ABSTRACT Introduction: The continuous improvement of the level modern sports competition compels reform and constant updates on training theory; research is migrating from low-intensity to high-intensity interval, many studies have shown that interval has a good effect athletes. Objective: Investigate different recovery modalities during athletic performance young male runners. Methods: Using b...

In recent years, machine learning algorithms, or rather deep have beenwidely used in many fields, including cybersecurity. However, systems are vulnerableto attacks by attackers, and this limits the use of learning, especially nonstationaryenvironments with hostile actions, such as cybersecurity field, where real attackersexist (for example, malware developers). With rapid development artificia...

We propose a hybrid coded modulation scheme which composes of inner and outer codes. The outer-code can be any standard binary linear code with efficient soft decoding capability (e.g. low-density parity-check (LDPC) codes). is designed using deep neural network (DNN) takes the channel bits outputs modulated symbols. For training DNN, we to use loss function that inspired by generalized mutual ...

Training of artificial neural networks is normally a time consuming task due to iterative search imposed by the implicit nonlinearity of the network behavior. To tackle the supervised learning of multilayer feed forward neural networks, the backpropagation algorithm has been proven to be one of the most successful neural network algorithm. Although backpropagation training has proved to be effi...

One area of pediatric care that strongly presents an opportunity for anticipatory guidance and clinical intervention is that of toilet training. Because most toilet training problems presenting to the health care practitioner reflect inappropriate training efforts and parental pressure, providers can, by consulting with parents, elucidate and address misconceptions parents have about the toilet...

We propose a technique for a training set approximation and its usage in kernel methods. The approach aims to represent data in a low dimensional space with possibly minimal representation error which is similar to the Principal Component Analysis (PCA). In contrast to the PCA, the basis vectors of the low dimensional space used for data representation are properly selected vectors from the tra...