In this study, a hybrid optimisation strategy is used to build a deep learning system for pan sharpening. The final output image is examined using a weighted nonlinear regression model after the spatial resolution of ...
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In this study, a hybrid optimisation strategy is used to build a deep learning system for pan sharpening. The final output image is examined using a weighted nonlinear regression model after the spatial resolution of the low resolution-hyperspectral image (LR-HIS) and high resolution multi-spectral image (HR-MSI) is increased. The deep maxout network (DMN), which used residual learning to acquire its priors, is given the HR-MSI. Moreover, DMN is trained by fractional competitive multi-verse feedbacktreealgorithm (FrCMVFTA). Finally, the output produced from DMN and a weighted nonlinear regression model is combined together for obtaining pan sharpened image. The PSNR value obtained by the FrCMVFTA-based DMN for the dataset Indian pines by varying the number of bands is 5.41% greater than the existing approaches. The DD value obtained by the FrCMVFTA-based DMN for the dataset Pavia by varying the number of bands is 31.47% greater than existing approaches.
Purpose Internet has endorsed a tremendous change with the advancement of the new technologies. The change has made the users of the internet to make comments regarding the service or product. The Sentiment classifica...
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Purpose Internet has endorsed a tremendous change with the advancement of the new technologies. The change has made the users of the internet to make comments regarding the service or product. The Sentiment classification is the process of analyzing the reviews for helping the user to decide whether to purchase the product or not. Design/methodology/approach A rider feedbackartificialtree optimization-enabled deep recurrent neural networks (RFATO-enabled deep RNN) is developed for the effective classification of sentiments into various grades. The proposed RFATO algorithm is modeled by integrating the feedbackartificialtree (FAT) algorithm in the rider optimization algorithm (ROA), which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of term frequency-inverse document frequency (TF-IDF) features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted. The metrics employed for the evaluation in the proposed RFATO algorithm are accuracy, sensitivity, and specificity. Findings By using the proposed RFATO algorithm, the evaluation metrics such as accuracy, sensitivity and specificity are maximized when compared to the existing algorithms. Originality/value The proposed RFATO algorithm is modeled by integrating the FAT algorithm in the ROA, which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of TF-IDF features and spam wo
Autism spectrum disorder (ASD) is an umbrella term for a number of neurodevelopmental conditions with many heterogeneous behavioural indications. Recent medical imaging approaches use functional Magnetic Resonance Ima...
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Autism spectrum disorder (ASD) is an umbrella term for a number of neurodevelopmental conditions with many heterogeneous behavioural indications. Recent medical imaging approaches use functional Magnetic Resonance Imaging (fMRI) for human recognition of the various neurological syndromes. However, these traditional techniques are time consuming and expensive. Thus, in this research, an optimization assisted deep learning technique, named feedbackartificial Virus Optimization (FAVO)-based deep residual network (DRN), is developed. FAVO-based DRN is designed to incorporate the feedbackartificialtree (FAT) algorithm with Anti Corona Virus Optimization (ACVO). First, Region-Of-Interest extraction is carried out using thresholding techniques with nub region extraction completed using the proposed FAVO algorithm. ASD classification is then carried out using a DRN classifier. Evaluation of the proposal uses the ABIDE-1 and ABIDE-2 datasets. The developed FAVO algorithm attains better accuracy, sensitivity, and specificity of 0.9214, 0.9365, and 0.9142, respectively, by considering ABIDE-2 dataset.
Diabetic retinopathy (DR) is a major cause of blindness in adults, but early detection can help to manage the condition effectively. Current methods for automated DR screening mostly focus on finding specific eye lesi...
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The rapid evolution and tremendous growth of internet has provided massive growth of unstructured data that leads to a complexity while retrieving dynamic data effectively. The rapid growth in data volume has imposed ...
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The rapid evolution and tremendous growth of internet has provided massive growth of unstructured data that leads to a complexity while retrieving dynamic data effectively. The rapid growth in data volume has imposed many challenging constraints, such as necessity to retrieve data completely even if newly arrived samples are occurred and storing of huge volume of data. This has paved a way for concentrating more on incremental learning that functions on information streams. To speed up retrieval, clustering methods and indexes are utilized and periodic updating of clusters is very substantial because of dynamic nature of databases. Moreover, the standard of clustering techniques purely based on data representation techniques, in which traditional methods faced problems like dimensionality explosion and sparsity. To address such limitations, an effectual strategy is developed for incremental indexing and image classification using proposed feedback Social Optimization algorithm (FSOA). The image classification is effectively carried out using Deep neuro fuzzy optimizer and it is trained by employing the proposed FSOA and newly FSOA is derived by the integration of feedbackartificialtree (FAT) algorithm and Social Optimization algorithm (SOA). Moreover, the proposed FSOA has achieved the maximum clustering accuracy of 93.382, the maximum testing accuracy of 94.4, the maximum sensitivity of 91.892, and the maximum specificity of 96.058.
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