Titlebook: Cognitive Computing and Cyber Physical Systems; 4th EAI Internationa Prakash Pareek,Nishu Gupta,M. J. C. S. Reis Conference proceedings 202

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https://doi.org/10.1007/978-3-031-43744-1 discriminative features for classification. Next, it employs variants of ensemble machine learning techniques to perform multi-class classification. We utilize popular ensemble methods such as Boosted Trees, Bagged Trees, RUSBoosted Trees and Optimized Ensemble to improve the accuracy and optimizat

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https://doi.org/10.1007/978-3-031-43807-3ion metrics. The findings highlight the effectiveness of cardiovascular disease ensemble DL models prediction, showcasing their potential for enhancing diagnostic accuracy in clinical settings and aiding healthcare professionals in making informed decisions for patient care.

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https://doi.org/10.1007/978-3-031-43759-5chronic hemolytic anemia, and capillary blockage causes tissue hypoxia and subsequent organ damage. So, it is important to monitor patients suffering from sickle cells..Here we have used machine learning to visualize those patients and categorize them according to their hemoglobin level, percentage

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Selected Studies in Indonesian Archaeologyen enhanced flower pollination allocate the optimized resources for the demanded request. The comparison of the proposed model is done with simulation results of MOROT and neural network model and also implemented on GoCS real dataset of google. The proposed model gives better results when compared

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,The God with the Horse’s Head,ax and micro-blogging sites such as Twitter. We can use these kinds of datasets to provides the aspect level sentiment analysis. Therefore, we have explored, in this article, a language model built upon a pretrained deep neural networks capable of analyzing the sequence of text to classify it as hav