As cyberattacks develop in volume and complexity, machine learning (ML) was extremely implemented for managing several cybersecurity attacks malicious performance. The cyber-physical systems (CPSs) combined the calculation with physical procedures. An embedded computer network monitor control procedure, commonly feedback loops whereas procedures affect calculations conversely, at same time, ML ...

A recent work introduced the concept of deep dictionary learning. The first level is a dictionary learning stage where the inputs are the training data and the outputs are the dictionary and learned coefficients. In subsequent levels of deep dictionary learning, the learned coefficients from the previous level acts as inputs. This is an unsupervised representation learning technique. In this wo...

We present a Transfer Learning (TL) framework based on Stacked Denoising Autoencoder (SDA) for the recognition of immunogold particles. These particles are part of a high-resolution method for the selective localization of biological molecules at the subcellular level only visible through Transmission Electron Microscopy (TEM). Four new datasets were acquired encompassing several thousands of i...

Collaborative Filtering (CF), a well-known approach in producing recommender systems, has achieved wide use and excellent performance not only in research but also in industry. However, problems related to cold start and data sparsity have caused CF to attract an increasing amount of attention in efforts to solve these problems. Traditional approaches adopt side information to extract effective...

Communication system mismatch represents a major influence for loss in speaker recognition performance. This paper considers a type of nonlinear communication system mismatchmodulation/demodulation (Mod/DeMod) carrier drift in single sideband (SSB) speech signals. We focus on the problem of estimating frequency offset in SSB speech in order to improve speaker verification performance of the dri...

Transfer Learning is a paradigm in machine learning to solve a target problem by reusing the learning with minor modifications from a different but related source problem. In this paper we propose a novel feature transference approach, especially when the source and the target problems are drawn from different distributions. We use deep neural networks to transfer either low or middle or higher...