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

Ethyl 4-(2-fur­yl)-2-oxochroman-3-carboxyl­ate

The title compound, C16H14O5, was prepared from the reaction of 3-carbethoxy­coumarin with furan in the presence of AlCl3 as catalyst. In the crystal, inter­molecular C—H⋯O hydrogen-bonding inter­actions between four mol­ecules lead to a tetra­mer in the unit cell. The furan ring is anti­periplanar [C—C—C—O = 167.9 (13)°] and the ethoxy­carbonyl group is (−)anti­clinal [C—C—C—O = −128.6 (14)°] to the lactone ring

A probabilistic method for the operation of three-phase unbalanced active distribution networks

YesThis paper proposes a probabilistic multi-objective optimization method for the operation of three-phase distribution networks incorporating active network management (ANM) schemes including coordinated voltage control and adaptive power factor control. The proposed probabilistic method incorporates detailed modelling of three-phase distribution network components and considers different operational objectives. The method simultaneously minimizes the total energy losses of the lines from the point of view of distribution network operators (DNOs) and maximizes the energy generated by photovoltaic (PV) cells considering ANM schemes and network constraints. Uncertainties related to intermittent generation of PVs and load demands are modelled by probability density functions (PDFs). Monte Carlo simulation method is employed to use the generated PDFs. The problem is solved using ɛ-constraint approach and fuzzy satisfying method is used to select the best solution from the Pareto optimal set. The effectiveness of the proposed probabilistic method is demonstrated with IEEE 13- and 34- bus test feeders