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Web Services Classification based on Wide & Bi-LSTM Model

Hongfan Ye, Buqing Cao, Zhenlian Peng, Ting Chen, Yiping Wen, Jianxun Liu
2019 IEEE Access  
To solve this problem, a Web service classification method based on Wide & Bi-LSTM model is proposed in this paper.  ...  INDEX TERMS Wide learning model, Bi-LSTM model, linear regression, web service classification.  ...  CONCLUSION AND FUTURE WORK This paper presents a Web service classification method based on Wide&Bi-LSTM model.  ... 
doi:10.1109/access.2019.2907546 fatcat:xhtys2rymbg55ao67jzyvtr4gm

Deepbot: A Deep Neural Network based approach for Detecting Twitter Bots

Linhao Luo, Xiaofeng Zhang, Xiaofei Yang, Weihuang Yang
2020 IOP Conference Series: Materials Science and Engineering  
For this challenge, the Deepbot is designed which adopts the Bi-LSTM model to analyze tweets and a Web interface is provided for public access which is developed using Web service.  ...  Although various techniques have been proposed, this task is still challenging if we want to judge whether the tweets are posted by a bot or not merely based on the textual information.  ...  In this paper, we propose a Bi-LSTM based Web application which can quickly detect bots based on only one piece of tweet.  ... 
doi:10.1088/1757-899x/719/1/012063 fatcat:bh4j2hkeenef5dpuhatddoyteq

ServeNet: A Deep Neural Network for Web Services Classification [article]

Yilong Yang, Nafees Qamar, Peng Liu, Katarina Grolinger, Weiru Wang, Zhi Li, Zhifang Liao
2020 arXiv   pre-print
Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years.  ...  and the length limitation, and then predict service classification on 50 service categories.  ...  Two search approaches are widely used in service discovery [5] [6] . The first one is the keyword-based method.  ... 
arXiv:1806.05437v3 fatcat:o6vgiw4izve4fc7afyn36w7r4y

Real-time Leakage Current Classification of 15kV and 25kV distribution insulators based on Bidirectional Long Short-Term Memory Networks with Deep Learning Machine

Chao-Tsung Yeh, Phuong Nguyen Thanh, Ming-Yuan Cho
2022 IEEE Access  
This paper presents an online monitoring system to classify the leakage/discharge current of the insulator in coastal sites using Bidirectional Long short-term memory (Bi-LSTM) models on a web-based service  ...  Moreover, a web-based service is developed for maintenance staff to interact with all current and predicted status of insulators.  ...  mechanism on a web-based service.  ... 
doi:10.1109/access.2022.3140479 fatcat:ut7aefcbo5eu7ef2m5pi22ecdi

Smart Contract Classification with a Bi-LSTM Based Approach

Gang Tian, Qibo Wang, Yi Zhao, Lantian Guo, Zhonglin Sun, Liangyu Lv
2020 IEEE Access  
Inspired by this, we proposed a smart contract classification approach based on Bi-LSTM model and Gaussian LDA, which can use a variety of information as inputs of the model, including source code, comments  ...  INDEX TERMS Smart contract classification, Bi-LSTM, attention mechanism, Gaussian LDA, account information.  ...  The other one considers Mashup requirements as application scenario to find best services. Besides, Cao et al. [9] used Bi-LSTM to learn the feature representations of Web services.  ... 
doi:10.1109/access.2020.2977362 fatcat:lylgzbigs5bfpiksnay5e3ksve

Identifying Emotion Labels from Psychiatric Social Texts Using a Bi-directional LSTM-CNN Model

Jheng-Long Wu, Yuanye He, Liang-Chih Yu, K. Robert Lai
2020 IEEE Access  
This study proposes a deep learning framework combining word embeddings, bi-directional long short-term memory (Bi-LSTM), and convolutional neural networks (CNN) to identify emotion labels from psychiatric  ...  The Bi-LSTM is a powerful mechanism for extracting features from sequential data in which a sentence consists of multiple words in a particular sequence.  ...  Healthcare-oriented web-based services draw many textbased queries related to depression.  ... 
doi:10.1109/access.2020.2985228 fatcat:nqqqxgssufcn3c6xaswqm4wzcm

GENETIC ALGORITHM - OPTIMIZED GATED RECURRENT UNIT (GRU) NETWORK FOR SEMANTIC WEB SERVICES CLASSIFICATION

Sridevi S, Karpagam G R, Vinoth Kumar B
2022 Malaysian Journal of Computer Science  
The novelty of the proposed model lies in implementing the GRU model for semantic web service classification.  ...  The state-of-the-art of GRU (Gated Recurrent Unit) one of the RNN model, provides a proficient classification process.  ...  The improved version of the LSTM model called the Bi_LSTM model is applied for web service classification [20] , that is initially developed by Google for deep and wide prediction of data to perform classification  ... 
doi:10.22452/mjcs.vol35no1.5 fatcat:nl6giqy6svgxjhailudclk62dm

Detection of Sentiment Analysis in Social Media using Deep Learning

2020 International journal of recent technology and engineering  
Furthermore, the prediction model trained using the activation function, Tanh, and when the amount of Bi-LSTM network layers is 2, the accuracy and F1-measure have a good better performance for sentiment  ...  classification.  ...  Bi-LSTM Xu et al. (2019) Binary Hotel reviews on travel service Web site, Ctrip Bi-LSTM Table - - II: The positive and negative lexical data and sources in our study Sentiment dictionary  ... 
doi:10.35940/ijrte.a1927.059120 fatcat:eaw5doex6zcn5pbpqplqyl4chu

