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Herein we consider the comparison of two neural networks: the Extreme Learning Machine (ELM) and the Fast Support Vector Classifier (FSVC, also known as RBF-M). Classification tasks are considered showing that FSVC has similar performance to ELM while having the advantage of a unique radius and of a precise result (no randomness is here involved)
This paper presents a real-time spiking neural network adaptation of the HMAX object recognition model on an event-driven platform. Visual input is provided by a spiking silicon retina, while the SpiNNaker system is used as a computational hardware platform for implementation. We show the implementation of a simple Leaky Integrate-and-Fire (LIF) neuron model on SpiNNaker to create an event driven...
Frequency restoration reserve capacity is traditionally dimensioned with the help of deterministic criteria or by using probabilistic approaches that determine the capacity for a long period (several months). These static approaches work out quite well with traditional power systems. But increasing shares of intermittent generation introduce higher volatility to today's and future power systems which...
The unsupervised learning of Self Organizing Map (SOM) is an effective computational tool in data mining exploration processes. It provides topology preserved data mapping from high-dimensional input space into low-dimensional representation such as two-dimensional map. The visualization and classification of clustered data even with good topological preservation between input and output spaces however...
Maturity detection is very important for fruit farmhouses. In a previous study, we revealed a type of odor sensor that responds to the strength of the fruits smell as well as to the fruits maturities. The smell data consists of a dead time and a step response of a first-order lag element. We focus on the step response of first-order lag element, which is a form that rises exponentially to a constant...
Processing thousands of applications can be a challenging task, especially when the applicant does not consider the university requirements and their qualification, while in some cases, the selection officer may face difficulties in deciding if more than one candidate has the same qualification for a limited vacancy of a particular program. In this paper, we present an investigation on university...
Attention Deficit Hyperactivity Disorder (ADHD) is one of the widely researched neuro-developmental disorder. This paper highlights the importance of phenotypic information in the diagnosis of ADHD, in addition to Magnetic Resonance Image (MRI) based features. In this study, features from amygdala region of the brain is extracted using region of interest based feature extraction technique. These features...
The paper presents a Meta-cognitive Fully Complex-valued Fast Learning Predictor (Mc-FCFLP) for solving real-valued prediction problems. Mc-FCFLP has two components namely, a cognitive component and a meta-cognitive one. The meta-cognitive component of Mc-FCFLP consist of a self-regulatory learning mechanism which fixes what-to-learn, when-to-learn and how-to-learn. As the training samples are provided...
Voting based Extreme learning machine was recently proposed to reduce the error due to variance in Extreme Learning Machine. This paper further refines the algorithm by using entropy based ensemble pruning. Results obtained shows significant improvement in performance along with reduction in computational and storage requirement.
The self-organizing map (SOM) is a popular neural network which was designed for solving problems that involve tasks such as clustering and visualization. Specially, It provides a new strategy of clustering using a competition and co-operation principal. The probabilistic Kohonen network (PRSOM) is the stochastic version of classical one. However, determination of the optimal number of neurons and...
The automatic extraction of road networks is an interesting and challenging task. In spite of significant research efforts this problem remains largely open. In our work we attempt to leverage context at two different levels to extract accurate and topologically correct road networks. Local context, in the form of powerful features extracted from large neighborhoods, exploits the layout of road pixels...
In this paper we investigate the problem of automatic classification of structural MRI images, to distinguish between schizophrenia patients and healthy controls. Our methodology involves usage of a meta-cognitive neural network architecture that addresses classification issues inspired by learning strategies of cognition in the human brain. Due to heterogeneity in schizophrenia patient population...
In 2010, Global Status Report on NCD World Health Organization (WHO) reported that 60 percent of deaths in the world caused by the non-communicable diseases, and one of the non-communicable diseases that consumed a lot of attention was diabetes mellitus. Diabetes is a serious threat to the health development, because diabetes is a disease that caused most other diseases (complications), such as blindness,...
In this work, we have developed a classification technique to characterize the seafloor of the Gaveshani (coralline) bank area using multi-beam backscatter data. Soft-computational techniques like the artificial neural networks (ANNs) based unsupervised self-organizing maps (SOM) architecture is used to determine the existence of six classes. Thereafter, 55 segments were identified for data segmentation,...
In this paper, we examine the effectiveness of SOM knowledge to train multi-layered neural networks. We have known that the SOM can produce very rich knowledge, used for visualization and class structure interpretation. It is expected that this SOM knowledge can be used for many different purposes in addition to visualization and interpretation. By using more flexible information-theoretic SOM, we...
This paper presents the prediction of hourly Total Electron Content (TEC) values of the ionosphere using neural network by utilizing the TEC data from a GPS Ionospheric Scintillation and TEC Monitor (GISTM) receiver. There are two network configurations; the first one by using data from January–November 2005 in training and December 2005 in testing, and the second one involved data from January–October...
In this paper, human motion classification using multilayered neural network is proposed to classify motion signal based on vertical ground resultant force (VGRF). VRGF readings were acquired using an instrumented treadmill. The work presented in this paper seeks to classify six activities i.e. standing to walking, walking, walking to jogging, jogging, jogging to running and running, based on the...
The increasing use of solar power as a source of electricity has introduced various challenges to the grid operator due to the high PV power variability. The energy management systems in electric utility control centers make several decisions at different time scales. In this paper, power output predictions of a large photovoltaic (PV) plant at eight different time instances, ranging from few seconds...
In this paper a novel quantum based binary neural network learning algorithm is proposed. It forms three layer network structure. The proposed method make use of quantum concept for updating and finalizing weights of the neurons and it works for two class problem. The use of quantum concept form an optimized network structure. Also performance in terms of number of neurons and classification accuracy...
With introduction of online transaction the fraudulent activities through World Wide Web have increased rapidly. It's not only affecting common people but also making them lose huge amount of money. Online transaction basically takes place between merchant and customer, and in this case neither customer nor the card needs to be present at the time of transaction so merchant does not know that whether...
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