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study features hybrid techniques with hybrid features to detect DR disease and determine its stage development. Saxena et al. [7] presented a deep learning network based on machine learning methods ...
convergence factor is: (12) Where x represents the current number of iterations , and xmax is the maximum number of iterations . In Eq (12), sine and cosine functions control the algorithm’s fluctuation during ...
the labels provided by the classifier. The learning process is iterative , until the obtained results are satisfactory. The goal is to minimize specialist involvement without losing control over the classifier ...
). To improve the accuracy and speed of the classification process, two techniques based on signatures [11, 18] and deep learning are employed to classify suspicious packets executed at the controller plane ...
is combined with 3DHT to teach some basic boxing skills. In contrast, the control group is taught a program based on a teacher command style. Pretest-posttest designs were made for the two groups ...
]. The combination of fuzz testing and machine learning techniques provides a new idea to alleviate the bottleneck problem of traditional fuzzing techniques , which also makes the fuzz testing process intelligent ...
abruptly at unpredictable times, for instance, during anesthesia-induced burst-suppression, different stages of sleep, and modulations from active learning and behavioral responses [22–30]. Time-varying ...
of machine learning [33]. When applied to real -world issues that reflect the natural selection method, these methods often provide more specific solutions. The first stage of their suggested three-phase ...
as a data augmentation technique . Both inputs and outputs were normalized to [−1, 1]. The deep architectures were trained for 100000 iterations with a learning rate starting at 1 × 10−4 and cosine learning ...
region for different iterations . Afterwards, the main optimizer MPA goes through several stages to meet the power demand by enforcing the lower and upper boundaries restriction of each controlling ...
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