Sign language, a language used by Deaf community, is a fully visual language with its own grammar. The Deaf people find it very difficult to express their feelings to the other people, since the other people lack the knowledge of the sign language used by the Deaf community. Due to the differences in vocabulary and grammar of the sign languages, complete adoption of methods used for other international sign languages is not possible for Indian Sign Language (ISL) recognition. It is difficult to handle continuous sign language sentence recognition and translation into text as no large video dataset for ISL sentences is available. INSIGNVID -the first Indian Sign Language video dataset has been proposed and with this dataset as input, a novel approach is presented that converts video of ISL sentence in appropriate English sentence using transfer learning. The proposed approach gives promising results on our dataset with MobilNetV2 as pretrained model.
The number of attacks increased with speedy development in web communication in the last couple of years. The Anomaly Detection method for IDS has become substantial in detecting novel attacks in Intrusion Detection System (IDS). Achieving high accuracy are the significant challenges in designing an intrusion detection system. It also emphasizes applying different feature selection techniques to identify the most suitable feature subset. The author uses Extremely randomized trees (Extra-Tree) for feature importance. The author tries multiple thresholds on the feature importance parameters to find the best features. If single classifiers use, then the classifier's output is wrong, so that the final decision may be wrong. So The author uses an Extra-Tree classifier applied to the best-selected features. The proposed method is estimated on standard datasets KDD CUP'99, NSL-KDD, and UNSW-NB15. The experimental results show that the proposed approach performs better than existing methods in detection rate, false alarm rate, and accuracy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.