An LSTM-Based Fake News Detection System Using Word Embeddings-Based Feature Extraction

Sharma, Rishibha; Agarwal, Vidhi; Sharma, Sushma; Arya, Meenakshi S.

doi:10.1007/978-981-15-8354-4_26

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…However, our proposed model, the PB-LSTM, is a DNN model built on two well-known NN models, namely, LSTM and MLP. The LSTM model ( 22 )has been successfully applied for time-series forecasting with long-term dependencies ( 1 , 17 , 23 , 24 ). Furthermore, over the past few years, many different forms of LSTM have been proposed for traffic flow forecasting; however, none has shown significant performance improvement over the basic LSTM model ( 17 ).…”

Section: Methodsmentioning

confidence: 99%

Real-Time Intersection Turning Movement Flow Forecasting Using a Parallel Bidirectional Long Short-Term Memory Neural Network Model

Zhang

Pan

2023

Transportation Research Record: Journal of the Transportation R

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Real-time forecasting of intersection turning movements is a critical requirement for predictive traffic signal control at signalized intersections. Several traffic flow forecasting models have been developed in the past; however, most of them have focused only on the road segment-level traffic instead of the turning movement flow (TMF) at intersections. Therefore, in this paper, we propose a new TMF forecasting model, which consists of a combination of two neural network models, namely the stacked bidirectional long short-term memory and the traditional multi-layer perceptron model; this combination will enable effective learning for both short- and long-term time-varying patterns. Moreover, extensive computational experiments, using two years of turning movement counts at 22 intersections in the city of Milton, Ontario, Canada, explore the performance advantage of the proposed model in comparison with several state-of-the-art base models for forecasting accuracy, robustness, and transferability.

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Section: Methodsmentioning

confidence: 99%

Real-Time Intersection Turning Movement Flow Forecasting Using a Parallel Bidirectional Long Short-Term Memory Neural Network Model

Zhang

Pan

2023

Transportation Research Record: Journal of the Transportation R

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“…The extraction of skills is a problem belonging to the subworld of information extraction. In this case, it seeks to identify those parts of a text that are related to the demand for skills (Sharma, 2019). The general framework for this type of problem is known as the emerging NLP tasks (Chernova, 2020;Fareri et al, 2020;Lunn, Zhu & Ross, 2020).…”

Section: Different Approaches To the Skills Extraction Problemmentioning

confidence: 99%

“…In the case of skill extraction, the same distinction exists. Within the applied techniques, most approaches to this type of problem are based on supervised models (Sharma, 2019;Chernova, 2020;Wings, Nanda & Adebayo, 2021). 2 Many studies have created a training dataset, trained a model on a specific technique and then applied it to unseen data for testing.…”

Section: Different Approaches To the Skills Extraction Problemmentioning

confidence: 99%

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But which skills?

Anelli

2023

WOLG

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Skills assessment is essential for today’s labour market. There are many factors that change the requirements for the workplace. More than ever, it is important to monitor which skills are in high demand so that workers stay employed and companies do not lose productivity. This research discusses the relevance of data from online job portals for this task. It then uses a skill extractor in online job advertisements from Chile to identify and extract the skills employers place in their online job advertisements through skills dictionaries. The study shows modest results when using the European Skills, Competences and Occupations (ESCO) dictionary but an enhanced and much-improved result when adding an inductively constructed dictionary of the national labour market. Using this method would allow a new input of information to be incorporated into labour market information systems that would enable better decisions to be made by the various actors in the labour market.

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FIND: Privacy-Enhanced Federated Learning for Intelligent Fake News Detection

Lian,

Zhang,

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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An LSTM-Based Fake News Detection System Using Word Embeddings-Based Feature Extraction

Cited by 4 publications

References 6 publications

Real-Time Intersection Turning Movement Flow Forecasting Using a Parallel Bidirectional Long Short-Term Memory Neural Network Model

Real-Time Intersection Turning Movement Flow Forecasting Using a Parallel Bidirectional Long Short-Term Memory Neural Network Model

But which skills?

FIND: Privacy-Enhanced Federated Learning for Intelligent Fake News Detection

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