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In this paper, a novel blind watermarking scheme based on the back-propagation neural networks (BPNN) for image is presented. First, the convolutional codes encoding is used to refine the watermark for increasing robustness of the scheme. BPNN is developed to memorize the relationships between the wavelet selected samples and a processed chaotic sequence. With wavelet domain of original image being...
The time series prediction model based on neural network can perfectly reflect the trend of development of nonlinear system, but the training speed for neural network is very slow, therefore, it is easily prone to local extremum. So we come up with a learning algorithm combining genetic algorithm and BP algorithm for the training of BP neural network, to realize optimization of network structure....
We consider the system-level self-diagnosis of multiprocessor and multicomputer systems under the generalized comparison model (GCM). In this diagnosis model, a set of tasks is assigned to pairs of nodes and their outcomes are compared by neighboring nodes. The collections of all comparison outcomes, agreements and disagreements among the nodes, are used to identify the set of faulty nodes. We consider...
Using microfossil-based transfer functions, domain scientists from the field of pale oceanography seek to reconstruct environmental conditions at various times in the past. This is accomplished by first determining a quantitative relationship between a forcing function, such as temperature, and the modern for aminiferal response using a calibration data set based on environmental data from an oceanographic...
Deep machine learning is an emerging framework for dealing with complex high-dimensionality data in a hierarchical fashion which draws some inspiration from biological sources. Despite the notable progress made in the field, there remains a need for an architecture that can represent temporal information with the same ease that spatial information is discovered. In this work, we present new results...
Structural machine learning method-covering algorithm (CA) possesses faster speed, lower complexity and higher precision. But construction of the weight of the neurons for new center of sphere domain is usually given a manmade criteria, could not follow the distribution of samples to achieve the optimal solution. In this paper, a new constructive algorithm which combines the cross covering algorithm...
In this paper, we analyze the potential of combining wireless sensor networks with artificial neural networks (ANNs) to build a "smart forest-fire early detection sensory system" (SFFEDSS). We outline our new SFFEDS system in which temperature, light and smoke data from low-cost sensor nodes spread out on the forest bed is aggregated into information. This information is spatially and temporally...
This article presents two classifiers based on machine learning methods, aiming to detect physiologic anomalies considering Poincaré plots of heart rate variability. It was developed a preprocessing procedure to encoding the plots, based on the Cellular Features Extraction Method. Simulation of different classifiers, artificial neural networks and support vector machine, has been performed and the...
Grid computing offers a service for sharing resources over uncertain and complex environments. In such multi-participated settings it is essential to make the grid middleware functionality transparent to members by providing the ability to act autonomous and learning from the environment. Parallel to grid, artificial neural networks is a paradigm for processing information, which is inspired by the...
It will increase identification accuracy when P2P traffic identification method based on flow feature is combined with machine learning methods. Recently, the most applied machine learning method is neural networks, but neural networks has insufficient generalization ability, this paper proposes an identification method based on BP neural network that use bayesian regularization to improve its generalization...
The prediction of asthma that persists throughout childhood and into adulthood, in early life of a child has practical, clinical and prognostic implications and sets the basis for the future prevention. Artificial Neural Networks (ANNs) seems to be a superior tool for analyzing data sets where nonlinear relationships are existing between the input data and the predicted output. This study presents...
One of the major issues concerning the Artificial Neural Networks (ANNs) design is a proper adjustment of the weights of the network. There have been a number of studies comparing the performance of evolutionary and gradient based ANNs learning. But the results of the studies, sometime conflicting to each other although the same and standard dataset development had been used. Motivated by this finding,...
We propose a classification model for the cognitive level of question items in examinations based on Bloom's taxonomy. The model implements the artificial neural network approach, which is trained using the scaled conjugate gradient learning algorithm. Several data preprocessing techniques such as word extraction, stop word removal, stemming, and vector representation are applied to a feature set...
Computer games are attracting increasing interest in the Artificial Intelligence (AI) research community, mainly because games involve reasoning, planning and learning. One area of particular interest in the last years is the creation of adaptive game AI. Adaptive game AI is the implementation of AI in computer games that holds the ability to adapt to changing circumstances, i.e., to exhibit adaptive...
Although traditional techniques of machine learning have, in many cases, presented good results, they have been inefficient for data which are constantly expanding and changing over time. To address these problems, new learning techniques have been proposed in the literature. In this paper we propose a technique called ePNN presenting aspects of this recent paradigm of learning. We carried out a series...
By exploiting the properties of superposition and entanglement found in quantum systems Quantum Computation has been applied to the design of algorithms considerably more efficient than the known classical ones. Known examples are the Shor's factoring algorithm and the Grover's search algorithm. This paper investigates the possibility of employing Quantum Computing techniques to the design of learning...
Two popular hazards in supervised learning of neural networks are local minima and over fitting. Application of the momentum technique dealing with the local optima has proved efficient but it is vulnerable to over fitting. In contrast, deployment of the early stopping technique might overcome the over fitting phenomena but it sometimes terminates into the local minima. This paper proposes a hybrid...
Artificial intelligence technologies have been applied in a number of systems to achieve learning and intelligent behavior. In this paper an artificial neural network is used to implement multimode authentication through information fusion. An information fusion model uses metrics computed from the identity attributes using Shannon's information theory. Initialisation of the artificial neural network...
The importance of providing guaranteed Quality of Service (QoS) cannot be overemphasised, especially in the NGN environment which supports converged services on a common IP transport network. Call Admission Control (CAC) mechanisms do provide QoS to class-based services in a proactive manner. However, due to the factors of complexity, scale and dynamicity of NGN, Machine Learning techniques are favoured...
A fault diagnosis method of probabilistic neural network was presented for turbine generator unit. the probabilistic neural network is based on probability statistics theory and Bayes classification rule, so it can efficiently identify and diagnose the fault of turbine generator unit. Theoretical analysis, practical procedure of neural network setting and training are given out. The simulation results...
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