Early detection of Alzheimer's disease (AD) and its prodromal state, mild cognitive impairment (MCI), is crucial for providing suitable treatment and preventing the disease from progressing. It can... Show More

Early detection of Alzheimer's disease (AD) and its prodromal state, mild cognitive impairment (MCI), is crucial for providing suitable treatment and preventing the disease from progressing. It can also aid researchers and clinicians in identifying early biomarkers and ministering new treatments that have been a subject of extensive research. The application of deep learning techniques on structural magnetic resonance imaging (MRI) has shown promising results in diagnosing the disease. In this research, the authors intend to introduce a novel approach of using an ensemble of the self-attention-based bottleneck transformers with a sharpness-aware minimizer for early detection of Alzheimer's disease. The proposed approach has been tested on the widely accepted ADNI dataset and evaluated using accuracy, precision, recall, F1 score, and ROC-AUC score as the performance metrics.

A set of experiments has shown that deep learning as well as traditional learning can be used in the weed detection process and perform well, although sometimes these models cannot fully exploit and... Show More

A set of experiments has shown that deep learning as well as traditional learning can be used in the weed detection process and perform well, although sometimes these models cannot fully exploit and utilize the long-term dependency relationship between some key features of images and image labels. To remedy this known problem in the field of image classification, the authors have introduced a classifier known as the linear support vector machine (SVM). Specifically, they have combined a ResNeXt and SVM network to provide the ResNeXt-SVM framework that can deepen the exploitation of the structured features of images and the understanding of their content. The experimental results show that compared to other algorithm models such as ResNet, ResNeXt, and VGG, the proposed solution is more precise and efficient in classifying weeds.

In the modern context, the similarity is determined by content-preserving stimuli, retrieval of relevant ‘nearest neighbor' objects, and the way similar objects are pursued. Current similarity... Show More

In the modern context, the similarity is determined by content-preserving stimuli, retrieval of relevant ‘nearest neighbor' objects, and the way similar objects are pursued. Current similarity search in hamming-space-based strategies finds all the data objects within a threshold hamming-distance for a user query, though the number of computations for distance and candidate generation are key concerns from the many years. The hamming-space paradigm extends the range of alternatives for an optimized search experience. A novel counting-based similarity search strategy is proposed with an improved hamming-space (e.g., optimized candidate generation and verification function). The strategy adapts towards the lesser set of user query dimensions and subsequently constrains the hamming-space computations with each data object driven by generated statistics. The extensive evaluation asserts that the proposed counting-based approach can be combined with any pigeonhole principle-based similarity search to further improve its performance.

This study uses aspect-level sentiment analysis using lexicon-based approach to analyse online reviews of an Indian brand called Patanjali, which sells many FMCG products under its name. These... Show More

This study uses aspect-level sentiment analysis using lexicon-based approach to analyse online reviews of an Indian brand called Patanjali, which sells many FMCG products under its name. These reviews have been collected from the microblogging site Twitter from where a total of 4961 tweets about 10 Patanjali branded products have been extracted and analysed. Along with the aspect-level sentiment analysis, an opinion-tagged corpora has also been developed. Machine learning approaches—support vector machine (SVM), decision tree, and naïve bayes—have also been used to perform the sentiment analysis and to figure out the appropriate classifiers suitable for such product reviews analysis. The authors first identify customer preferences and/or opinions about a product or brand by analyisng online customer reviews as they express them on the social media platform Twitter by using aspect-level sentiment analysis. The authors also address the limitations of scarcity of opinion tagged data required to train supervised classifiers to perform sentiment analysis by developing tagged corpora.

Images, being around us in every aspect of life, have become an emerging field of research. Extensive image analysis has been done on binary as well as coloured images, which has led various... Show More

Images, being around us in every aspect of life, have become an emerging field of research. Extensive image analysis has been done on binary as well as coloured images, which has led various researchers to explore images having deep spectral knowledge about a particular area of interest. High resolution images, having more than three spectral bands, capture minute details of an object in various spectral bands resulting in high computational complexity. In this paper, the authors have tried to reduce the complexity of multispectral image by selecting only the relevant bands need to reconstruct an image. Traditional principal component analysis technique is used for band selection of true color bands and classification-assessed results of both the images; original and dimensionality reduced images are compared using partitioning clustering technique. Experimental results show that compressed image after reduction of bands by PCA yields better classification results than the original image.