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The research and studies of encryption and compression of digital files to the confidentiality and ease of dealing with networks and the Internet, by reducing the size of files transmitted through multimedia, and also us...

Digital in-line holographic microscopy (DIHM) is a widely used interference technique for real-time reconstruction of living cells’ morphological information with large space-bandwidth product and compact setup. Howeve...

Media sosial sebagai media informasi dan komunikasi mulai berkembang pesat sejak internet mudah diakses. Orang dengan mudah menyatakan pendapat, ekspresi, opini, dan informasi melalui tulisan pada media sosial. Opini ata...

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are t...

Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of gr...

Materias / palabras clave: Artificial intelligence; Deep learning; Computer science; Artificial neural network; Backpropagation; Reinforcement learning; Machine learning; Deep neural networks; Unsupervised learning; Recurrent neural network; Encod...

Images compression is a widely studied topic. Conventional situations offer variable compression ratios depending on the image in question and, in general, do not yield good results for images that are rich in tones. Thi...

The proposal of this research line is the search for alternatives to the resolution of complex problems where human knowledge should be apprehended in a general fashion. In particular, the activities developed so far can...

In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up ...

Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a sh...

We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 e...

Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where th...

We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two...

Fast response, storage efficiency, fault tolerance and graceful degradation in face of scarce or spurious inputs make neural networks appropiate tools for Intelligent Computer Systems. But on the other hand, learning alg...

It is observed that conventional techniques to analyse the steady state analysis of Self-Excited Induction Generator (SEIG) involve cumbersome mathematical procedures. In this paper an Artificial Intelligence (AI) techni...

This paper proposes a model that predicts the complexity of Boolean functions with only XOR/XNOR min-terms using back propagation neural networks (BPNNs) applied to Binary Decision Diagrams (BDDs). The BPNN model (BPNNM)...

The exponential periodicity and stability of continuous nonlinear neural networks with variable coefficients and distributed delays are investigated via employing Young inequality technique and Lyapunov method. Some new...

Deep learning offers a novel opportunity to achieve both high-quality and high-speed computer-generated holography (CGH). Current data-driven deep learning algorithms face the challenge that the labeled training datasets...

This work presents a neuro-adaptive control method in sliding mode control designed in discrete time for SCARA robot arm. The proposed control structure is based on neuro-adaptive sliding mode control to adjust dynamics ...

The rapid growth and increasing complexity of Internet of Things (IoT) networks require efficient real-time monitoring and anomaly detection mechanisms. Traditional machine learning approaches often struggle to handle th...

The prediction of suspended sediment concentration (SSC) is of critical importance for the management of aquatic ecosystems, flood control, and erosion assessments. However, the continuous measurement of SSC is technical...

This paper addresses the challenge of packet-based information routing in large-scale communication networks. The problem is framed as a constrained statistical learning task, where each network node operates using only ...

Background Walking impairments post-stroke are common but studying their neural basis with fMRI is difficult due to motion constraints. This study used post-task resting-state residual activity (PTRRA) to investigate wal...

Abstract Echo State Networks are recurrent neural networks that leverage a random reservoir’s dynamics, training only a simple readout layer. This approach is computationally efficient but limits network performance. B...