Concepts,,,,(සංකල්ප)

https://stackoverflow.com/questions/62778867/opening-port-19132-on-an-oracle-compute-instance-ubuntu-20-04

https://newbedev.com/only-authenticate-with-google-auth-provider-if-user-exists

Firebase nodejs operations are async and the request like check the exhistance and then proceed with reminder need to be done is sync way using promises . This requires to done using firebase admin client if you are developing rest api and  follwing function is a example for it. usersRef.orderByChild('email').equalTo(result.email).once('value').then(function(snapshot) {                     isExhist= snapshot.exists();                       if (isExhist) {                        }                   else {                    } });

 I have to utlised the combined letters of Sinhala for my study. This is not a problem of English, however, a language having vowel sounds by combining parts with a letter, the analysis of letters within the word ideally need to carry out with combing parts.   Output : ['ඵ', ';', '\u200c', ':', 'ෙ', 'ු', 'ඕ', 'c', 'ප', 'ථ', 'ඥ', 'x', '්', '2', '☔', 'උ', 'n', 'ම', '3', '}', '?', 'ඃ', 'ත', 'හ', '0', 'ො', 'z', 'ෲ', ',', 'ඔ', 'ඳ', 'i', 'ං', '▪', 'ි', '[', 'ඤ', 'ස', 'b', 'ූ', '_', '☁', 'ඟ', 'ඩ', 'ෑ', 'ේ', '1', 'අ', 'ආ', 'ට', 'ී', 'q', '•', 'd', 'ද', '–

This article highlights the milestones in approach in developing www.panhidhalyrics.com. Anyone researching in the same area can use the following as a guideline to improve their approach.  2020: Dec Deprication of the probabilistic basic model of song generations developed using Django  2021 : Jan  RNN based raw impleneration for predict sinhala words using larger sample of data (not pre-prossed) fushenkao - https://github.com/fushenkao/Sinhala-Lyrics-Gen  minimaxir - https://github.com/minimaxir/textgenrnn  පසුතලය : කැටපත් පවුර, සීගිරිය (Sigiri Graffiti) http://thenationaltrust.lk/wp-content/uploads/2018/06/nds-nt-sigiriya.pdf when the dataset becomes bigger training conducted using the GoogleColab to generate the model (refer following notebook) :  https://colab.research.google.com/drive/1YlooyHnyhK8BcmrHF87resDT8O1FAppf?usp=sharing 2021 : Feb Approach to adopt LSTM assuming the relationship between stanzas in a sequence. The basis of the following Notebook adopted as the inception.

  The raw data recorded when a change detected in the stream enables more realistic data recording comparison to time based sampling.Thus, sampling and interpolation required for most of the comparative analysis using time series, limit the usage of raw data for extended analysis. However, you might wonder how it can distort the realistic nature of the data (except the probable anomalies) and leading to false interpretation.Lets focus on our theme. For the analysis I am using fe w  selected variables from water quality dataset recorded from  Baffle Creek   and  Byrnett River .  First I will combine the two datasets into single dataset without sampling using panads merge function. The Pearson Correlation Coefficient (PCC) calculated for the combined dataset and the heat-map of the results given below. Figure 1 : PCC between variables in two un-sampled datasets From the results, it is obvious that the two datasets may have correlations in-between the variable inside the dateset, thus, no