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Epileptic seizure source identification involves neurologists combing through a substantial amount of data manually, which sometimes takes weeks per patient. This paper presents a methodology for minimizing the amount of data a neurologist has to analyze to identify the seizure focus. The method keeps the neurologist as the final decision maker and aids in the decision making process. It has to be...
Scaling CMOS integrated circuit technology leads to decrease the chip price and increase processing performance in complex applications with re-configurability. Thus, VLSI architecture is a promising candidate in implementing neural network models nowadays. Backpropagation algorithm is used for training multilayer perceptron with high degree of parallel processing. Parallel computing implementation...
The paper proposes a hybrid classifier model for Dynamic Stability Prediction in Power System (DSPPS). The Hybrid Classifier Model (HCM) is included multiple parallel classifiers. Data for dynamic stable state in power system are very large, complex, and non-linear. These lead to making difficult for a single classifier to learn boundaries of classes. While previous works mainly concentrate on a single...
In this paper, we put forward deep neural network ensemble to model and predict Chinese stock market index (including Shanghai composite index and SZSE component index), based on the input indices of recent days. A set of component networks are trained by historical data for this task, where Backpropagation and Adam algorithm are used to train each network efficiently. Bagging approach combines these...
Aiming at the problem that the key water quality parameters in wastewater treatment processing is difficult to detect real-time accurately. An ammonia nitrogen concentration soft measure model based on the artificial neural network(ANN) is proposed in this paper, and utilizing existing data to achieve parameters detection in real-time accurately during the process of wastewater treatment processing...
The detection and elimination principle of redundant elements in the mathematical model is proposed in this paper. The efficiency of the proposed approach has been analyzed based on the neural network model of economic system and differential equations model. Results prove the 16 times increasing in the model accuracy.
An ultra-low power neural spike sorting technique for implantable, multi-channel neural implant is proposed. It involves spiking neural network (SNN) with binary weights as an energy and area efficient classifier, along with a suitable frontend for spike encoding of the recorded neuro-potential. The proposed scheme employs two step training to implement supervised learning for the classifier, in order...
Following a number of studies that have interrogated the usability of an autoencoder neural network in various classification and regression approximation problems, this manuscript focuses on its usability in water demand predictive modelling, with the Gauteng Province of the Republic of South Africa being chosen as a case study. Water demand predictive modelling is a regression approximation problem...
Recurrent neural network(RNN) has been widely applied to many sequential tagging tasks such as natural language process(NLP), and it has been proved that RNN works well in those areas. In this paper, we propose to use RNN with long short-term memory(LSTM) units for web server performance prediction. Classical methods focus on building relation between performance and time domain, which can't capture...
Analog resistive memories promise to reduce the energy of neural networks by orders of magnitude. However, the write variability and write nonlinearity of current devices prevent neural networks from training to high accuracy. We present a novel periodic carry method that uses a positional number system to overcome this while maintaining the benefit of parallel analog matrix operations. We demonstrate...
Diabetes is one of the most common metabolic diseases and the statistics show that one in eleven adults has diabetes, but one in two adults with diabetes is undiagnosed, and in 2040 one in 10 adults will have diabetes. In this paper is proposed a hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classifying patients with diabetes based on data sets with diabetic patients (Pima Indians...
Chronic kidney failure (chronic kidney disease ‘CKD’) is a serious disease that related to the gradual loss of kidney function. It is considered one of the health threats in the developing and undeveloped countries At early stages, few symptoms can be detected, where the CKD may not become obvious until significant kidney function impaired occur. CKD treatment focuses on reducing the kidney damage...
In the practice applications of defect detecting, large amounts of data need to be analyzed. In this paper, a new analysis method is developed based on adaboost algorithm. By using neural networks with a fixed structure, a series of models are built which may be not accurate. Error rates of the models are computed to gain and adjust the weights of every model. A higher accurate model is built by the...
The aim of the paper is to introduce a new approach for the Regions of Required Quality (RRQ) construction under the Control Systems computer-aided analysis and design. Application of the Artificial Neural Networks (ANNs) as a tool in the proposed techniques is represented under the title “Method of Sensitive Border”. The developed Neural network model of the RRQ-region's border allows one to get...
Ultrasonic Non-Destructive Testing (NDT) and imaging systems has been widely used for industrial and medical applications. In NDT system, detection and characterization of target signal can be extremely challenging because of the complex echo scattering environment and the system noise. In this paper, an algorithm based on Neural Network (NN) is presented to explore the possible solutions for ultrasonic...
In the process of establishing evaluation index system of physical education, the traditional methods setting weights for each indicator mainly include analytic hierarchy process, fuzzy comprehensive evaluation method, and Delphi method, etc. These methods mostly rely on experience, which is strongly influenced by artificial factors and cannot be avoided. Because artificial neural network model has...
With the future development of substation, the research of power fault detection algorithm has very important theoretical significance and wide application prospects. In order to improve the recognition of power line fault detection, one modeling method based on sparse self-encoding neural network is proposed. The dB3 wavelet is used to decompose the fault signal, and then the sub-band energy is calculated...
The paper proposes using a neuro-fuzzy controller in mobile networks for improving the handover process. An architecture of the neuro-fuzzy controller was developed. Linguistic variables, terms and membership functions for input and output values were defined. A rules base was developed. The operation of the neuro-fuzzy controller was simulated and trained.
Web applications commonly provide a high attack surface. In today's world of high impact attacks, protecting them against both known and unknown attacks becomes more important than ever. We present an approach of machine learning based anomaly detection to flexibly detect anomalous requests. Our approach leverages long short-term memory (LSTM) neural networks to learn a detailed model of normal requests...
At present, the researches on credit risk analysis mainly focus on commercial bank loan or consumer credit risk, and there is little research about the credit risk of rural credit cooperatives. The purpose of this paper is to evaluate credit risk for the rural credit cooperatives using artificial neural network model. We establish credit risk assessment index system for rural credit cooperatives....
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