A Novel Soft Clustering Approach for Gene Expression Data

Kavitha, E.; Tamilarasan, R.; Devi, B. Aruna; Kannan, M. K. Jayanthi

doi:10.32604/csse.2022.021215

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…Polarity problems [28] are tackled in our study by polarizing the list of sentences and then creating a specific vocabulary (building a new lexicon) related to the discharge summary. The subjectivity and polarity of the text are then determined by applying unsupervised techniques and accessible terminologies, such as SentiWordNet, TextBlob, Unified Medical Language System (ULMS), and Valence Aware Dictionary and sentiment Reasoner (VADSR) [27].…”

Section: Proposed Methodsmentioning

confidence: 99%

Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Waheeb¹

2023

Computer Systems Science and Engineering

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Automatic extraction of the patient's health information from the unstructured data concerning the discharge summary remains challenging. Discharge summary related documents contain various aspects of the patient health condition to examine the quality of treatment and thereby help improve decision-making in the medical field. Using a sentiment dictionary and feature engineering, the researchers primarily mine semantic text features. However, choosing and designing features requires a lot of manpower. The proposed approach is an unsupervised deep learning model that learns a set of clusters embedded in the latent space. A composite model including Active Learning (AL), Convolutional Neural Network (CNN), BiGRU, and Multi-Attention, called ACBMA in this research, is designed to measure the quality of treatment based on discharge summaries text sentiment detection. CNN is utilized for extracting the set of local features of text vectors. Then BiGRU network was utilized to extract the text's global features to solve the issues that a single CNN cannot obtain global semantic information and the traditional Recurrent Neural Network (RNN) gradient disappearance. Experiments prove that the ACBMA method can demonstrate the effectiveness of the suggested method, achieve comparable results to state-of-arts methods in sentiment detection, and outperform them with accurate benchmarks. Finally, several algorithm studies ultimately determined that the ACBMA method is more precise for discharge summaries sentiment analysis.

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Section: Proposed Methodsmentioning

confidence: 99%

Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Waheeb¹

2023

Computer Systems Science and Engineering

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“…Hooda et al studied the classification of brain MRI using the fuzzy-gravitational search algorithm (GSA) using four benchmark datasets from the UC Irvine repository. 30 The results are compared with GSA and current research on brain image segmentation techniques for both real-world and hypothetical databases based on dice coefficient values. The results demonstrate that the Fuzzy-GSA methodology achieves the highest quality clustering over the selected datasets when compared to various alternative clustering algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hooda et al studied the classification of brain MRI using the fuzzy‐gravitational search algorithm (GSA) using four benchmark datasets from the UC Irvine repository 30 . The results are compared with GSA and current research on brain image segmentation techniques for both real‐world and hypothetical databases based on dice coefficient values.…”

Section: Literature Reviewmentioning

confidence: 99%

Glioma detection using EHO based FLAME clustering in MR brain images

Karun,

Prasath,

Rajasekaran

et al. 2023

Int J Imaging Syst Tech

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MRI is a popular imaging method for examining brain tumours. The ability to precisely segment tumours from MRI is absolutely essential for medical diagnostics and surgical planning. Manual tumour segmentation might be unrealistic for more comprehensive studies. Deep learning is the most widely used technique in medical diagnosis. For effective tumour dissection from brain MRI, this paper proposed a novel combination of FLAME and EHO Algorithm. FLAME is a type of clustering method that groups the most similar pixels in to a single cluster. EHO algorithm is one of the nature‐inspired metaheuristic optimization algorithms based on the social herding behaviour of elephants and swimming search methods. The proposed methodology's efficiency is validated through testing on various BraTS challenge datasets. The average computational time, mean squared error, peak signal to noise ratio, tanimoto coefficient, and dice score ‐ obtained are 23.3775 s, 0.213, 54.9669 dB, 54.6148%, and 84.053%, respectively.

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A Novel Soft Clustering Approach for Gene Expression Data

Cited by 2 publications

References 16 publications

Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Glioma detection using EHO based FLAME clustering in MR brain images

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