Fake News Detection Using Enhanced BERT

Since there are so many users on social media, who are not qualified to report news, fake news has become a major problem in recent years. Therefore, it is crucial to identify and restrict the dissemination of false information. Numerous deep learning models that make use of natural language processing have yielded excellent results in the detection of fake news. bidirectional encoder representations from transformers (BERT), based on transfer learning, is one of the most advanced models. In this work, the researchers have compared the earlier studies that employed baseline models versus the research articles where the researchers used a pretrained model BERT for the detection of fake news. The literature analysis revealed that utilizing pretrained algorithms is more effective at identifying fake news because it takes less time to train them and yields better results. Based on the results noted in this article, the researchers have advised the utilization of pretrained models that have already been taught to take advantage of transfer learning, which shortens training time and enables the use of large datasets, as well as a reputable model that performs well in terms of precision, recall, as well as the minimum number of false positive and false negative outputs. As a result, the researchers created an improved BERT model, while considering fine-tuning it to meet the demands of the fake news identification assignment. To obtain the most accurate representation of the input text, the final layer of this model is also unfrozen and trained on news texts. The dataset used in the study included 23 502 articles of fake news and 21 417 items of actual news. This dataset was downloaded from the Kaggle website. The results of this study demonstrated that the proposed model showed a better performance compared with other models, and achieved 99.96% and 99.96% in terms of accuracy and F1 score, respectively.

Video Conferencing with Sign language Detection

Video conferencing software has become an essential tool for communicating with one another across long distances. There are several video conferencing software that are utilised for communication all over the world. Huge numbers of people are unable to interact via spoken language and find typical video conferencing solutions difficult to use. Our project intends to solve this problem by creating a user-friendly Video Conferencing App that can identify sign language in real time and provide correct subtitles. Due to a lack of communication skills, deaf and hard of hearing persons confront several obstacles in their everyday lives. The covid epidemic has made traditional ways of communication extremely challenging for these people. Our goal is to bridge the gap by giving them a platform to showcase their skills. © 2022 IEEE.

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

COVID-19 is a contagious respiratory illness that can be passed from person to person. Because it affects the lungs, damages blood arteries, and causes cardiac problems, COVID-19 must be diagnosed quickly. The reverse transcriptase polymerase chain reaction (RT-PCR) is a method for detecting COVID-19, but it is time consuming and labor expensive, as well as putting the person collecting the sample in danger. As a result, clinicians prefer to use CT scan and Xray images. COVID-19 classification can be done manually, however AI makes the process go faster. AI approaches include image processing, machine learning, and deep learning. An AI model is required to diagnose COVID-19, and a dataset is necessary to train that model. A dataset consists of the information from which the model is trained. This paper consists of the review of different image processing, machine learning and deep learning papers proposed by different researchers. As well as models based on deep learning and pretrained model using gradient boosting algorithm The goal of this paper is to provide information for future researchers to work with. [ FROM AUTHOR] Copyright of International Journal of Hybrid Intelligent Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

TL-med: A Two-stage transfer learning recognition model for medical images of COVID-19.

The recognition of medical images with deep learning techniques can assist physicians in clinical diagnosis, but the effectiveness of recognition models relies on massive amounts of labeled data. With the rampant development of the novel coronavirus (COVID-19) worldwide, rapid COVID-19 diagnosis has become an effective measure to combat the outbreak. However, labeled COVID-19 data are scarce. Therefore, we propose a two-stage transfer learning recognition model for medical images of COVID-19 (TL-Med) based on the concept of "generic domain-target-related domain-target domain". First, we use the Vision Transformer (ViT) pretraining model to obtain generic features from massive heterogeneous data and then learn medical features from large-scale homogeneous data. Two-stage transfer learning uses the learned primary features and the underlying information for COVID-19 image recognition to solve the problem by which data insufficiency leads to the inability of the model to learn underlying target dataset information. The experimental results obtained on a COVID-19 dataset using the TL-Med model produce a recognition accuracy of 93.24%, which shows that the proposed method is more effective in detecting COVID-19 images than other approaches and may greatly alleviate the problem of data scarcity in this field.

COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis.

AIM: : COVID-19 is a disease caused by a new strain of coronavirus. Up to 18th October 2020, worldwide there have been 39.6 million confirmed cases resulting in more than 1.1 million deaths. To improve diagnosis, we aimed to design and develop a novel advanced AI system for COVID-19 classification based on chest CT (CCT) images. METHODS: : Our dataset from local hospitals consisted of 284 COVID-19 images, 281 community-acquired pneumonia images, 293 secondary pulmonary tuberculosis images; and 306 healthy control images. We first used pretrained models (PTMs) to learn features, and proposed a novel (L, 2) transfer feature learning algorithm to extract features, with a hyperparameter of number of layers to be removed (NLR, symbolized as L). Second, we proposed a selection algorithm of pretrained network for fusion to determine the best two models characterized by PTM and NLR. Third, deep CCT fusion by discriminant correlation analysis was proposed to help fuse the two features from the two models. Micro-averaged (MA) F1 score was used as the measuring indicator. The final determined model was named CCSHNet. RESULTS: : On the test set, CCSHNet achieved sensitivities of four classes of 95.61%, 96.25%, 98.30%, and 97.86%, respectively. The precision values of four classes were 97.32%, 96.42%, 96.99%, and 97.38%, respectively. The F1 scores of four classes were 96.46%, 96.33%, 97.64%, and 97.62%, respectively. The MA F1 score was 97.04%. In addition, CCSHNet outperformed 12 state-of-the-art COVID-19 detection methods. CONCLUSIONS: : CCSHNet is effective in detecting COVID-19 and other lung infectious diseases using first-line clinical imaging and can therefore assist radiologists in making accurate diagnoses based on CCTs.