An efficient approach for textual data classification using deep learning

Alqahtani, Abdullah; Khan, Habib Ullah; Alsubai, Shtwai; Sha, Mohemmed; Almadhor, Ahmad; Iqbal, Tayyab; Abbas, Sidra

doi:10.3389/fncom.2022.992296

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…However, the majority of data found today are in an unstructured format (Joseph et al, 2021). An EHR consists of extremely large volumes of valuable information and researchers have therefore worked to establish data-driven models (Alqahtani et al, 2022).…”

Section: Structured and Unstructured Healthcare Datamentioning

confidence: 99%

Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions

Shetty¹,

Ananthanarayana²,

Mahale³

2022

AIP

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Over the past few decades, the enormous expansion of medical data has led to searching for ways of data analysis in smart healthcare systems. Acquisition of data from pictures, archives, communication systems, electronic health records, online documents, radiology reports and clinical records of different styles with specific numerical information has given rise to the concept of multimodality and the need for machine learning and deep learning techniques in the analysis of the healthcare system. Medical data play a vital role in medical education and diagnosis; determining dependency between distinct modalities is essential. This paper gives a gist of current radiology medical data analysis techniques and their various approaches and frameworks for representation and classification. A brief outline of the existing medical multimodal data processing work is presented. The main objective of this study is to spot gaps in the surveyed area and list future tasks and challenges in radiology. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (or PRISMA) guidelines were incorporated in this study for effective article search and to investigate several relevant scientific publications. The systematic review was carried out on multimodal medical data analysis and highlighted advantages, limitations and strategies. The inherent benefit of multimodality in the medical domain powered with artificial intelligence has a significant impact on the performance of the disease diagnosis frameworks.

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Section: Structured and Unstructured Healthcare Datamentioning

confidence: 99%

Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions

Shetty¹,

Ananthanarayana²,

Mahale³

2022

AIP

View full text Add to dashboard Cite

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“…Several traditional machine learning and statistical approaches have been proposed for text categorization, such as Bayesian classifier, support vector machine (SVM), K-nearest neighbor (KNN), and neural networks [4]. Classification techniques have drawn realization in many applications including image classification, text filtering, spam filtering, email categorization, and text classification [5]. However, the information on websites can offer fast growth and brings it big challenge to the conventional method of web data analysis.…”

Section: Introductionmentioning

confidence: 99%

“…However, the information on websites can offer fast growth and brings it big challenge to the conventional method of web data analysis. Several machine learning techniques have been applied to analyze web data and time series prediction [5] but are facing challenges with the continued increasing amounts of web data.…”

Section: Introductionmentioning

confidence: 99%

Text Classification Using Deep Learning Models: A Comparative Review

Muhammad Zulqarnain,

Rubab Sheikh,

Shahid Hussain

et al. 2023

Cloud Computing and Data Science

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With the fast popularization and continued development of web pages on the Internet, text classification has become a very serious problem in organizing and managing large amounts of digital text data in documents. The deep learning approaches have been applied in several areas of text classification with comparative and outstanding results. In this article, we analyzed and gave comprehensive reviews of the different deep learning models for text classification tasks. Based on the literature review survey, this paper addresses three various deep learning models and declares their gaps and limitations. We have evaluated the various classification applications and a small discussion on the available Deep Neural Networks (DNN) frameworks for the implementation of text datasets. The work presents guidance for future research to regulate more significance that can be distributed for the better area of this research. In summary, our study presented the main implications, identified potential directions for future research, and highlighted the challenges within this specific research field. Additionally, our aim is to acquaint readers with the various subtasks and relevant literature related to the text classification process. By engaging with our discussion, we aspire to inspire readers to explore novel and enhanced techniques for text classification, applicable across diverse domains.

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