Analyzing the Impact of Feature Correlation on Classification Acuracy of Machine Learning Model

Mishra, Satabdi; Pradhan, Ranjan K.

doi:10.1109/aisc56616.2023.10085293

Cited by 3 publications

(1 citation statement)

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“…There are several methods for replacing faces in video, including HRNet, RetinaFace, Dlib, Medi-aPipe, KeyPoint R-CNN, and OpenCV. Choosing the best method for a particular project depends on specific requirements and constraints, such as accuracy, speed, and computational requirements [18]. With the right approach and tools, you can achieve accurate and efficient face substitution in videos using Python or C++ languages.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient face detection and replacement in the creationofsimple fake videos

Oleksii I.,

Oleksandr V.,

Denys V.

et al. 2023

AAIT

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Face detection and facial recognition technologies are among the most intensively studied topics within the field of computervision, owing to their vast application potential across a multitude of industries. These technologies have demonstrated practicalap-plicability in varied contexts such as identifying suspicious individuals in crowded urban spaces, real-time recognition of smartphone owners, creating compelling deepfakes for entertainment applications, and specialized applications that modify the movements of facial features such as the lips or eyes. With the current state-of-the-art advancements in hardware and software technology, today's technological infrastructure provides more resources than are necessary for video streaming. As a result, simpleface recognition systems can be implemented without the need for high-cost server instances that require specified pre-trained models. This abun-dance of resources is changing the landscape of face recognition, and the discussion within this paper will revolve around these emerging paradigms.The primary focus of this article is an in-depth analysis of the key concepts of face detection in streaming video data using prominent pre-trained models. The models under discussion include HRNet, RetinaFace, Dlib, MediaPipe, and KeyPoint R-CNN. Each of these models has its strengths and weaknesses, and the article discusses these attributes in the context of real-world case studies. This discussion provides valuable insights into the practical applications of these models and the trade-offs involved in their utilization.Moreover, this paper presents a comprehensive overview of image transformation techniques. It introduces an ab-stract method for affine image transformation, animportanttechnique in image processing that changes the geometric properties of an image without affecting its pixel intensity. Additionally, the article discusses image transformation operations executed through the OpenCV library, one of the leading libraries in the field of computer vision, providing a highly flexible and efficient toolset for image manipulation.The culmination of this research is presented as a practical standalone system for image replacement in video. This system leverages the RetinaFace model for inference and employs OpenCV for affine transformations, demonstrating the con-cepts and technologies discussed in the paper. The work outlined in this article thereby advances the field of face detectionand recognition, presenting an innovative approach that makes full use of contemporary hardware and software advances

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Section: Literature Reviewmentioning

confidence: 99%

Efficient face detection and replacement in the creationofsimple fake videos

Oleksii I.,

Oleksandr V.,

Denys V.

et al. 2023

AAIT

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Comparative analysis of feature selection techniques for COVID-19 dataset

Mohtasham,

Pourhoseingholi,

Hashemi Nazari

et al. 2024

Sci Rep

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In the context of early disease detection, machine learning (ML) has emerged as a vital tool. Feature selection (FS) algorithms play a crucial role in ensuring the accuracy of predictive models by identifying the most influential variables. This study, focusing on a retrospective cohort of 4778 COVID-19 patients from Iran, explores the performance of various FS methods, including filter, embedded, and hybrid approaches, in predicting mortality outcomes. The researchers leveraged 115 routine clinical, laboratory, and demographic features and employed 13 ML models to assess the effectiveness of these FS methods based on classification accuracy, predictive accuracy, and statistical tests. The results indicate that a Hybrid Boruta-VI model combined with the Random Forest algorithm demonstrated superior performance, achieving an accuracy of 0.89, an F1 score of 0.76, and an AUC value of 0.95 on test data. Key variables identified as important predictors of adverse outcomes include age, oxygen saturation levels, albumin levels, neutrophil counts, platelet levels, and markers of kidney function. These findings highlight the potential of advanced FS techniques and ML models in enhancing early disease detection and informing clinical decision-making.

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