No abstract

No abstract

Out of all non-linguistic communications, one of the most popular is face expression and is capable of communicating effectively with others. We have number of applications of facial expressions in as sorted arenas comprising of medicine like psychology, security, gaming, Classroom communication and even commercial creativities. Owing to huge intra-class distinction it is still challenging to recognize the emotions automatically based on facial expression though it is a vigorous area of research since decades. Conventional lines for this approach are dependent on hand-crafted characteristics like Scale Invariant Feature Transform, Histogram of Oriented Gradient and Local Binary Patterns surveyed by a classifier which is applied on a dataset. Various types of architectures were applied for restored performance as Deep learning proved an outstanding feat. The goal of this study is to create a deep learning model on automatic facial emotion recognition FER. The proposed model efforts more on pulling out the crucial features, thereby, advances the expression recognition accuracy, and beats the competition on FER2013 dataset.

Medical care services are changing to address problems with the development of big data frameworks as a result of the widespread use of big data analytics. Covid illness has recently been one of the leading causes of death in people. Since then, related input chest X-ray image for diagnosing COVID illness have been enhanced by diagnostic tools. Big data technological breakthroughs provide a fantastic option for reducing contagious Covid disease. To increase the model's confidence, it is necessary to integrate a large number of training sets, however handling the data may be difficult. With the development of big data technology, a unique method to identify and categorise covid illness is now found in this research. In order to manage incoming big data, a massive volume of chest x-ray images is gathered and analysed using a distributed computing server built on the Hadoop framework. In order to group identical groups in the input x-ray images, which in turn segments the dominating portions of an image, the fuzzy empowered weighted k-means algorithm is then employed. A hybrid quantum dilated convolution neural network is suggested to classify various kinds of covid instances, and a Black Widow-based Moth Flame is also shown to improve the performance of the classifier pattern. The performance analysis of COVID-19 detection makes use of the COVID-19 radiography dataset. The suggested HQDCNet approach has an accuracy of 99.01. The experimental results are evaluated in Python using performance metrics such as accuracy, precision, recall, f-measure, and loss function.

Traditional drug discovery is an expensive and time consuming process. Pharmaceutical industry suffers from a huge attrition due to last stage failure in traditional drug discovery. Bioinformatics principles can be utilized to overcome this pressure and speedup the process of drug discovery. Computer aided drug design is a remedy to avoid this loss. Drug design means designing the ligand that has high affinity towards target protein. This can be achieved by Virtual Screening. Ligand based virtual screening utilizes information from the ligand about the target. It is a ligand centric approach. The availability of three dimensional structures of protein targets and their possible ligands are utilized for identification and optimization of lead molecules (positive hits) in Structure based virtual screening. It is a target centric approach. To find out fit poses of ligand and its affinity at the active site of target Molecular Docking is done. Molecular docking is tool that contains search algorithm and scoring function. Search algorithms predict the binding modes of a target and fit ligand conformations towards the target. Scoring function is involved in prediction of the affinity of a ligand to bind to a protein target. There are various plat forms and scoring functions for predicting ligand – protein interactions. Consensus Scoring is a technique of combing information from multiple scoring functions and gives relatively accurate result when compared to single scoring function. It shows improvement in terms of quality of hit scores, false positive rate and enrichment. Consensus Scoring gives better, accurate and consistent results across the receptor systems when compared to individual or single scoring functions.