Titlebook: Data Engineering and Applications; Proceedings of the I Jitendra Agrawal,Rajesh K. Shukla,Chin-Shiuh Shieh Conference proceedings 2024 The

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https://doi.org/10.1007/978-1-4302-0755-9he alphabet in American Sign Language (ASL) using a marker-less vision sensor. Using photos obtained from the color images, this model presents a novel user-independent identification approach for the American Sign Language alphabet. Convolutional neural network designs are utilized for feature lear

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alarming rate, and it is critical and significant to predict these diseases in advance. A major challenge in clinical data research is predicting cardiovascular disease. At this point in time, one person dies from heart disease per minute. As a result, early identification of CHD can aid in the red

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nhance the classification accuracy. The swarm intelligence algorithm provides superior optimal solution through distributive search over complete search in the feature space. Nevertheless, the SSA algorithm falls with local optima and poor convergence rate. In this paper, the levy distribution-based

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able to edit photographs and modify their attributes. Copy and move forgery is the most prevalent sort of picture counterfeiting. An approach based on machine learning is proposed in this work to identify copy–move picture forgery. The suggested technique detects and localizes copy–move patches. Th

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al disclosure methods are typically not very effective because they do not understand the two groups in any communication having particular words as hateful as a speech. In this proposal, large hate speech vocabularies were developed to collect tweets that contained terms of hate speech. A dimension

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all over the world. Machine learning and deep learning techniques are playing an important role in medical diagnosis. Several machine learning and deep learning techniques are proposed for the prediction of SARS-COVID-19. The analysis of SARS-COVID is performed based on the lung images. These lung

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