Using BiLSTM Networks for Context-Aware Deep Sensitivity Labelling on Conversational Data

Pogiatzis, Antreas; Samakovitis, Georgios

doi:10.3390/app10248924

Cited by 12 publications

(6 citation statements)

References 43 publications

(52 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The features from the cosine similarity are passed into the BiLSTM model to classify the web pages. RNN is a special variety of normal ANN that demonstrates sequential information using recurrent connections [24]. Basically, it continues with the hidden state that is regarded as "memory" of preceding input.…”

Section: Design Of Bso-bilstm Based Classification Modelmentioning

confidence: 99%

An Automated Word Embedding with Parameter Tuned Model for Web Crawling

Neelakandan¹,

Aggarwal²,

Bhukya³

et al. 2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

In recent years, web crawling has gained a significant attention due to the drastic advancements in the World Wide Web. Web Search Engines have the issue of retrieving massive quantity of web documents. One among the web crawlers is the focused crawler, that intends to selectively gather web pages from the Internet. But the efficiency of the focused crawling can easily be affected by the environment of web pages. In this view, this paper presents an Automated Word Embedding with Parameter Tuned Deep Learning (AWE-PTDL) model for focused web crawling. The proposed model involves different processes namely pre-processing, Incremental Skip-gram Model with Negative Sampling (ISGNS) based word embedding, bidirectional long short-term memory-based classification and bird swarm optimization based hyperparameter tuning. The SGNS training desires to go over the complete training data to pre-compute the noise distribution before performing Stochastic Gradient Descent (SGD) and the ISGNS technique is derived for the word embedding process. Besides, the cosine similarity is computed from the word embedding matrix to generate a feature vector which is fed as input into the Bidirectional Long Short-Term Memory (BiLSTM) for the prediction of website relevance. Finally, the Birds Swarm Optimization-Bidirectional Long Short-Term Memory (BSO-BiLSTM) based classification model is used to classify the webpages and the BSO algorithm is employed to determine the hyperparameters of the BiLSTM model so that the overall crawling performance can be considerably enhanced. For validating the enhanced outcome of the presented model, a comprehensive set of simulations are carried out and the results are examined in terms of different measures. The Automated Word Embedding with Parameter Tuned Deep Learning (AWE-PTDL) technique has attained a higher harvest rate of 85% when compared with the other techniques. The experimental results highlight the enhanced web crawling performance of the proposed model over the recent state of art web crawlers.

show abstract

Section: Design Of Bso-bilstm Based Classification Modelmentioning

confidence: 99%

An Automated Word Embedding with Parameter Tuned Model for Web Crawling

Neelakandan¹,

Aggarwal²,

Bhukya³

et al. 2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…Figure 3 illustrates the process of BiLSTM. This is determined by every neuron represent a calculation function of whole prior data (Pogiatzis & Samakovitis, 2020). The input units …,…”

Section: Bilstm-based Feature Extractionmentioning

confidence: 99%

“…Figure 3 illustrates the process of BiLSTM. This is determined by every neuron represent a calculation function of whole prior data (Pogiatzis & Samakovitis, 2020). The input units

({} \dots, x_{t - 1}, x_{t}, x_{t + 1} \dots)

whereas

= ((),,,,, x_{1}, x_{2}, x_{3}, \dots, x_{N})

, are linked to the hidden unit

h_{t} = ((),,,, h_{1}, h_{2}, \dots, h_{M})

in the hidden layer, through links determined using weight matrix

W_{IH}

.…”

Section: The Proposed Modelmentioning

confidence: 99%

Internet of things and deep learning enabled healthcare disease diagnosis using biomedical electrocardiogram signals

et al. 2021

View full text Add to dashboard Cite

With recent advancements in the internet of things (IoT), wearables, and sensing technologies, the quality of healthcare services gets improved and it caused a shift from conventional clinical‐based healthcare to real‐time monitoring. The sensors are commonly integrated into several medical gadgets to save the bio‐signals produced by the physiological activities of the human body. At the same time, a biomedical electrocardiogram (ECG) signal is employed as a familiar way to examine and diagnose cardiovascular diseases (CVDs), which is rapid and non‐invasive. Since the increasing number of patients degrades the classification performance due to high differences in the ECG signal patterns among several patients, computer‐assisted automated diagnostic tools are essential for ECG signal classification. With this motivation, this paper introduces a new IoT and deep learning (DL) enabled healthcare disease diagnosis (IoTDL‐HDD) model using biomedical ECG signals. The proposed IoTDL‐HDD model aims to detect the presence of CVDs by the use of DL models in biomedical ECG signals. In addition, the proposed IoTDL‐HDD model utilizes a BiLSTM feature extraction technique to extract useful feature vectors from the ECG signals. For improving the efficiency of the BiLSTM technique, the artificial flora optimization (AFO) algorithm is employed as a hyperparameter optimizer. Besides, a fuzzy deep neural network (FDNN) classifier is employed for assigning proper class labels to the ECG signals. The performance of the IoTDL‐HDD model is examined on biomedical ECG signals and the outcomes are inspected in distinct features. The resultant experimental outcomes pointed out the supremacy of the IoTDL‐HDD model with the maximum accuracy of 93.452%.

show abstract

“…Bidirectional LSTMs extend from traditional LSTMs [17], and they could enhance the model performance in terms of sequence classification issues. In situations wherein all time steps of the input sequence have been provided, the Bidirectional LSTMs trains two LSTMs [18] rather than just one on the input sequence: the first one on the input sequence will be as-is, whereas the other is on a reversed copy of the input sequence.…”

Section: Bidirectional Lstmsmentioning

confidence: 99%

Sentiment classification of user's reviews on drugs based on global vectors for word representation and bidirectional long short-term memory recurrent neural network

Obayed

Al-Turaihi

Alhussayni

2021

IJEECS

View full text Add to dashboard Cite

<p>The process of product development in the health sector, especially pharmaceuticals, goes through a series of precise procedures due to its directrelevance to human life. The opinion of patients or users of a particular drugcan be relied upon in this development process, as the patients convey their experience with the drugs through their opinion. The social media field provides many datasets related to drugs through knowing the user's ratingand opinion on a drug after using it. In this work, a dataset is used that includes the user’s rating and review on the drug, for the purpose of classifying the user’s opinions (reviews) whether they are positive ornegative. The proposed method in this article includes two phases. The first phase is to use the Global vectors for word representation model for converting texts into the vector of embedded words. As for the second stage, the deep neural network (Bidirectional longshort-termmemory) is employedin the classification of reviews. The user's rating is used as a ground truth inevaluating the classification results. The proposed method present sencouraging results, as the classification results are evaluated through threecriteria, namely Precision, Recall and F-score, whose obtained values equal(0.9543, 0.9597and0.9558), respectively. The classification results of theproposed method are compared to a number of classifiers, and it was noticed that the results of the proposed method exceed those of the alternative classifiers.</p>

show abstract

Using BiLSTM Networks for Context-Aware Deep Sensitivity Labelling on Conversational Data

Cited by 12 publications

References 43 publications

An Automated Word Embedding with Parameter Tuned Model for Web Crawling

An Automated Word Embedding with Parameter Tuned Model for Web Crawling

Internet of things and deep learning enabled healthcare disease diagnosis using biomedical electrocardiogram signals

Sentiment classification of user's reviews on drugs based on global vectors for word representation and bidirectional long short-term memory recurrent neural network

Contact Info

Product

Resources

About