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The main objective of this research work is to develop an expert system for the diagnosis and detection of Hepatitis and liver disorders based on various Artificial Neural Networks models. In this research work Artificial Neural Networks models like Back Propagation Algorithm, Probabilistic Neural Networks, Competitive learning Networks, Learning vector quantization and Elman Networks have been used...
In this paper various types of classifiers for quantitatively identify teletraffic service devices are proposed. The classification method “K — Nearest Neighbors With Defined Cityblock Metric Distance At Three Nearest Neighbors” is selected. A classifier structure is synthesized based on Adaptive Neuro-Fuzzy Interface Systems (ANFIS) in hybrid learning algorithm and Gaussian type membership function...
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,...
This paper aims to develop intelligent Predictive Monitoring Emission Systems (PEMS) for three distinct case studies involving traffic, gasoline fuel tanks and large combustion plants (LCP). The underlying theme of pollutant emissions exists in all three case studies whereby the gases that are monitored are NO2, unburned hydrocarbons, and SO2. These pollutants can cause grievous harm to health, environment...
Electricity market demands to the power industry in de-regulated form in this paper. The proposed load forecasting using ANN shows the effective risk management plans. This power market is to maintain their effective cost in terms of energy generation, energy purchase and optimization of the switching losses. This creates the need of load forecasting. So in this paper the load forecasting using ANN...
We design and implement a system to reduce the risk of heat stress, a recognized occupational health hazard (OHH), in two labor intensive industries using a job-combination approach. A novel feature of the system is employing artificial neural networks (ANNs) as model free estimators to evaluate perceived discomforts (PDs) of workers for different job combinations proposed in the work.
This paper presents the artificial neural network (ANN) that used to perform the short-term load forecasting (STLF). The input data of ANN is comprises of multiple lags of hourly peak load. Hence, imperative information regarding to the movement patterns of a time series can be obtained based on the multiple time lags of chronological hourly peak load. This may assist towards the improvement of ANN...
Eddy current testing (ECT) is becoming a widely used inspection technique, particularly in the aircraft, power and nuclear industries. Many factors may affect the eddy current response. Inverse problems to determine the thickness from ECT signals of multilayer conductors have been a challenge for a certain degree. The objectives of this study are to introduce a method based on improved back propagation...
Acoustic emission (AE) of material is a common phenomenon; In fact, it has relation with the material states. In this paper, the acoustic emission test method is used and the characteristics of different phases of aluminum alloy cracks are analyzed. Artificial neural network (ANN) is used to recognize the pattern of AE of fatigue cracks. Practical test shows the above method could test the cracks...
In view of the deficiency of the traditional methods, according to the analysis of surface water in Suzhou city, a BP neural network model is proposed to evaluate water quality. Firstly The present situation and changing trends of surface water are analyzed. The structure of BP model is described and the choice of hidden layer is also optimized. Finally, the proposed model was applied to evaluate...
In the present work an attempt is made to develop a clinical decision support system (CDSS) using the pathological attributes to predict the fetal delivery to be done normal or by surgical procedure. The pathological tests like blood sugar (BR), blood pressure (BP), resistivity index (RI) and systolic-diastolic (S/P) ratio will be recorded at the time of delivery. All attributes lie within a specific...
In view of the deficiency of the traditional methods, a BP neural network model is proposed to evaluate water quality. The proposed model was applied to evaluate the water quality of 20 sections in Suzhou river. The evaluation result was compared with that of the RBF neural network method and the reported results in Suzhou river. It indicated that the performance of proposed neural network model is...
Particle swarm optimization and neural networks (PSO-NN) were proposed for twin-spirals scroll compressor (TSSC) performance prediction. The method integrated evolutionary mechanism of PSO and self-learning, nonlinear approach ability of NN. The main structure parameters of TSSC were been as input variables and the main performance parameters were been as output variables in established NN. PSO was...
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