On Optimality of Long Document Classification using Deep Learning

Document classification is effective with elegant models of word numerical distributions. The word embeddings are one of the categories of numerical distributions of words from the WordNet. The modern machine learning algorithms yearn on classifying documents based on the categorical data. The context of interest on the categorical data is posed with weights and the sense and quality of the sentences is estimated for sensible classification of documents. The focus of the current work is on legal and criminal documents extracted from the popular news channels, particularly on classification of long length legal and criminal documents. Optimization is the essential instrument to bring the quality inputs to the document classification model. The existing models are studied and a feasible model for the efficient document classification is proposed. The experiments are carried out with meticulous filtering and extraction of legal and criminal records from the popular news web sites and preprocessed with WordNet and Text Processing contingencies for efficient inward for the learning framework

Different methods to study gravity wave variance -A comparison

235-238Hourly wind observations made by Indian MST radar at Gadanki have been used to study gravity wave variance of period 2-6 h. Variance was calculated after the wind data were high-pass filtered with a cut-off period of 6 h. The data from the output of high-pass filter are used to calculate total horizontal variance for this band. Variance has also been calculated by power spectral density method using the filtered data. The irregular winds have been studied by another method which does not require continuous data. In this method, two successive hourly wind height profiles are subtracted which is supposed to give information of gravity waves of period 2-6 h. It is observed that this difference filter gives little higher value of variance showing that the cut-off is not sharp. This method is quite useful to obtain information from discontinuous data sets

Synthesis, molecular docking with COX 1& II enzyme, ADMET screening and in vivo anti-inflammatory activity of oxadiazole, thiadiazole and triazole analogs of felbinac

Based on the core structure of Felbinac drug, three series (4a–d, 5a–d and 6a–n) of five membered heterocyclic derivatives containing three heteroatoms were designed and synthesized starting from Felbinac. In the rational design of the target molecules, the biphenyl ring along with the methylene bridge of felbinac was retained while the carboxyl group was substituted with biologically active substituents like 1,2,4-triazole, 1,3,4-thiadiazole and 1,3,4-oxadiazole, with an intent to obtain novel, better and safer anti-inflammatory agents with improved efficacy. The prepared molecules were then investigated for their anti-inflammatory, ulcerogenicity and analgesic activity in experimental animals. The tested compounds exhibited varying degrees of inflammatory activity (25.21–72.87%), analgesic activity (27.50–65.24%) and severity index on gastric mucosa in the range of 0.20–0.80 in comparison to positive control felbinac (62.44%, 68.70% and 1.5, respectively). Among all the prepared compounds, 2-(biphenyl-4-ylmethyl)-5-(4-chlorophenyl)-1,3,4-oxadiazole (6c) emerged as the most potent NSAID compound exhibiting the highest anti-inflammatory activity (72.87% inhibition) and analgesic activity (65.24%) along with the least severity index on gastric mucosa (0.20). Further, molecular docking on cyclooxygenase and in silico ADME-Toxicity prediction studies also supported the experimental biological results and indicated that 6c has a potential to serve as a drug candidate or lead compound for developing novel anti-inflammatory and analgesic therapeutic agent(s) with minimum toxicity on gastric mucosa. Keywords: Felbinac, Oxadiazole, Triazole, Thiadiazole, Anti-inflammatory, Molecular dockin