work is University of Puerto Rico's Planetary Habitability Laboratory's Exoplanets Catalog (PHL-EC). We perform a detailed analysis of the structure of the data and propose methods that can be used to effectively categorize new exoplanet samples. Our contributions are twofold. We elaborate on the results obtained by using ML algorithms by stating the accuracy of each method used and propose a paradigm to automate the task of exoplanet classification for relevant outcomes. In particular, we focus on the results obtained by novel neural network architectures for the classification task, as they have performed very well despite complexities that are inherent to this problem. The exploration led to the development of new methods fundamental and relevant to the context of the problem and beyond. The data exploration and experimentation also result in the development of a general data methodology and a set of best practices which can be used for exploratory data analysis experiments.

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“…ML has been extensively used to solve challenging problems of different domains. An extensive exercise is attempted by Saha et al [71] to perform a detailed experiment to categorize and classify new exoplanet samples. The method uses NN as base architecture, and plugs-in different activation functions to investigate how well they perform on the PHL-EC dataset.…”

Section: Neural Network On Phl-ec Datasetmentioning

confidence: 99%

Quantifying the Classification of Exoplanets: in Search for the Right Habitability Metric

Safonova,

Mathur,

Basak

et al. 2021

Preprint

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What is habitability? Can we quantify it? What do we mean under the term habitable or potentially habitable planet? With estimates of the number of planets in our Galaxy alone running into billions, possibly a number greater than the number of stars, it is high time to start characterizing them, sorting them into classes/types just like stars, to better understand their formation paths, their properties and, ultimately, their ability to beget or sustain life. After all, we do have life thriving on one of these billions of planets, why not on others? Which planets are better suited for life and which ones are definitely not worth spending expensive telescope time on? We need to find sort of quick assessment score, a metric, using which we can make a list of promising planets and dedicate our efforts to them. Exoplanetary habitability is a transdisciplinary subject integrating astrophysics, astrobiology, planetary science, even terrestrial environmental sciences. It became a challenging problem in astroinformatics, an emerging area in computational astronomy. Here, we review the existing metrics of habitability and the new classification schemes (machine learning (ML), neural networks, activation functions) of extrasolar planets, and provide an exposition of the use of computational intelligence techniques to evaluate habitability scores and to automate the process of classification of exoplanets. We examine how solving convex optimization techniques, as in computing new metrics such as a CDHS and b CEESA, cross-validates ML-based classification of exoplanets. Despite the recent criticism of exoplanetary habitability ranking, we are sure that this field has to continue and evolve to use all available machinery of astroinformatics, artificial intelligence (AI) and machine learning. It might actually develop into a sort of same scale as stellar types in astronomy, to be used as a quick tool of screening exoplanets in important characteristics in search for potentially habitable planets (PHPs), or Earth-like planets, for detailed follow-up targets.

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Evolution of novel activation functions in neural network training for astronomy data: habitability classification of exoplanets

Cited by 21 publications

References 66 publications

Weed Detection in Rice Fields Using Remote Sensing Technique: A Review

Weed Detection in Rice Fields Using Remote Sensing Technique: A Review

Habitability classification of exoplanets: a machine learning insight

Quantifying the Classification of Exoplanets: in Search for the Right Habitability Metric

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