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Outlier detection is a method to improve performances of machine learning models. In this paper, we use an outlier detection method to improve the performance of our proposed algorithm called decision boundary making (DBM). The primary objective of DBM algorithm is to induce compact and high performance machine learning models. To obtain this model, the DBM reconstructs the performance of support...
The information about the agricultural activities being performed on the farm is useful for providing agriculture advisory to the farmers. In this paper, we present the Neural Network based approach for the classification of agriculture activities like harvesting, bed-making, transplantation, walking and standstill from the acceleration data obtained from mobile phone carried by the farmer. The performance...
The brain-inspired neural networks have demonstrated great potential in big data analysis. The spiking neural network (SNN), which encodes the real world data into spike trains, promises great performance in computational ability and energy efficiency. Moreover, it is much more biologically plausible than the traditional artificial neural network (ANN), which keeps the input data in its original form...
Research community has recently put more attention to the Extreme Learning Machines (ELMs) algorithm in Neural Network (NN) area. The ELMs are much faster than the traditional gradient-descent-based learning algorithms due to its analytical determination of output weights with the random choice of input weights and hidden layer bias. However, since the input weights and bias are randomly assigned...
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...
In this paper, a novel classification algorithm, ELMDF (Extreme Learning Machine based on Data Field), is proposed to solve the problem of estimating the number of hidden layer neurons in typical ELM. For constructing ELMDF, a new theory based on data field, FMDF (Fundamental Matrix of Data Field) is proposed in this paper. The breast cancer cell image dataset, and the genome dataset are used to test...
This paper describes the methodology for implementation of artificial neural networks with adaptable parameters (weights, connections, number of neurons) on fixed-point embedded systems. Components of neuron unit and interconnecting matrix are discussed. Particular example of implementation on PIC18F46K80 is given. Results are discussed in appropriate part.
Parkinson's disease (PD) is a chronic neurological progressive disorder caused by lack of the chemical dopamine in the brain. Up to today, there is still no cure or prevention for PD, and usually the disease worsens gradually over time. However, this disease can be controlled with some treatment, especially in the early stage. Hence, this study proposes a method in early detection and diagnosis of...
A Distributed Autonomous Neuro-Gen Learning Engine (DANGLE) is proposed in this paper for file type identification. DANGLE is a machine learning tool designed to solve limitations of existing implementation of neural networks, namely excessive training time, fixed architecture and catastrophic forgetting. DANGLE consists of a Gene Regulatory Engine (GRE) and a Distributed Adaptive Neural Network (DANN)...
This paper presents an approach to digit recognition using single layer neural network classifier with Principal Component Analysis (PCA). The handwritten digit recognition is an important area of research as there are so many applications which are using handwritten recognition and it can also be applied to new application. There are many algorithms applied to this computer vision problem and many...
The predicting daily network traffic usage is a very important issue in the service activities of the university. This paper present techniques based on the development of backpropagation (BP) and radial basis function (RBF) neural network models, for modelling and predicting the daily network traffic at Universitas Mulawarman, East Kalimantan, Indonesia. The experiment results indicate that a strong...
A neural network task scheduler (NNTS) is proposed for the congestion-minimized network-on-chip in multi-core systems. The NNTS is composed of a near-optimal task assignment (NOTA) algorithm and a reconfigurable precision neural network accelerator (RP-NNA). The NOTA adopting a neural network is proposed to predict and avoid the network congestion intelligently. And the RP-NNA is implemented to improve...
Surgical removal of bladder, i.e. radical cystectomy, is a standard treatment option for muscle invasive bladder cancer. Unfortunately, the treatment is associated with significant morbidities and mortalities. Many studies have been conducted to predict the morbidities and mortalities of radical cystectomy based on statistical analysis. In this paper, an artificial neural network is employed to predict...
Data classification is an important branch of data mining and there are different methods for its implementation. Neural networks are one of the best ways for classification in machine learning. Structure and weights of neural network are most important in their precision. In recent years, due to the defects in gradient-based search algorithms in neural network training algorithms, metahuristic algorithms...
Civil structures are known for having a non-linear and time-variant behavior, these features make a challenging task the use of linear methods for modeling the dynamical behavior since they only model time-invariant systems. To overcome this limitation, several approaches based on non-parametric methods have been proposed, however, the selection of the best-suited method for a particular case can...
Incremental functional diagnosis is the process of iteratively selecting a test, executing it and based on the collected outcome deciding either to execute one more test or to stop the process since a faulty candidate component can be identified. The aim is to minimise the cost and the duration of the diagnosis process. In this paper we compare six engines based on machine learning techniques for...
Sensory information, such as the tactile or proprioceptive signals, helps motor brain-machine interface (mBMI) work more naturally. Before applying sensory feedback, we need to explore if the neural activities are discriminative to different stimuli during a BMI task. Previous studies on the cortical discrimination are mainly focused on the rat whisker system. In this paper, we design a BMI task,...
This paper introduces a novel dynamic neural network model which can recognize dynamic visual image patterns of human actions based on learning. The proposed model is characterized by its capability of extracting the spatio-temporal feature hierarchy latent in the training visual image streams. The model achieves this property by integrating two essential ideas: (1) multiple spatial-scales processing...
The paper proposes a destructive method for optimizing the topology of neural networks based on the Shapley value, a game theoretic solution concept which estimates the contribution of each network element to the overall performance. More network elements can be simultaneously pruned, which can lead to shorter execution times and better results. An evolutionary hill climbing procedure is used to fine-tune...
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