Attention-based word-level contextual feature extraction and cross-modality fusion for sentiment analysis and emotion classification

Huddar, Mahesh G.; Sannakki, Sanjeev S.; Rajpurohit, Vijay S.

doi:10.1504/ijiei.2020.105430

Cited by 7 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DRCNN may visualize a variety of genome sequence features ( Fig. 5 ) in a compositional manner by using many convolutional layers and filters [ 11 ]. We simulate a set of significant sequences with several motif occurrences in the middle and a set of non-significant sequences with numerous motif occurrences dispersed throughout the sequence.…”

Section: Resultsmentioning

confidence: 99%

Intelligent Alzheimer’s Diseases Gene Association Prediction Model Using Deep Regulatory Genomic Neural Networks

Rohini,

Oswalt Manoj,

Surendran

2024

ADR

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is an illness that affects the nervous system, leading to a loss in cognitive and logical abilities. Gene regulatory expressions, which are the complex language exhibited by DNA, serve several functionalities, including the physical and biological life cycle processes in the human body. The gene expression sequence affects the pathology experienced by an individual, its longevity, and potential for a cure. The transcription factors, from DNA to RNA conversion, and the binding process determine the gene expression, which varies for every human organ and disease. This study proposes Deep convolutional neural network model that reads the gene regulatory expression sequence through various convolutional layers encoded to detect positive spikes in transcription factors. This results in the prediction of disease conversion probability from mild cognitive impairment to AD which is the key-requisite for affected geriatric cohorts.

show abstract

Section: Resultsmentioning

confidence: 99%

Intelligent Alzheimer’s Diseases Gene Association Prediction Model Using Deep Regulatory Genomic Neural Networks

Rohini,

Oswalt Manoj,

Surendran

2024

ADR

View full text Add to dashboard Cite

show abstract

“…A possible solution could be cross-modality attention, where we may carry out feature extraction at common frequency domains as suggested in Huddar et al . ( 2020 ). After that, we can align the features of the two modalities in the time dimension and feed them to the cross-modality models (such as Wei et al ., 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Prediction of cognitive scores by joint use of movie-watching fMRI connectivity and eye tracking via Attention-CensNet

et al. 2023

View full text Add to dashboard Cite

Brain functional connectivity under the naturalistic paradigm has been illustrated to be better at predicting individual behaviors than other brain states, such as rest and task. Nevertheless, the state-of-the-art methods are difficult to achieve desirable results from movie-watching paradigm fMRI (mfMRI) induced brain functional connectivity, especially when the datasets are fewer. Incorporating other physical measurements into the prediction method may enhance accuracy. Eye tracking, becoming popular due to its portability and less expense, can provide abundant behavioral features related to the output of human's cognition, and thus might supplement the mfMRI in observing subjects’ subconscious behaviors. However, there are very few works on how to effectively integrate the multimodal information to strengthen the performance by a unified framework. To this end, a fusion approach with mfMRI and eye tracking, based on Convolution with Edge-Node Switching in Graph Neural Networks (CensNet), is proposed in this article, with subjects taken as nodes, mfMRI derived functional connectivity as node feature, different eye tracking features used to compute similarity between subjects to construct heterogeneous graph edges. By taking multiple graphs as different channels, we introduce squeeze-and-excitation attention module to CensNet (A-CensNet) to integrate graph embeddings from multiple channels into one. The experiments demonstrate that the proposed model outperforms the one using single modality, single channel and state-of-the-art methods. The results suggest that brain functional activities and eye behaviors might complement each other in interpreting trait-like phenotypes.

show abstract

“…In this work, we execute each tree that terminates down in the bottom of the leaf with a new subset of predictors and we acquire a new observation subset vi. We proceed to the next tree, which has a slightly different leaf, which is anticipated to be of a different set of significant classes [5].…”

Section: Figure42 Proposed Rf Modelmentioning

confidence: 99%

Weighted Randomn Forest Model for Significant Feature and Disease Progression Prediction

Rohini¹,

Surendran²

2022

Preprint

View full text Add to dashboard Cite

In recent studies, several machine learning and deep learning prediction models have been proposed for the early detection and classification of various stages of Alzheimer's Disease (AD). Many years before the actual onset of AD, there occur several structural changes in the brain. These structural brain features can be utilized in learning the disease progression from early stage of disease. The various stages of pathology cause mild cognitive impairment (MCI) from their normal cognition and AD from normal cognition. This chapter intends to develop a random forest learning model that utilizes a relevant subset of predictors to diagnose the progression of the disease. The conversion from normal cognition to MCI is identified at an early stage of the onset of structural brain changes. The importance of existing research works lies in more early identification of significant feature that increases the disease progression and appropriate inventions greatly improves subjects' recovery. The ADNI cross-sectional MRI data were analysed in this study that utilized brain curvature, grey matter white matter density, volume of cortical and sub cortical structures, shape of hippocampus, hippocampal subfield volume, Mini-mental state exam (MMSE), Clinical Dementia Rating (CDR), Estimated Total Intracranial Volume, Normalize Whole Brain Volume, and Atlas Scaling Factor for constructing randomized trees and thus predicting the features that cause the progression of disease stages from MCI to Alzheimer’s disease that causes dementia. This implementation model proved to give robust AD conversion probability and identifying significant features that are sufficient for future clinical inferences.

show abstract

Attention-based word-level contextual feature extraction and cross-modality fusion for sentiment analysis and emotion classification

Cited by 7 publications

References 25 publications

Intelligent Alzheimer’s Diseases Gene Association Prediction Model Using Deep Regulatory Genomic Neural Networks

Intelligent Alzheimer’s Diseases Gene Association Prediction Model Using Deep Regulatory Genomic Neural Networks

Prediction of cognitive scores by joint use of movie-watching fMRI connectivity and eye tracking via Attention-CensNet

Weighted Randomn Forest Model for Significant Feature and Disease Progression Prediction

Contact Info

Product

Resources

About