User-generated multi-media content, such as images, text, videos, and speech, has recently become more popular on social media sites as a means for people to share their ideas and opinions. One of the most popular social media sites for providing public sentiment towards events that occurred during the COVID-19 period is Twitter. This is because Twitter posts are short and constantly being generated. This paper presents a deep learning approach for sentiment analysis of Twitter data related to COVID-19 reviews. The proposed algorithm is based on an LSTM-RNN-based network and enhanced featured weighting by attention layers. This algorithm uses an enhanced feature transformation framework via the attention mechanism. A total of four class labels (sad, joy, fear, and anger) from publicly available Twitter data posted in the Kaggle database were used in this study. Based on the use of attention layers with the existing LSTM-RNN approach, the proposed deep learning approach significantly improved the performance metrics, with an increase of 20% in accuracy and 10% to 12% in precision but only 12–13% in recall as compared with the current approaches. Out of a total of 179,108 COVID-19-related tweets, tweets with positive, neutral, and negative sentiments were found to account for 45%, 30%, and 25%, respectively. This shows that the proposed deep learning approach is efficient and practical and can be easily implemented for sentiment classification of COVID-19 reviews.
Wireless sensor networks have revolutionized the way healthcare works replacing the traditional methods with sensor-enabled IoT devices that help in monitoring the data. The data is collected by these sensors that are there on the body of the user, the data is transmitted over the network to the healthcare monitoring systems. The transmission follows the route of the wireless channel that is not secure as it can be accessed by legitimate as well as illegitimate users. These pose security threats; one such attack is a replication attack. This makes the replicas of the original node, replaces the data with the malicious content for attacking the system, and deploys the node back to the network making it difficult to detect. The aim of the work is to review the Blockchain-based intelligent monitored security system for the detection of replication attacks in the wireless healthcare network. The method used for review is the secondary research method. The main focus of the work is kept on the literature review for obtaining insights and knowledge. The results show that blockchain provides the required security to the data carried by the sensor-enabled IoT. The result contributed to the understanding of the different blockchain techniques in securing data. The system component is farmed in the work and verified in the results.
Supervised or unsupervised classification is the main objective of pattern recognition. The statistical approach is the most popular approach that is practised among the several frameworks where pattern recognition is initially formulated. In the recent past, the neural network technique and the methodology scheme from the statistical learning theory have garnered the attention of people. It requires proper attention to deal with the design of the recognition system. There are several issues associated with the design of the recognition system. They are the pattern class definition, sensing environment and representation extraction and selection of features, cluster analysis, classifier design, learning, and choosing the training and test samples. There is no solution to the general issue of recognizing complex patterns associated with arbitrary patterns. Data mining, web searching, and retrieval of multimedia are the various emerging applications that require proper and effective regulation techniques. The main purpose of this paper is to give a detailed overview of the various methods that can be used in the different stages of the pattern recognition system. The paper also aims to figure out the research topics in the application that can be highlighted in this challenging field.
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