Radio Heronian Mean k-Graceful Labeling on Degree Splitting of Graphs

A mapping g:V\left(G\right)\rightarrow{k,k+1,\ldots,k+N-1} is a radio heronian mean k-labeling such that if for any two distinct vertices s and t of G, d\left(s,t\right)+\left\lceil\frac{g\left(s\right)+g\left(t\right)+\sqrt{g\left(s\right)g\left(t\right)}}{3}\right\rceil\geq1+D,for every s,t\in\ V(G), where D is the diameter of G. The radio heronian mean k-number of g, {rrhmn}_k(g), is the maximum number assigned to any vertex of G. The radio heronian mean number of G, {rhmn}_k(g), is the minimum value of {rhmn}_k(g) taken overall radio heronian mean labelings g of G. If {rhmn}_k(g)=\left|V\left(G\right)\right|+k-1, we call such graphs as radio heronian mean k-graceful graphs. In this paper, we investigate the radio heronian mean k-graceful labeling on degree splitting of graphs such as comb graph P_n\bigodot K_1, rooted tree graph {RT}_{n,n} hurdle graph {Hd}_n and twig graph\ {TW}_n.A  mapping    is a radio heronian mean k-labeling such that  if for any two distinct vertices  and  of , ,for every V(G), where  is the diameter of . The   radio heronian mean k-number of g, , is the maximum number assigned to any vertex of . The   radio heronian mean number of , , is the minimum value of  taken overall radio heronian mean labelings  of . If , we call such graphs as  radio heronian mean k-graceful graphs. In this paper, we investigate the  radio heronian mean k-graceful labeling  on degree splitting of graphs  such as comb graph ,  rooted tree graph   hurdle  graph     and  twig graph

COVID tweet analysis using NLP

The pandemic has taken the world by storm. Almost the entire world went into lockdown to save the people from the deadly COVID-19. With the progression of time, news and mindfulness about COVID-19 spread like the actual pandemic, with a blast of messages, updates, recordings, and posts. Widespread panic manifest as one more worry not withstanding the well-being risk that COVID-19 introduced. Typically, for the most part because of misinterpretations, an absence of data, or now and again by and large deception about COVID- 19 and its effects. General people however have been expressing their feelings about the safety and effectiveness of the vaccines on social media like Twitter. In this study, such tweets are being extracted from Twitter using a Twitter API authentication token. The raw tweets are stored and processed using NLP. The processed data is then classified using a CNN classification algorithm. The algorithm classifies the data into three classes, positive, negative, and neutral. These classes refer to the sentiment of the general people whose Tweets are extracted for analysis.&nbsp