Feature-Enhanced Nonequilibrium Bidirectional Long Short-Term Memory Model for Chinese Text Classification

Huan, Hai; Jiayu, Yan; Xie, Yaqin; Chen, Yifei; Li, Pengcheng; Zhu, Rongrong

doi:10.1109/access.2020.3035669

Cited by 17 publications

(6 citation statements)

References 14 publications

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“…Please see et al for information on the cluster. The terminal priority has been filed with the remaining energy levels in the CH node selection case [5]. Sink conditions are monitored by observing tree roots.…”

Section: Erman Et Al (2012)mentioning

confidence: 99%

Improved Network Validity Using Various Soft Computing Techniques

Yuvaraju¹,

Elakkiyavendan²

2023

Intelligent Automation &Amp; Soft Computing

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Nowadays, when a life span of sensor nodes are threatened by the shortage of energy available for communication, sink mobility is an excellent technique for increasing its lifespan. When communicating via a WSN, the use of nodes as a transmission method eliminates the need for a physical medium. Sink mobility in a dynamic network topology presents a problem for sensor nodes that have reserved resources. Unless the route is revised and changed to reflect the location of the mobile sink location, it will be inefficient for delivering data effectively. In the clustering strategy, nodes are grouped together to improve communication, and the cluster head receives data from compactable nodes. The sink receives the aggregated data from the head. The cluster head is the central node in the conventional technique. A single node uses more energy than a node that is routed to a dead node. Increasing the number of people using a route shortens its lifespan. The proposed work demonstrates the effectiveness with which sensor node paths can be modified at a lower cost by utilising the virtual grid. The best routes are maintained mostly by sink node communication on routes based on dynamic route adjustment (VGDRA). Only specific nodes are acquired to re-align data supply to the mobile sink in accordance with new paradigms of route reconstruction. According to the results, VGDRA schemes have a longer life span because of the reduced number of loops.

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Section: Erman Et Al (2012)mentioning

confidence: 99%

Improved Network Validity Using Various Soft Computing Techniques

Yuvaraju¹,

Elakkiyavendan²

2023

Intelligent Automation &Amp; Soft Computing

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“… Huan et al (2020) introduced a method for Chinese text classification that depended on a feature-enhanced nonequilibrium bidirectional long short-term memory (Bi-LSTM) network. This method enhanced the precision of Chinese text classification and had a reliable capability to recognize Chinese text features.…”

Section: Literature Reviewmentioning

confidence: 99%

Rider weed deep residual network-based incremental model for text classification using multidimensional features and MapReduce

Abdalla

Ahmed

Zeebaree

et al. 2022

PeerJ Computer Science

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Increasing demands for information and the rapid growth of big data have dramatically increased the amount of textual data. In order to obtain useful text information, the classification of texts is considered an imperative task. Accordingly, this article will describe the development of a hybrid optimization algorithm for classifying text. Here, pre-processing was done using the stemming process and stop word removal. Additionally, we performed the extraction of imperative features and the selection of optimal features using the Tanimoto similarity, which estimates the similarity between features and selects the relevant features with higher feature selection accuracy. Following that, a deep residual network trained by the Adam algorithm was utilized for dynamic text classification. Dynamic learning was performed using the proposed Rider invasive weed optimization (RIWO)-based deep residual network along with fuzzy theory. The proposed RIWO algorithm combines invasive weed optimization (IWO) and the Rider optimization algorithm (ROA). These processes are carried out under the MapReduce framework. Our analysis revealed that the proposed RIWO-based deep residual network outperformed other techniques with the highest true positive rate (TPR) of 85%, true negative rate (TNR) of 94%, and accuracy of 88.7%.

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“…The article [36] proposed a model to classify Chinese text by enhancing the features. This work first extracts the preliminary semantic features.…”

Section: Somementioning

confidence: 99%

Feature Engineering With Sentence Similarity Using the Longest Common Subsequence for Email Classification

Kodabagi²

2022

MJCS

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Feature selection plays a prominent role in email classification since selecting the most relevant features enhances the accuracy and performance of the learning classifier. Due to the exponential increase rate in the usage of emails, the classification of such emails posed a fitting problem. Therefore, there is a requirement for a proper classification system. Such an email classification system requires an efficient feature selection method for the accurate classification of the most relevant features. This paper proposes a novel feature selection method for sentence similarity using the longest common subsequence for email classification. The proposed feature selection method works in two main phases: First, it builds the longest common subsequence vector of features by comparing each email with all other emails in the dataset. Later, a template is constructed for each class using the closest features of emails of a particular class. Further, email classification is tested for unseen emails using these templates. The performance of the proposed method is compared with traditional feature selection methods such as TF-IDF, Information Gain, Chi-square, and semantic approach. The experimental results showed that the proposed method performed well with 96.61% accuracy.

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Feature-Enhanced Nonequilibrium Bidirectional Long Short-Term Memory Model for Chinese Text Classification

Cited by 17 publications

References 14 publications

Improved Network Validity Using Various Soft Computing Techniques

Improved Network Validity Using Various Soft Computing Techniques

Rider weed deep residual network-based incremental model for text classification using multidimensional features and MapReduce

Feature Engineering With Sentence Similarity Using the Longest Common Subsequence for Email Classification

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