Classification of Continuous Sky Brightness Data Using Random Forest

Priyatikanto, Rhorom; Mayangsari, Lidia; Prihandoko, Rudi A.; Admiranto, A. G.

doi:10.1155/2020/5102065

Cited by 6 publications

(6 citation statements)

References 35 publications

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“…Once the tree is fully formed, the validation data points traverse through the tree following a specific path and reach a leaf node that gives the corresponding output value. Finally, the output from the forest of trees is averaged to get the final output for a data point [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

Deep Learning‐Based Methods for Sentiment Analysis on Nepali COVID‐19‐Related Tweets

Sitaula

Basnet

Mainali³

et al. 2021

Computational Intelligence and Neuroscience

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COVID-19 has claimed several human lives to this date. People are dying not only because of physical infection of the virus but also because of mental illness, which is linked to people’s sentiments and psychologies. People’s written texts/posts scattered on the web could help understand their psychology and the state they are in during this pandemic. In this paper, we analyze people’s sentiment based on the classification of tweets collected from the social media platform, Twitter, in Nepal. For this, we, first, propose to use three different feature extraction methods—fastText-based (ft), domain-specific (ds), and domain-agnostic (da)—for the representation of tweets. Among these three methods, two methods (“ds” and “da”) are the novel methods used in this study. Second, we propose three different convolution neural networks (CNNs) to implement the proposed features. Last, we ensemble such three CNNs models using ensemble CNN, which works in an end-to-end manner, to achieve the end results. For the evaluation of the proposed feature extraction methods and CNN models, we prepare a Nepali Twitter sentiment dataset, called NepCOV19Tweets, with 3 classes (positive, neutral, and negative). The experimental results on such dataset show that our proposed feature extraction methods possess the discriminating characteristics for the sentiment classification. Moreover, the proposed CNN models impart robust and stable performance on the proposed features. Also, our dataset can be used as a benchmark to study the COVID-19-related sentiment analysis in the Nepali language.

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Section: Methodsmentioning

confidence: 99%

Deep Learning‐Based Methods for Sentiment Analysis on Nepali COVID‐19‐Related Tweets

Sitaula

Basnet

Mainali³

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Once the trees are fully formed, each test sample is travelled through each tree from root to leaf node and its label is determined from each tree. Finally, the output of all trees is averaged to get the final output of data point [ 36 ].…”

Section: Proposed Approachmentioning

confidence: 99%

A Hybrid Feature Extraction Method for Nepali COVID-19-Related Tweets Classification

Shahi

Sitaula

Paudel

2022

Computational Intelligence and Neuroscience

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COVID-19 is one of the deadliest viruses, which has killed millions of people around the world to this date. The reason for peoples’ death is not only linked to its infection but also to peoples’ mental states and sentiments triggered by the fear of the virus. People’s sentiments, which are predominantly available in the form of posts/tweets on social media, can be interpreted using two kinds of information: syntactical and semantic. Herein, we propose to analyze peoples’ sentiment using both kinds of information (syntactical and semantic) on the COVID-19-related twitter dataset available in the Nepali language. For this, we, first, use two widely used text representation methods: TF-IDF and FastText and then combine them to achieve the hybrid features to capture the highly discriminating features. Second, we implement nine widely used machine learning classifiers (Logistic Regression, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, Decision Trees, Random Forest, Extreme Tree classifier, AdaBoost, and Multilayer Perceptron), based on the three feature representation methods: TF-IDF, FastText, and Hybrid. To evaluate our methods, we use a publicly available Nepali-COVID-19 tweets dataset, NepCov19Tweets, which consists of Nepali tweets categorized into three classes (Positive, Negative, and Neutral). The evaluation results on the NepCOV19Tweets show that the hybrid feature extraction method not only outperforms the other two individual feature extraction methods while using nine different machine learning algorithms but also provides excellent performance when compared with the state-of-the-art methods.

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“…The data acquired from Timau are then pre-processed to complement them with the lunar phase data and to identify invalid measurements. The erroneous and invalid data were identified using the Random Forest classifier described by Priyatikanto et al (2020). Basically, the classifier characterised the nightly data according to the general statistics and several measures of fluctuation.…”

Section: In-situ Sky Brightness Datamentioning

confidence: 99%

“…Furthermore, the sky condition can either be categorised as photometric, spectroscopic, or overcast. In line with that, Priyatikanto et al (2020) trained a random forest model to perform a similar classification task based on some statistical figures extracted from the SQM data. Additionally, the variability of moonlit sky brightness can also be used to estimate the scattering and extinction parameters to a certain degree of accuracy (Yao et al 2013;Priyatikanto 2020).…”

Section: Introductionmentioning

confidence: 99%

Characterisation of Timau National Observatory using limited in-situ measurements

Priyatikanto,

Mumpuni,

Hidayat

et al. 2022

Preprint

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A new astronomical observatory in southeastern Indonesia is currently under construction. This Timau National Observatory will host a 3.8-metre telescope for optical and near-infrared observations. To support the operation and planning, the characterisation of the site needs to be appropriately performed. However, limited resources and access to the site hindered the deployment of instruments for comprehensive site testing. Fortunately, in-situ sky brightness data from the Sky Quality Meter (SQM) have been available for almost two years. Based on the data acquired in 470 nights, we obtain a background sky brightness of 𝜇 0 = 21.86 ± 0.38 magnitude/arcsec 2 . Additionally, we evaluate the moonlit sky brightness to estimate the atmospheric extinction coefficient (𝑘) and level of scattering on site. We find an alleviated value of 𝑘 = 0.48 ± 0.04, associated with a high atmospheric aerosol content. It is considered regular for an equatorial area situated at a low altitude (∼1300 masl). By analysing the fluctuation of the sky brightness and infrared images from Himawari-8 satellite, we estimate the available observing time (AOT) of at least 5.3 hours/night and the yearly average percentage of usable nights of 66%. The monthly average AOT from SQM and satellite data analysis correlate with 𝑅 = 0.82. In terms of the monthly percentage of usable nights, the correlation coefficient is 𝑅 = 0.78. During the wet season (November-April), the results from SQM and satellite data analysis deviate more significantly, mainly due to the limited capability of Himawari-8 in detecting fragmented low-altitude clouds. According to these results, we expect Timau to complement other observatories greatly.

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Classification of Continuous Sky Brightness Data Using Random Forest

Cited by 6 publications

References 35 publications

Deep Learning‐Based Methods for Sentiment Analysis on Nepali COVID‐19‐Related Tweets

Deep Learning‐Based Methods for Sentiment Analysis on Nepali COVID‐19‐Related Tweets

A Hybrid Feature Extraction Method for Nepali COVID-19-Related Tweets Classification

Characterisation of Timau National Observatory using limited in-situ measurements

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