Matin Hosseini

• University of Louisiana at Lafayette

Video frame prediction is needed for various computer-vision-based systems such as self-driving vehicles and video streaming. This paper proposes a novel Inception-based convolutional recurrent neural network (RNN) as an enhancement to a basic gated convolutional RNN. A basic gated convolutional RNN has fixed-size kernels that are hyperparameters o...

The complexity metric is an effective tool to evaluate the behavioral dynamics in systems with high level of nonlinearity and interconnectivity. This paper aims to evaluate the effectiveness of using the entropic complexity as a feature to enhance situational awareness in dynamical systems. In fact, the complexity measurement aims to detect dynamic...

Bayesian predictive coding is a putative neuromorphic method for acquiring higher-level neural representations to account for sensory input. Although originating in the neuroscience community, there are also efforts in the machine learning community to study these models. This paper reviews some of the more well known models. Our review analyzes mo...

In this paper, we proposed a novel deep-learning method called Inception LSTM for video frame prediction. A standard convolutional LSTM uses a single size kernel for each of its gates. Having multiple kernel sizes within a single gate would provide a richer features that would otherwise not be possible with a single kernel. Our key idea is to intro...

This study is mainly focused on the identification of minor faults in power distribution systems utilizing four intelligent classifiers for pattern recognition. Dynamics discussed in this paper are not easily detectable by protection devices; however, they can affect the power quality adversely in the grid. A grid-connected wind farm is used as a c...

Variation in solar irradiance causes power generation fluctuations in solar power plants. Power grid operators need accurate irradiance forecasts to manage this variability. Many factors affect irradiance, including the time of year, weather and time of day. Cloud cover is one of the most important variables that affects solar power generation, but...

In this paper, we proposed a novel deep-learning method called Inception LSTM for video frame prediction. A standard convolutional LSTM uses a single size kernel for each of its gates. Having multiple kernel sizes within a single gate would provide a richer features that would otherwise not be possible with a single kernel. Our key idea is to intro...

The problem of video frame prediction has received much interest due to its relevance to many computer vision applications such as autonomous vehicles or robotics. Supervised methods for video frame prediction rely on labeled data, which may not always be available. In this paper, we provide a novel unsupervised deep-learning method called Inceptio...

A vision-based intelligent traffic control system is a robust framework that controls the traffic flow in real-time by estimating the traffic density near traffic lights. In this paper, a traffic light control system based on fuzzy Q-learning is proposed according to the vehicle density and the pedestrian number estimated from the visual informatio...

We present a novel adaptive feedforward neural network for online learning from doubly-streaming data, where both the data volume and feature space grow simultaneously. Traditional online learning and feature selection algorithms can’t handle this problem because they assume that the feature space of the data stream remains unchanged. We propose a...

In this paper, we present a hybrid of Incremental Learning radial basis function Neural Network, Gaussian Process classifier and AdaBoost for building a breast cancer survivability prediction model. Diagnosis of breast cancer is a difficult to accomplish due to its noisy data and relatively small database. We carried out our experiment on breast ca...

Traffic is an issue that many big cities are confronted with because of ever-increasing population growth. In this paper we propose a two phase traffic light control system based on fuzzy Q-learning for an isolated 4-way intersection. The states and actions of the Q-learning variables is set by a fuzzy algorithm which can be learned through environ...