Titlebook: Enabling Machine Learning Applications in Data Science; Proceedings of Arab Aboul Ella Hassanien,Ashraf Darwish,Dabiah Ahmed A Conference

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An Efficient Framework to Build Up Heart Sounds and Murmurs Datasets Used for Automatic Cardiovasculn this paper, we propose a framework for creating and collecting heart sounds and murmurs datasets. In this dataset, we aim to achieve multiple objectives such as of data balancing and data quality, along with a proper logical structure of as many samples of multiple classes of heart diseases and mu

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Facial Recognition and Emotional Expressions Over Video Conferencing Based on Web Real Time Communiclications and platforms based on WebRTC technology helps in establishing a peer to peer communication and streaming,?transmitting?and?receiving video, audio and data in real-time. In other hand, TensorFlow.js is opensource models in JavaScript which applied the concept of machine learning. Both WebR

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Recent Advances in Intelligent Imaging Systems for Early Prediction of Colorectal Cancer: A Perspectinical practice. This chapter is an attempt to highlight the importance of intelligent medical imaging systems for decision making and management of the disease. A detailed overview of various insights of CRC is described in this chapter. We have also given a detailed analysis of various intelligent

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Optimum Voltage Sag Compensation Strategies Using DVR Series with the Critical Loads (DVR), where the use of a DVR is the best way to compensate for the voltage near the loads during voltage sag to save the electrical source in the healthy operation. The DVR is designed to be connected in series with a power system to protect the sensitive load from voltage sag or swell. This study

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Robust Clustering Based Possibilistic Type-2 Fuzzy C-means for Noisy Datasets feature selection and the improved possibilistic clustering, the robustness against uncertain datasets is provided due to the use of Type-2 Fuzzy C-means, in addition, ensemble clustering provides better clustering quality. The comparison of the proposed clustering method with similar clustering al

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