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Robustness of Fuzzy Modeling

Martha G Smons (Marthasimons) on March 9, 2021
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Robustness of Fuzzy Modeling and Its Applications in Clustering and Classification Problems This paper investigates the use of deep learning for a classification task in a natural scene context. Deep Learning is a powerful data-driven approach to learning for scene analysis given the natural visual world as a whole. Although the deep learning algorithms used in this work are not fully-trained, it could be considered a natural data-driven approach to learning for this task given the natural visual world and the natural objects themselves in the context of the scene. In this paper, we present a novel framework for applying the deep learning method to natural scenes for natural object detection. The proposed method is designed to solve for the problem of natural object detection. Extensive experimental study on real images from the field show that the proposed method is a promising approach for object detection in real real-world environments.

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Video In HV range prediction from the scientific literature We present a novel method for learning supervised learning problems based on an adversarial learning algorithm. The adversarial technique is motivated by the fact that it is the least-squares model used in practice. The approach exploits the adversarial learning principle to minimize the influence of its adversarial input and to reduce the adversarial output to a set of small, weighted minimizers. The objective is to minimize the total variance of the squared loss function over adversarial input and minimizes the adversarial output. We apply our learned adversarial algorithm to various supervised learning tasks, including classification, clustering, and classification with a single pass of the training images. Our results show that the proposed approach provides a simple yet effective learning technique to improve both prediction accuracy and performance. Using this approach, we found that the proposed approach significantly outperforms competing methods on three datasets.

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Predicting the Treatment of Medulloblastoma Patients Based on Functional Connectomes Using Deep Convolutional Neural Network The research on the potential use of deep learning for medical machine translation (MT) has focused on identifying the source of textural patterns in human speech. In this work we study the effect of MT on the transcription of the patient-related speech in response to a question posed by the human in the context of a medical evaluation. To this end, we used a recurrent neural network to learn the structure and dynamics of a patient's speech with a high-quality corpus. We investigated the effect of MT on the translation process of the translated speech and the ability of the human-AI community to generate appropriate speech patterns for translation. On the basis of the results presented we conducted experiments to investigate the effect of MT and its effects on translation performance. The results indicate that MT's effects also extend to the training stage.

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