Efficient text summarization method for blind people using text mining techniques

Basheer, Shakila; Anbarasi, M. S.; Sakshi, Darpan Garg; Kumar, V. Vinoth

doi:10.1007/s10772-020-09712-z

Cited by 26 publications

(8 citation statements)

References 39 publications

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“…Venkata Srikanth and Strik (2019) use Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures for breath occasion discovery as a likely pointer of COVID-19 recognition. As of late, Basheer et al (2020) used the CNN architecture to perform direct COVID-19 symptomatic groupings dependent on cough sounds. The work in Chon et al (2012) uses a learning step technique of deep finding out how to do a similar analysis to our own, with an F1 score of 0.929, which is not at all like the methods discussed in this article.…”

Section: Literature Surveymentioning

confidence: 99%

RETRACTED ARTICLE: An adaptive speech signal processing for COVID-19 detection using deep learning approach

Al‐Dhlan

2021

Int J Speech Technol

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Section: Literature Surveymentioning

confidence: 99%

RETRACTED ARTICLE: An adaptive speech signal processing for COVID-19 detection using deep learning approach

Al‐Dhlan

2021

Int J Speech Technol

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“…Classical CNN gets 256-D, but we got 4096-D at the FC levels. This gives us extra data to make improved decisions In this suggested approach AlexNet CNNs architecture, we used 5 convolutional levels CONV [1,2,3,4,5] and 2 FC levels (FC-6 and FC-7). Of the three potential fully interconnected layers (FC-6, FC-7, and FC-8), FC-8 stood out because of its 1024-D properties.…”

Section: Alexresnet101 + Extraction Of Featurementioning

confidence: 99%

“…In the model we suggested, both sets of features were added together before categorization. Thus, we were able to (3). Feat DeepHybrid = conc [Feat AlexNet , Feat ResNet ] The following section provides details of the SVM-RBF model.…”

Section: Feature Fusionmentioning

confidence: 99%

“…DL has established an interest in up-to-date years in an extensive range of academic fields, including computer visualization, image analysis, and big data analytics. This strategy was able to obtain a high result across a wide variety of competitions, including the (ILSVRC) [3] is one of the most effective deep learning approaches, achieving exceptional results on demanding tasks including image analysis [4] visual object identification [5] and division [6]. This work aims to implement DCNN to identify breast cancer from mammograms (8000 mammography).…”

Section: Introductionmentioning

confidence: 99%

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Integrated IoT-based Healthcare System for the Early Detection of Breast Cancer Using Intelligent Diagnostic System

Shruthishree

Tiwari²,

Verma

2023

JMC

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Breast cancer represents one of the leading cancer-related diseases worldwide, affecting mostly women after puberty. Even though the illness is fatal and kills thousands of people each year, it is mostly curative if found quickly. As a result, prompt and precise detection methods are critical to patient survival. Previously, doctors used manual detection systems for this objective. However, such techniques have been slow and frequently dependent on the physician's expertise. As technology advanced, these primitive methodologies were supplemented by computer-aided detection (CAD) algorithms. Deep learning is extremely common because of the massive development in large data, the Internet of Things (IoT), linked devices, and high-performance computers using GPUs and TPUs. The Internet of Things (IoT) has advanced recently, and the healthcare industry is benefiting from this growth. Sensors that gather data for required analysis are crucial components utilized in the Internet of Things. Physicians and medical staff will be able to carry out their tasks with ease and intelligence thanks to the Internet of Things. The proposed research focus on integrating Alexnet and ResNet101 for accurate prediction of Breast malignancy from mammogram data. This methodology will target the features more precisely than any other combination of the pre-trained model. Finally, to resolve the computational burden issue, the feature reduction ReliefF methodology is used. To demonstrate the proposed method, an online publicly released set of data of 750 BU images is used. For training and testing the models, the set of data has been further split into 80 and 20% ratios. Following extensive testing and analysis, it was discovered that the DenseNet-201 and MobileNet-v2 trained SVMs to have an accuracy of 98.39 percent for the original and augmented Mammo images online datasets, respectively. This research discovered that the proposed approach is efficient and simple to implement to assist radiographers and physicians in diagnosing breast cancer in females.

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“…To avoid having to read the entire text each time, a more efficient method of summarizing books into important keywords was developed. The suggested model summarizes the content using a weighted TF-IDF (Term Frequency Inverse Document Frequency), and then converts it to speech [9]. These models are considered out of our study.…”

Section: A Conventional Summarization Modelsmentioning

confidence: 99%

A Comparative Study for Speech Summarization Based on Machine Learning: A Survey

Altememi¹,

Al-Irhaim²

2022

AL-Rafidain Journal of Computer Sciences and Mathematics

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The most important aspect of human communication is speech. Lengthy media such as speech takes a long time to read and understand. This difficulty is solved by providing a reduced summary with semantics. Speech summarization can either convert speech to text using automated speech recognition (ASR) and then build the summary, or it can process the speech signal directly and generate the summary. This survey will look at a various of recent studies that have used machine and deep learning algorithms to summarize speech. it discusses the speech summarizing literatures in terms of time restrictions, research methodology, and lack of interest in particular databases for literature searches. As newer deep learning approaches were not included in earlier surveys, this is a new survey in this discipline where different approaches with various datasets were explored for speech summarization and evaluated using subjective or objective methods.

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Efficient text summarization method for blind people using text mining techniques

Cited by 26 publications

References 39 publications

RETRACTED ARTICLE: An adaptive speech signal processing for COVID-19 detection using deep learning approach

RETRACTED ARTICLE: An adaptive speech signal processing for COVID-19 detection using deep learning approach

Integrated IoT-based Healthcare System for the Early Detection of Breast Cancer Using Intelligent Diagnostic System

A Comparative Study for Speech Summarization Based on Machine Learning: A Survey

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