Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the *** is propagated from the seeds of paddy and it is a stable food almost used byfifty percent of...
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Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the *** is propagated from the seeds of paddy and it is a stable food almost used byfifty percent of the total world *** extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food *** paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation *** of the researchers used to relay on historical records of meteorological parameters to predict the yield of *** is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind,relative humidity,Instant Wind Speed in paddy *** real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series.A Robust Optimized Artificial Neural Network(ROANN)algorithm with Genetic algorithm(GA)and Multi Objective Particle Swarm Optimization algorithm(MOPSO)proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation.A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the *** input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the *** database optimized by using Artificial Neural Network backpropagation *** reason for improving the growth of paddy was identified using the output of the Neural *** metrics such as Accuracy,Error Rate etc were used to measure the performance of the proposed *** analysis made between ANN with GA and ANN with MOPSO to identify the recommenda
This study proposes a back propagation algorithm into mechanism-Support Vector Machine model (BP-SVM), which dynamically adjusts the parameters of the model. In this paper, we used SILAB driving simulation software to...
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ISBN:
(数字)9780784484869
ISBN:
(纸本)9780784484869
This study proposes a back propagation algorithm into mechanism-Support Vector Machine model (BP-SVM), which dynamically adjusts the parameters of the model. In this paper, we used SILAB driving simulation software to simulate the driver's driving state under different weather conditions (i.e., sunny and foggy) and to detect driver fatigue by monitoring vehicle behavior (i.e., acceleration, steering wheel turns, standard deviation of speed, and longitudinal acceleration). The effectiveness of the BP-SVM model was measured by comparing the performance of RF, VGG, and SVM models in terms of accuracy and ROC curve. The results show that the average accuracy of BP-SVM, RF, SVM, and VGG are 93.97%, 89.53%, 83.88%, and 91.95%, respectively, all of which are lower than that of BP-SVM. The AUC areas of RF, SVM, VGG, and BP-SVM are 0.94, 0.92, 0.83, and 0.99, respectively, which shows that the BP-SVM model outperforms other classification models, particularly the VGG model.
Because of the nonlinear hysteresis characteristics of the magneto-rheological damper, the damper's inverse model has disadvantages of low fitting accuracy and poor practicality. Therefore, in this study, an optim...
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Because of the nonlinear hysteresis characteristics of the magneto-rheological damper, the damper's inverse model has disadvantages of low fitting accuracy and poor practicality. Therefore, in this study, an optimized genetic algorithm has been proposed to optimize the backpropagation neural network's initial weights and threshold. Compared with other damper controllers, the proposed inverse model improves the control current's prediction accuracy and tracks the desired damping force in real time. Moreover, the proposed inverse model and designed fuzzy controller are applied to the 1/4 vehicle suspension system simulation. The obtained results show that the optimized neural network model can be applied to a practical control. The root mean square value of body acceleration of semi-active suspension is lower than that of passive suspension under different road excitation. This method provides a foundation for the accurate modeling and semi-active control of the magneto-rheological damper.
A proton exchange membrane (PEM) fuel cell is an alternative energy source. Generally, the fuel cell is an electrochemical device in which the chemical energy is converted into electric energy, and the by-product of t...
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In this paper, we utilize the mechanism of simulating human nervous system in deep learning for movie data. Feature extraction is performed automatically and unstructured data is processed to improve the model recomme...
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This study aims to refine the backpropagation (BP) algorithm to enhance the precision and efficiency of evaluating mathematical teaching quality. Initial data collection, facilitated by the K-means clustering method, ...
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In order to run power plant operations smoothly, power plant faults need to be detected, located, and classified quickly. For this, artificial neural network approaches are considered significant tool in related appli...
