Classification of Low-Grade and High-Grade Glioma from MR Brain Images Using Multiple-Instance Learning with Combined Feature Set

Fully automatic brain image classification of MR brain images is of great importance for research and clinical studies, since the precise detection may lead to a better treatment. In this work, an efficient method based on Multiple-Instance Learning (MIL) is proposed for the automatic classification of low-grade and high-grade MR brain tumor images. The main advantage of MIL-based approach over other classification methods is that MIL considers an image as a group of instances rather than a single instance, thus facilitating an effective learning process. The mi-Graph-based MIL approach is proposed for this classification. Two different implementations of MIL-based classification, viz. Patch-based MIL (PBMIL) and Superpixel-based MIL (SPBMIL), are made in this study. The combined feature set of LBP, SIFT and FD is used for the classification. The accuracies of low-grade–high-grade tumor image classification algorithm using SPBMIL method performed on [Formula: see text], [Formula: see text] and FLAIR images read 99.2765%, 99.4195% and 99.2265%, respectively. The error rate of the proposed classification system was noted to be insignificant and hence this automated classification system could be used for the classification of images with different pathological conditions, types and disease statuses.

Common Gabor Features for Image Watermarking Identification

Image watermarking is one of many methods for preventing unauthorized alterations to digital images. The major goal of the research is to find and identify photos that include a watermark, regardless of the method used to add the watermark or the shape of the watermark. As a result, this study advocated using the best Gabor features and classifiers to improve the accuracy of image watermarking identification. As classifiers, discriminant analysis (DA) and random forests are used. The DA and random forest use mean squared energy feature, mean amplitude feature, and combined feature vector as inputs for classification. The performance of the classifiers is evaluated using a variety of feature sets, and the best results are achieved. In order to assess the performance of the proposed method, we use a public database. VOC2008 is a public database that we use. The findings reveal that our proposed method’s DA classifier with integrated features had the greatest TPR of 93.71 and the lowest FNR of 6.29. This shows that the performance outcomes of the proposed approach are consistent. The proposed method has the advantages of being able to find images with the watermark in any database and not requiring a specific type or algorithm for embedding the watermark.

A New Approach for Deception Detection in Open Domain Text

The gigantic growth of platforms that give individuals the ability to write a review that is visible to everyone and the huge number of documents shared on the internet have triggered the researchers to try to detect if these platforms are trying to mislead and deceive people. There is a crucial need to find ways to automatically identify fake reviews and detect deceptive people or groups. The main aim of this research is to detect deception in open domain text by using a machine learning technique. Several sets of features are used to analyse the text including unigram, part of speech, and production rules. The experimental results showed that combined feature sets of (part of speech and production rules) using the support vector machine classifier achieve the best accuracy, and it clearly improves on the accuracy of the results reported in a previous study.