High-quality Train Data Generation for Deep Learning-based Web Page Classification Models

Jeong-Jae Kim, Byung-Won On, Ingyu Lee
2021 IEEE Access  
Our experimental results with movies and cellphones data sets show that the average F 1 -score of the deep learning models (FNN, CNN, Bi-LSTM, and SeqGAN) trained with our proposed algorithm shows up to  ...  The current deep learning models detecting relevant web pages show low accuracy because of the poor quality of the training data.  ...  classification models (FNN, CNN, and Bi-LSTM) as shown in Table 5 .  ... 
doi:10.1109/access.2021.3086586 fatcat:4stqiqfnx5go5a5iimini4ds4i

Web Information Extraction methods using Web Content Mining (WCM) for Webapplications

Raghavendra R, Dr. Niranjanamurthy M
2022 International Journal of Computing and Digital Systems  
Classification methods are used in Artificial Neural Networks (ANN), it would train the input data from the large network and segregate them based on the algorithms used by the user.  ...  The technique Long Short-Term Memory (LSTM) is used to hold the status in intermediate memory then all generated data in web applications send this status to RNN for further classifications.  ...  services discovery TF-IDF, LDA, WE-LDA, Deep mining of hidden information Wide & Bi-LSTM model Collaborative Filtering (CF) Sentiment-aware deep recommender system 8 Review based analysis Matrix factorization  ... 
doi:10.12785/ijcds/110149 fatcat:cpkcklt2rzh6dhwpd45yktybge

Multimodal Classification of Onion Services for Proactive Cyber Threat Intelligence Using Explainable Deep Learning

Harsha Moraliyage, Vidura Sumanasena, Daswin De Silva, Rashmika Nawaratne, Lina Sun, Damminda Alahakoon
2022 IEEE Access  
In this paper, we propose a novel multimodal classification approach based on explainable deep learning that classifies onion services based on the image and text content of each site.  ...  on the content, as well as provide an interpretation of the classification outcome.  ...  The pipeline approach shown in Fig. 1 is widely adopted in building a dark web classification model.  ... 
doi:10.1109/access.2022.3176965 fatcat:dn4l6w5pqnbnzigsuzd46lf6oq

Unlocking the Power of Deep PICO Extraction: Step-wise Medical NER Identification [article]

Tengteng Zhang, Yiqin Yu, Jing Mei, Zefang Tang, Xiang Zhang, Shaochun Li
2020 arXiv   pre-print
However, in most circumstances, there will be more than one evidences in an extracted sentence even it has been categorized to a certain class.  ...  In table 2, we compare the PICO classification results of different models based on Bi-LSTM architecture.  ...  However, the scores of the Bi-LSTM model are much higher than the CNN model's, so here we only show the result of the Bi-LSTM model as in Table 2 .  ... 
arXiv:2005.06601v1 fatcat:pllt66lorbhmngley7hwg2z6tm

Dominant Lexicon Based Bi-LSTM for Emotion Prediction on a Text

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
As a result the proposed model Selective Lexicon Based BI-LSTM (SL + BI-LSTM) outperforms all the models with high accuracy.  ...  User-generated content and opinionative data has become a massive source of information on World Wide Web in the past few decades.  ...  This final vector is given as an input to neural network models CNN [24] , LSTM [25] , BI-LSTM and Selective Lexicon based BI-LSTM to predict the emotion.  ... 
doi:10.35940/ijitee.k1256.09811s19 fatcat:klvw37y6ijgppkxfrojhxxapli

Deepfake tweets classification using stacked Bi-LSTM and words embedding

Vaibhav Rupapara, Furqan Rustam, Aashir Amaar, Patrick Bernard Washington, Ernesto Lee, Imran Ashraf
2021 PeerJ Computer Science  
Besides, the performance of deep learning models is analyzed including long short-term memory network, gated recurrent unit, bi-direction LSTM, and convolutional neural network+LSTM.  ...  For this purpose, a stacked bi-directional long short-term memory (SBi-LSTM) network is proposed to classify the sentiment of deep fake tweets.  ...  Bi-LSTM Bidirectional LSTM is an extension to the traditional LSTM. Bi-LSTM improves the performance of the model on sequence classification problems.  ... 
doi:10.7717/peerj-cs.745 pmid:34805502 pmcid:PMC8576542 fatcat:pcwuornoere7ricq7ilorh2ewu

Deep Learning-Based Autoscaling Using Bidirectional Long Short-Term Memory for Kubernetes

Nhat-Minh Dang-Quang, Myungsik Yoo
2021 Applied Sciences  
Furthermore, as compared to the LSTM model, the Bi-LSTM model performs better in terms of resource provision accuracy and elastic speedup.  ...  Through experiments with two different realistic workloads, the Bi-LSTM model achieves better accuracy not only than the Long Short-Term Memory model but also than the state-of-the-art statistical auto-regression  ...  The Bi-LSTM prediction model explained in Section 5 is compared with the forward LSTM and ARIMA prediction models. The configuration of each compared model differs based on different datasets.  ... 
doi:10.3390/app11093835 doaj:49c18189a30942c6bcafa77966cabdde fatcat:qkf4lwpfh5ez7c25q5r42tidvq
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