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In order to run power plant operations smoothly, power plant faults need to be detected, located, and classified quickly. For this, artificial neural network approaches are considered significant tool in related application of power system. This research paper focuses on power plant faults detection and classification using intelligent approach of artificial neural network. There is frequent nature of power plant faults, in particular transmission line faults. The key intention of this research is to use ANN approach to detect and classify power plant faults, and in the end to compare their performance and accuracy. In this proposal, the three-phase current and voltage are use as input. backpropagation (BPNN) technique is used in developing the learning algorithm for multi-layer neural network that can accurately and reliably classify faults. The multi-layer neural network such as Levenberg-Marquardt and Bayesian regularization are used for offline training of the data. Findings from this research show that both Levenberg-Marquardt and Bayesian regularization show good accuracy to detect and classify faults in transmission line. However, LM algorithm trained data much faster than BR. LM shows fastest detection of faults with overall mean square error value of 6.13785e-3 at the 91 epoch. Whereas, BR trained network at the 717 epochs with overall mse of 4.67123e-3. It is to be noted that the accuracy of trained network to classify all the fault types is found to be 81.1%, which is higher than previous studies using backpropagation neural network technique. Training and test results show that these neural network-based methods can effectively detect and classify faults and faults types and have adequate performance. Matlab is used in this research because it has toolbox, which has effective features to train required data and achieve effective result.
Breast cancer is a major concern among women that causes high risk of death. Early diagnosis of such cancer becomes challenging due to alterations in the color of the histopathological breast images. This study uses a...
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Breast cancer is a major concern among women that causes high risk of death. Early diagnosis of such cancer becomes challenging due to alterations in the color of the histopathological breast images. This study uses a publicly available dataset of breast cancer histopathology images. This paper introduces a dual stage normalization approach, to address the color variation problem of biopsy specimen collectively caused by incompatible staining in biopsy process and bizarre imaging quality. The dual stage normalization proposed here consists of a stain normalization unit and a light normalization unit. This system addresses the variations of both imaging and staining of specimen that are caused by a microscopic imaging setup. Later on, eight features have been extracted from the normalized images and used for the classification of breast cancer (benign and malignant). The overall accuracy of the back propagation algorithm (BPA) classifier is obtained as 81.8%. After comparison with other classifier accuracies, BPA classifier is found to be acceptable. Recall and precision values are approximately 89% and 90%, respectively, which is acceptable. The saturation-weighted hue statistics produces balanced and uniform color hues for stain normalization. This statistic is powerful against variations in model parameters and unsusceptible to image subjects and achromatic colors. This normalization technique retains all histological data with an enhanced performance.
Secure transmission of images over a communication channel, with limited data transfer capacity, possesses compression and encryption schemes. A deep learning based hybrid image compression-encryption scheme is propos...
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Secure transmission of images over a communication channel, with limited data transfer capacity, possesses compression and encryption schemes. A deep learning based hybrid image compression-encryption scheme is proposed by combining stacked auto-encoder with the logistic map. The proposed structure of stacked autoencoder has seven multiple layers, and back propagation algorithm is intended to extend vector portrayal of information into lower vector space. The randomly generated key is used to set initial conditions and control parameters of logistic map. Subsequently, compressed image is encrypted by substituting and scrambling of pixel sequences using key stream sequences generated from logistic *** proposed algorithms are experimentally tested over five standard grayscale images. Compression and encryption efficiency of proposed algorithms are evaluated and analyzed based on peak signal to noise ratio(PSNR), mean square error(MSE), structural similarity index metrics(SSIM) and statistical,differential, entropy analysis respectively. Simulation results show that proposed algorithms provide high quality reconstructed images with excellent levels of security during transmission..
Grid computing is employed to unravel massive computational problems by using large numbers of heterogeneous computers connected to the computing network. Job scheduling is an important part of the grid computing envi...
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Grid computing is employed to unravel massive computational problems by using large numbers of heterogeneous computers connected to the computing network. Job scheduling is an important part of the grid computing environment, which is employed to extend the throughput and reduce the turnaround and reaction time. This paper proposed a new scheduling algorithm called "Feed forward neural network in the grid computing (FFNNGC) system," which is used to solve some real-life problems related to the pattern classification. In the proposed method, we have used a feed-forward algorithm to find the output in the grid computing network, and the network training is done until the system converges to a minimum error solution. The pattern classification problem consists of 13 real-life, and artificial dataset problems, including two class and multiclass problems. Experiments were performed under these real-life problems, and the results indicated that the proposed method is helpful in such types of problems.
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