Feature selection analysis for multimedia event detection

Rajput, Dharmendra Singh; Singh, Aditya; Agarwal, Yukta; Maddikunta, Praveen Kumar Reddy; Reddy, G. Thippa

doi:10.1088/1757-899x/263/4/042004

Cited by 2 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The major contribution of this work is the extraction of important features from the COVID-19 fake news dataset. Feature extraction plays a very important role in text processing as it reduces the dimension of feature space by considering only the important features (27)(28)(29). To extract the features, the named-entity recognition (NER) approach is used in our work.…”

Section: Feature Extractionmentioning

confidence: 99%

Detecting COVID-19-Related Fake News Using Feature Extraction

Khan

Hakak

Deepa

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

Since its emergence in December 2019, there have been numerous posts and news regarding the COVID-19 pandemic in social media, traditional print, and electronic media. These sources have information from both trusted and non-trusted medical sources. Furthermore, the news from these media are spread rapidly. Spreading a piece of deceptive information may lead to anxiety, unwanted exposure to medical remedies, tricks for digital marketing, and may lead to deadly factors. Therefore, a model for detecting fake news from the news pool is essential. In this work, the dataset which is a fusion of news related to COVID-19 that has been sourced from data from several social media and news sources is used for classification. In the first step, preprocessing is performed on the dataset to remove unwanted text, then tokenization is carried out to extract the tokens from the raw text data collected from various sources. Later, feature selection is performed to avoid the computational overhead incurred in processing all the features in the dataset. The linguistic and sentiment features are extracted for further processing. Finally, several state-of-the-art machine learning algorithms are trained to classify the COVID-19-related dataset. These algorithms are then evaluated using various metrics. The results show that the random forest classifier outperforms the other classifiers with an accuracy of 88.50%.

show abstract

Section: Feature Extractionmentioning

confidence: 99%

Detecting COVID-19-Related Fake News Using Feature Extraction

Khan

Hakak

Deepa

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Researchers studied patterns at different scales, including local features like patterns on corners or intersections and long-range patterns over objects. Regions serve as the fundamental building blocks for a variety of vision applications [3][4][5][6]. For example, due to its robustness against image changes, a local region detector is critical for image matching and object recognition.…”

Section: Introductionmentioning

confidence: 99%

Multimedia Concepts on Object Detection and Recognition with F1 Car Simulation Using Convolutional Layers

Amutha

Ramana²,

Dhiman³

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

This paper presents a framework for detecting objects in images based on global features and contours. The first step is a shape matching algorithm that uses the background subtraction process. Object detection is accomplished by an examination of the oversegmentation of the image, where the space of the potential boundary of the object is examined to identify boundaries that have a direct resemblance to the prototype of the object type to be detected. Our analysis method removes edges using bilinear interpolation and reestablishes color sensors as lines and retracts background lines from the previous frame. Object contours are generated with clustered lines. The objects detected will then be recognized using the extraction technique. Here, we analyze the color and shape characteristics with which each object is capable of managing occlusion and interference. As an extension of object detection and recognition, F1 car simulation is experimented with simulation using various layers, such as layer drops, convolutionary layers, and boundary elimination, avoiding obstacles in different pathways.

show abstract

Feature selection analysis for multimedia event detection

Cited by 2 publications

References 5 publications

Detecting COVID-19-Related Fake News Using Feature Extraction

Detecting COVID-19-Related Fake News Using Feature Extraction

Multimedia Concepts on Object Detection and Recognition with F1 Car Simulation Using Convolutional Layers

Contact Info

Product

Resources

About