Abstract The aim of this research is to propose an extended mean value cloning algorithm with automatic trimap generation and accurate alpha matting. This implementation improves the visualization accuracy of the merged video by reducing the discolored and smudging artefacts of the remote surgeon’s boundary. It also makes the merge robust for the illumination changes by taking less processing time in real time surgery. The proposed system uses automatic trimap generation from the source video for accurate foreground extraction. Extended mean value cloning with gradient mixing is then applied for the cloning with optimized alpha matting for accurate and realistic video composition. The proposed system improved the visualization accuracy by providing almost 99.7% visibility of the pixels compared to the state-of-the-art solution, which provides 99.1% visibility of pixels. The overlay error was reduced from 0.93 mm to 0.63 mm. The processing time was also reduced. The proposed solution processed 8 frames per second, which is less time than the state-of-the-art solution, which processed 5 frames per second. The extended mean value cloning smooths the differences that presented in the target and source frames for seamless and realistic blending of pixels. The automatic trimap generation reduced the risk of false foreground selection and the generated optimal trimaps improved the alpha matte quality, which is optimized to reduce the smudging artefacts completely and to produce accurate visualization of the final merged image.

Accurate classification of Magnetic Resonance Images (MRI) is essential to accurately predict Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) conversion. Meanwhile, deep learning has been successfully implemented to classify and predict dementia disease. However, the accuracy of MRI image classification is low. This paper aims to increase the accuracy and reduce the processing time of classification through Deep Learning Architecture by using Elastic Net Regularisation in Feature Selection. The proposed system consists of Convolutional Neural Network (CNN) to enhance the accuracy of classification and prediction by using Elastic Net Regularisation. Initially, the MRI images are fed into CNN for features extraction through convolutional layers alternate with pooling layers, and then through a fully connected layer. After that, the features extracted are subjected to Principle Component Analysis (PCA) and Elastic Net Regularisation for feature selection. Finally, the selected features are used as an input to Extreme Machine Learning (EML) for the classification of MRI images. The result shows that the accuracy of the proposed solution is better than the current system. In addition to that, the proposed method has improved the classification accuracy by 5% on average and reduced the processing time by 30 ~ 40 seconds on average. The proposed system is focused on improving the accuracy and processing time of MCI converters/non-converters classification. It consists of features extraction, feature selection, and classification using CNN, FreeSurfer, PCA, Elastic Net, and Extreme Machine Learning. Finally, this study enhances the accuracy and the processing time by using Elastic Net Regularisation, which provides important selected features for classification.

Abstract Recently, deep learning using convolutional neural network (CNN) has been used successfully to classify the images of breast cells accurately. However, the accuracy of manual classification of those histopathological images is comparatively low. This research aims to increase the accuracy of the classification of breast cancer images by utilizing a patch‐based classifier (PBC) along with deep learning architecture. The proposed system consists of a deep convolutional neural network that helps in enhancing and increasing the accuracy of the classification process. This is done by the use of the PBC. CNN has completely different layers where images are first fed through convolutional layers using hyperbolic tangent function together with the max‐pooling layer, drop out layers, and SoftMax function for classification. Further, the output obtained is fed to a PBC that consists of patch‐wise classification output followed by majority voting. The results are obtained throughout the classification stage for breast cancer images that are collected from breast‐histology datasets. The proposed solution improves the accuracy of classification whether or not the images had normal, benign, in‐situ, or invasive carcinoma from 87% to 94% with a decrease in processing time from 0.45 to 0.2 s on average. The proposed solution focused on increasing the accuracy of classifying cancer in the breast by enhancing the image contrast and reducing the vanishing gradient. Finally, this solution for the implementation of the contrast limited adaptive histogram equalization technique and modified tangent function helps in increasing the accuracy.

Abstract Background and aim: Many sophisticated data mining and machine learning algorithms have been used for software defect prediction (SDP) to enhance the quality of software. However, real‐world SDP data sets suffer from class imbalance, which leads to a biased classifier and reduces the performance of existing classification algorithms resulting in an inaccurate classification and prediction. This work aims to improve the class imbalance nature of data sets to increase the accuracy of defect prediction and decrease the processing time. Methodology: The proposed model focuses on balancing the class of data sets to increase the accuracy of prediction and decrease processing time. It consists of a modified undersampling method and a correlation feature selection (CFS) method. Results: The results from ten open source project data sets showed that the proposed model improves the accuracy in terms of F1‐score to 0.52 ∼ 0.96, and hence it is proximity reached best F1‐score value in 0.96 near to 1 then it is given a perfect performance in the prediction process. Conclusion: The proposed model focuses on balancing the class of data sets to increase the accuracy of prediction and decrease processing time using the proposed model.