A morphological study of galaxies in ZwCl0024+1652, a galaxy cluster at redshift z ∼ 0.4

Abstract The well-known cluster of galaxies ZwCl0024+1652 at z ∼ 0.4 lacks an in-depth morphological classification of its central region. While previous studies provide a visual classification of a patched area, we used the public code called galaxy Support Vector Machine (galsvm) and HST/ACS data as well as the WFP2 master catalogue to automatically classify all cluster members up to 1 Mpc. galsvm analyses galaxy morphologies through support vector machine (SVM). From the 231 cluster galaxies, we classified 97 as early types (ETs) and 83 as late types (LTs). The remaining 51 stayed unclassified (or undecided). By cross-matching our results with the existing visual classification, we found an agreement of 81 per cent. In addition to previous Zwcl0024 morphological classifications, 121 of our galaxies were classified for the first time in this work. In addition, we tested the location of classified galaxies on the standard morphological diagrams, colour–colour and colour–magnitude diagrams. Out of all cluster members, ∼20 per cent are emission-line galaxies, taking into account previous GLACE results. We have verified that the ET fraction is slightly higher near the cluster core and decreases with the clustercentric distance, while the opposite trend has been observed for LT galaxies. We found a higher fraction of ETs (54 per cent) than LTs (46 per cent) throughout the analysed central region, as expected. In addition, we analysed the correlation between the five morphological parameters (Abraham concentration, Bershady–Concelice concentration, asymmetry, Gini, and M20 moment of light) and the clustercentric distance, without finding a clear trend. Finally, as a result of our work, the morphological catalogue of 231 galaxies containing all the measured parameters and the final classification is available in the electronic form of this paper.

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A support vector machine for spectral classification of emission-line galaxies from the Sloan Digital Sky Survey

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stv1617 ◽

2015 ◽

Vol 453 (1) ◽

pp. 122-127 ◽

Cited By ~ 7

Author(s):

Fei Shi ◽

Yu-Yan Liu ◽

Guang-Lan Sun ◽

Pei-Yu Li ◽

Yu-Ming Lei ◽

...

Keyword(s):

Support Vector Machine ◽

Emission Line ◽

Sloan Digital Sky Survey ◽

Support Vector ◽

Spectral Classification ◽

Sky Survey ◽

Emission Line Galaxies

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SENTIMENT ANALYSIS OF THE LARGE PRIEST OF FPI’S RETURN USING SUPPORT VECTOR MACHINE WITH OVERSAMPLING METHOD

Jurnal Riset Informatika ◽

10.34288/jri.v4i1.262 ◽

2021 ◽

Vol 4 (1) ◽

pp. 17-22

Author(s):

Zetta Nillawati Reyka Putri ◽

Muhammad Muhajir

Keyword(s):

Support Vector Machine ◽

Support Vector ◽

Test Results ◽

Text Data ◽

Machine Method ◽

Svm Classification ◽

The Public ◽

Positive Class ◽

Negative Class

At the end of 2020, Habib Rizieq's return to Indonesia drew criticism from the public for causing crowds during the Covid-19 pandemic. News and opinions about Habib Rizieq fill internet platforms, including Twitter. The researcher wants to classify the opinion text data of Habib Rizieq's return from Twitter into positive and negative sentiments using the Support Vector Machine method. Opinion data comes from Twitter, so the data is analyzed by text mining through the preprocessing stage. The SVM classification of unbalanced data between positive and negative classes resulted in 95.06% accuracy with a negative class precision value of 84% and better than 72% recall, in the positive class the precision value was 96% less than 2% of recall 98%. While the SVM classification with the oversampling method gets 100% accuracy, precision, and recall. The results of positive sentiments are known that the public will always support and want freedom for Rizieq, for negative sentiments it is known that many people are disappointed with Rizieq regarding the lies of his swab test results.

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Galaxy evolution studies in clusters: the case of Cl0024 + 1652 cluster galaxies at z ∼ 0.4

Proceedings of the International Astronomical Union ◽

10.1017/s174392132000280x ◽

2019 ◽

Vol 15 (S356) ◽

pp. 163-168

Author(s):

Zeleke Beyoro-Amado ◽

Mirjana Pović ◽

Miguel Sánchez-Portal ◽

Solomon Belay Tessema ◽

Tilahun Getachew-Woreta ◽

...

Keyword(s):

Galaxy Evolution ◽

Morphological Classification ◽

Cluster Galaxies ◽

Star Forming ◽

The Public ◽

Diagnostic Diagram ◽

Public Data ◽

Emission Line Galaxies ◽

The Universe

AbstractStudying the transformation of cluster galaxies contributes a lot to have a clear picture of evolution of the universe. Towards that we are studying different properties (morphology, star formation, AGN contribution and metallicity) of galaxies in clusters up to z ∼ 1.0 taking three different clusters: ZwCl0024 + 1652 at z ∼ 0.4, RXJ1257 + 4738 at z ∼ 0.9 and Virgo at z ∼ 0.0038. For ZwCl0024 + 1652 and RXJ1257 + 4738 clusters we used tunable filters data from GLACE survey taken with GTC 10.4 m telescope and other public data, while for Virgo we used public data. We did the morphological classification of 180 galaxies in ZwCl0024 + 1652 using galSVM, where 54 % and 46 % of galaxies were classified as early-type (ET) and late-type (LT) respectively. We did a comparison between the three clusters within the clustercentric distance of 1 Mpc and found that ET proportion (decreasing with redshift) dominates over the LT (increasing with redshift) throughout. We finalized the data reduction for ZwCl0024 + 1652 cluster and identified 46 [OIII] and 73 Hβ emission lines. For this cluster we have classified 22 emission line galaxies (ELGs) using BPT-NII diagnostic diagram resulting with 14 composite, 1 AGN and 7 star forming (SF) galaxies. We are using these results, together with the public data, for further analysis of the variations of properties in relation to redshift within z < 1.0.

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Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.131.1495 ◽

2011 ◽

Vol 131 (8) ◽

pp. 1495-1501

Author(s):

Dongshik Kang ◽

Masaki Higa ◽

Hayao Miyagi ◽

Ikugo Mitsui ◽

Masanobu Fujita ◽

...

Keyword(s):

Support Vector Machine ◽

Acoustic Signals ◽

Support Vector ◽

Amplitude Difference

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Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i2.1840 ◽

2020 ◽

Vol 4 (2) ◽

pp. 362-369

Author(s):

Sharazita Dyah Anggita ◽

Ikmah

Keyword(s):

Support Vector Machine ◽

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

The Public ◽

Svm Algorithm ◽

Bayes Algorithm ◽

Freight Forwarding ◽

Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

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