“Synthesis and in-vitro Glucosidase Inhibitory Activity of Some Novel Indole Derivatives”

The objective of this paper is to focus on developing new entities of the heterocyclic molecule for its in-vitro inhibitory activities of glucosidase enzyme and the preparation and separation of new entities of the indole derivatives. Most of the indole derivatives are synthesized from the Phenyl Hydrazine and Ketone as a precursor. The prepared molecules were thoroughly tested for qualitative and quantitative analysis such as preliminary test, melting points, boiling points, Thin Layer Chromatography, Infra-Red spectroscopy, proton NMR spectroscopy, and element are determined by confirmation of final structure. The Indole ring compounds were analyzed and evaluated for glucosidase inhibitory activity by the In-vitro studies through the glucosidase inhibitory activity assay. Acarbose was used as a standard drug. The present data of in-vitro studies have led to the probable conclusion that the new Indole analogs would be taken as efficient glucosidase inhibitory activity.</jats:p

Random Forest: A Hybrid Implementation for Sarcasm Detection in Public Opinion Mining

Modelling the sentiment with context is one of the most important part in Sentiment analysis. There are various classifiers which helps in detecting and classifying it. Detection of sentiment with consideration of sarcasm would make it more accurate. But detection of sarcasm in people review is a challenging task and it may lead to wrong decision making or classification if not detected. This paper uses Decision Tree and Random forest classifiers and compares the performance of both. Here we consider the random forest as hybrid decision tree classifier. We propose that performance of random forest classifier is better than any other normal decision tree classifier with appropriate reasoning.</jats:p

Sarcasm Detection using Naïve Bayes SVM Hybrid

Sarcasm detection plays a vital role in Sentiment analysis and sentiment classification as the occurrence of sarcasm in an input text may drive Sentiment Analysis job in different (Wrong) classification. Our research work aims in sarcasm detection using basic ML approaches like Naïve Bayes and SVM. Understanding the importance of each model and its merits and combining them accordingly. This work majorly aims at building a hybrid model which leads to better accuracy which will help readers for better decision making.</jats:p

Understanding the Perception of Road Segmentation and Traffic Light Detection u sing Machine Learning Techniques

Advanced Driving Assistance System (ADAS) has seen tremendous growth over the past 10 years. In recent times, luxury cars, as well as some newly emerging cars, come with ADAS application. From 2014, Because of the entry of the European new car assessment programme (EuroNCAP) [1] in the AEBS test, it helped gain momentum the introduction of ADAS in Europe [1]. Most OEMs and research institutes have already demonstrated on the self-driving cars [1]. So here, a focus is made on road segmentation where LiDAR sensor takes in the image of the surrounding and where the vehicle should know its path, it is fulfilled by processing a convolutional neural network called semantic segmentation on an FPGA board in 16.9ms [3]. Further, a traffic light detection model is also developed by using NVidia Jetson and 2 FPGA boards, collectively named as 'Driving brain' which acts as a super computer for such networks. The results are obtained at higher accuracy by processing the obtained traffic light images into the CNN classifier [5]. Overall, this paper gives a brief idea of the technical trend of autonomous driving which throws light on algorithms and for advanced driver-assistance systems used for road segmentation and traffic light detection.</jats:p