Construction of Hybrid Model for English News Headline Sarcasm Detection by Word Embedding Technique

People often use sarcasm to taunt, anger, or amuse one another. Scathing undertones can't be missed, even when using a simple sentiment analysis tool. Sarcasm may be detected using a variety of machine learning techniques, including rule-based approaches, statistical approaches, and classifiers. Since English is a widely used language on the internet, most of these terms were created to help people recognize sarcasm in written material. Convolutional Neural Networks (CNNs) are used to extract features, and Naive Bayes (NBs) are trained and evaluated on those features using a probability function. This suggested approach gives a more accurate forecast of sarcasm detection based on probability prediction. This hybrid machine learning technique is evaluated according to the stretching component in frequency inverse domain, the cluster of the words and word vectors with embedding. Based on the findings, the proposed model surpasses many advanced algorithms for sarcasm detection, including accuracy, recall, and F1 scores. It is possible to identify sarcasm in a multi-domain dataset using the suggested model, which is accurate and resilient.

Applied TRL Calibration Technique Using ABCD Matrices for Bended Access Ports

Calibrating network analyzer is still an issue for bended access port devices (devices with access). Bended accesses can give additional errors which are taken into account by using a new design standards of Vector Network Analyzer (VNA) calibration. This Thru-Reflect-Line calibration technique is computed from ABCD parameters that easily allow to remove the bended port effects. This approach is based on the assumption that the Vector Network Analyzer error boxes can be considered as passive system. Furthermore, the method can be applied for de-embedding devices with bended accesses. This calibration and de-embedding technique could be applied, for example, to a coplanar circulator or a power divider measurement which have access lines at 120° from each other.

A High Fidelity Authentication Scheme for AMBTC Compressed Image Using Reference Table Encoding

In this paper, we propose a high-quality image authentication method based on absolute moment block truncation coding (AMBTC) compressed images. The existing AMBTC authentication methods may not be able to detect certain malicious tampering due to the way that the authentication codes are generated. In addition, these methods also suffer from their embedding technique, which limits the improvement of marked image quality. In our method, each block is classified as either a smooth block or a complex one based on its smoothness. To enhance the image quality, we toggle bits in bitmap of smooth block to generate a set of authentication codes. The pixel pair matching (PPM) technique is used to embed the code that causes the least error into the quantization values. To reduce the computation cost, we only use the original and flipped bitmaps to generate authentication codes for complex blocks, and select the one that causes the least error for embedment. The experimental results show that the proposed method not only obtains higher marked image quality but also achieves better detection performance compared with prior works.

Sentiment Analysis of Healthcare Reviews Using Context-Based Feature Weight Embedding Technique

Healthcare reviews play a major role in providing feedback to consumers as well as medical care information to users. Historically, the sentiment analysis of clinical documents will help patients in analyzing the medicines and identifying the relevant medicines. Existing methods of word embeddings use only the context of words; hence, they ignore the sentiment of texts. Medical review analysis is important due to several reasons. Patients will know the results of using medicines since such information is not easily obtained from any other source. Historical results of predictive analysis say that among people aged 55-80, the death rate from 2005 to 2015 in the US was at the top for the deadliest disease, which increased exponentially. Traditional machine learning techniques use a lexical approach for feature extraction. In this paper, baseline algorithms are checked with the proposed work of the recurrent network, and results show that the method outperforms baseline methods by a significant improvement in terms of precision, recall, f-score, and accuracy.