Creating, placing, and characterizing social media comments and reactions is a challenging problem. This is particularly true for reddit.com, a highly trafficked social media website with thousands of posts per day. Each post has an associated comment thread, and users of Reddit can vote the comments up or down, generating a net score, or ”Karma,” for each comment. Users aspire to collect this ...

runoff estimation is one of the main challenges encountered in water and watershed management. spatial and temporal changes of factors which influence runoff due to het-erogeneity of the basins explain the complicacy of relations. artificial neural network (ann) is one of the intelligence techniques which is flexible and doesn’t call for any much physically complex processes. these networks can...

This paper investigates how behavior primitives are self-organized in a neural network model utilizing a distributed representation scheme. The model is characterized by so-called parametric biases which adaptively modulate the encoding of different behavior patterns in a single recurrent neural net (RNN). Our experiments, using a real robot arm, showed that a set of end-point and oscillatory b...

A long deep and wide artificial neural net (LDWNN) with multiple ensemble neural nets for individual frequency subbands is proposed for robust speech recognition in unknown noise. It is assumed that the effect of arbitrary additive noise on speech recognition can be approximated by white noise (or speech-shaped noise) of similar level across multiple frequency subbands. The ensemble neural nets...

This paper presents a powerful and flexible Digital Signal Processing (DSP) architecture based on the Texas Instruments TMS320VC33 DSP and high speed PCI bus. The DSP board provides a convenient, flexible means to test signal processing algorithms in real-time hardware. Algorithms implemented for several research projects include Normalized Least Mean Square (NLMS) Adaptive Filter, Recurrent Ne...

A major challenge in performing pattern recognition with neural networks is large input data sets; for example, high-resolution static images. There is a direct relationship between the number of inputs and the number of neurons and links required to process those inputs. Specifically, as the number of inputs increases linearly, the complexity of the neural net increases exponentially. We prese...

Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurre...

In this paper a time domain recursive digital filter model, based on recurrent neural network is proposed. This problem can be considered as a training procedure of two layer recurrent neural network. The proposed neural network training algorithm is based on determination of the sensitivity coefficients of the recurrent system. The dynamic model of two layer recurrent neural network described ...

Constrained optimization problems have a wide range of applications in science, economics, and engineering. In this paper, a neural network model is proposed to solve a class of nonsmooth constrained optimization problems with a nonsmooth convex objective function subject to nonlinear inequality and affine equality constraints. It is a one-layer non-penalty recurrent neural network based on the...