This work intends to discover diversified association rules efficiently using a cluster computing model. At first, the input data is pre-processed for data transformation. Then, the preprocessed data is given to k-mea...
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This work intends to discover diversified association rules efficiently using a cluster computing model. At first, the input data is pre-processed for data transformation. Then, the preprocessed data is given to k-means clustering to cluster the data. Since the initialization of the centroid is the key feature of k-means clustering, it is taken as the major challenge here. The randomly assigned centroid is optimally tuned by the new Fitness based Probability for cuckoosearch (FP-CS) model. By exploiting adopted FP-CS, the best k-means centroid is determined. Thus, the optimal centroids are further processed for k-means clustering, and the optimal clustered data is attained. The clustered data is then given as input to the apriori algorithm, and rule mining data is attained in a proficient manner. Moreover, the adopted FP-CS model is evaluated with conservative methods, and the relevant outcomes are verified.
Accurate photovoltaic(PV)power prediction can effectively help the power sector to make rational energy planning and dispatching decisions,promote PV consumption,make full use of renewable energy and alleviate energy ...
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Accurate photovoltaic(PV)power prediction can effectively help the power sector to make rational energy planning and dispatching decisions,promote PV consumption,make full use of renewable energy and alleviate energy *** address this research objective,this paper proposes a prediction model based on kernel principal component analysis(KPCA),modified cuckoo search algorithm(MCS)and deep convolutional neural networks(DCNN).Firstly,KPCA is utilized to reduce the dimension of the feature,which aims to reduce the redundant input *** using MCS to optimize the parameters of ***,the photovoltaic power forecasting method of KPCA-MCS-DCNN is *** order to verify the prediction performance of the proposed model,this paper selects a photovoltaic power station in China for example *** results show that the new hybrid KPCA-MCS-DCNN model has higher prediction accuracy and better robustness.
In this work, a novel and efficient approach for image denoising is proposed. More often, noise affecting the pixels in image is Gaussian in nature and uniformly deters information pixels in image irrespective of thei...
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In this work, a novel and efficient approach for image denoising is proposed. More often, noise affecting the pixels in image is Gaussian in nature and uniformly deters information pixels in image irrespective of their intensity values. This behaviour of noise can also be identified as Additive White Gaussian Noise (AWGN). For restoration of AWGN affected images, the proposed denoising approach is inspired by image adaptive guided image filtering using modified cuckoo search algorithm. The guidance image is itself derived from the noisy image for this purpose. Bilateral filtering smoothed noisy image is sharpened by unsharp masking and then employed as guidance image for the proposed optimal guided filtering approach. Optimal evaluation of parameters like guided filter smoothing parameter (regularization parameter or degree of smoothing (DoS)) and guided filter?s neighbourhood (kernel) size is done appropriately with the help of the modified cuckoo search algorithm. Two-dimensional search space is explored and exploited for deciding the behaviour of guided filtering adaptively as per the input image requirements. This guided image filter has a better behaviour at it acts as an edge preserving smoothing operator. It is considerably effective as its computational complexity is independent of filtering kernel size. A novel attempt is made by incorporating the Markov Random Field based Energy Minimization based objective/fitness function for imparting adaptive image denoising using metaheuristic intelligence. The proposed method is tested in terms of the performance metrics like peak signal to noise ratio, structural similarity index and mean square error. Performance of the proposed approach is compared with the already proposed image denoising techniques. For this comparison, only those methods are considered which were proposed for filtering of Gaussian Noise. Qualitative (visual) as well as quantitative (objective) results underlines the efficacy of the proposed met
In this present work, an attempt is made to solve FMS scheduling problem by considering the multiobjective using modifiedcuckoos searchalgorithm. Combined Objective Function (COF) is formulated by considering two ob...
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ISBN:
(纸本)9789811513077;9789811513060
In this present work, an attempt is made to solve FMS scheduling problem by considering the multiobjective using modifiedcuckoos searchalgorithm. Combined Objective Function (COF) is formulated by considering two objectives with equal weightage i.e., minimizing the machine idle time and minimizing the penalty cost. The problem considered is 43 jobs need to be manufactured by processing on 16 machines is taken from literature. Matlab program is written to calculate COF value and for finding best sequence, COF value MCS algorithm is implemented. Best sequence and COF values obtained by MCS algorithm are compared with values obtained by other algorithms like SPT, LPT, PSO, GA & CS. It is observed that sequence obtained by modifiedcuckoos searchalgorithm is giving better COF values.
For fault detection and diagnosis in large-scale industrial systems, traditional methods have a low classification accuracy, which is an issue. This paper proposes a fault diagnosis method based on the modifiedcuckoo...
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For fault detection and diagnosis in large-scale industrial systems, traditional methods have a low classification accuracy, which is an issue. This paper proposes a fault diagnosis method based on the modified cuckoo search algorithm (MCS) to optimize the probabilistic neural network (PNN). The random forest treebagger (RFtb) is used to reduce the data feature and the PNN is trained for fault diagnosis and classification. However, in order to address the problem that the parameters of PNN easily fall into the local optimal value, the MCS algorithm is introduced to globally optimize the hidden layer element smoothing factor (sigma) in the PNN. The MCS algorithm uses a parameters update and a better optimization mechanism to achieve excellent global convergence and to effectively improve the fault diagnosis capability of the model. During the testing process using the Tennessee Eastman (TE) process dataset, the performance of the proposed model is assessed by comparing the accuracy and the F-1-score of different methods. Graphs are presented that depict fault classification and diagnostic results for the different models. The results show that the MCS algorithm has a better optimization ability than the traditional optimization algorithm and the proposed combination method can significantly improve the accuracy of the TE process fault diagnosis.
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