Fake news detection: A RNN-LSTM, Bi-LSTM based deep learning approach

Mahara, Govind Singh; Gangele, Sharad

doi:10.1109/icdds56399.2022.10037403

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…Predicting task workload is a typical time series forecasting problem, and the deep learning model LSTM (long-short-term memory) can effectively handle it [26]. An LSTM network consists of two data gates to capture the sequence information, i.e., the update gate and the forget gate.…”

Section: Mec Servers Leverage Lstm-based Multi-step Prediction Mechan...mentioning

confidence: 99%

Task Offloading and Resource Optimization Based on Predictive Decision Making in a VIoT System

Lv,

Wang,

Wang

et al. 2024

Electronics

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With the exploration of next-generation network technology, visual internet of things (VIoT) systems impose significant computational and transmission demands on mobile edge computing systems that handle large amounts of offloaded video data. Visual users offload specific tasks to cloud or edge computing platforms to meet strict real-time requirements. However, the available scheduling and computational resources for offloading tasks constantly destroy the system’s reliability and efficiency. This paper proposes a mechanism for task offloading and resource optimization based on predictive perception. Firstly, we proposed two LSTM-based decision-making prediction methods. In resource-constrained scenarios, we improve resource utilization by encouraging edge devices to participate in task offloading, ensuring the completion of more latency-sensitive request tasks, and enabling predictive decision-making for task offloading. We propose a polynomial time optimal mechanism for pre-emptive decision task offloading in resource-abundant scenarios. We solve the 0–1 knapsack problem of offloading tasks to better meet the demands of low-latency tasks where the system’s available resources are not constrained. Finally, we provide numerical results to demonstrate the effectiveness of our scheme.

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Section: Mec Servers Leverage Lstm-based Multi-step Prediction Mechan...mentioning

confidence: 99%

Task Offloading and Resource Optimization Based on Predictive Decision Making in a VIoT System

Lv,

Wang,

Wang

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…The authors in [15] proposed several approaches based on recurrent neural networks (RNN) using long short-term memory (LSTM), auto encoders and multi-layer perceptron. For the authors [16], deep learning LSTM is applied with the resampling of an imbalanced dataset.…”

Section: Related Workmentioning

confidence: 99%

Proposal of a Machine Learning-based Model to Optimize the Detection of Cyber-attacks in the Internet of Things

Seyed,

NGO,

KEBE

2023

IJACSA

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In this article, we propose a model to optimize the detection of attacks in IoT. IoT network is a promising technology that connects living and non-living things around the world. Despite the increased development of these technologies, cyber-attacks remains a weakness, making it vulnerable to numerous cyber-attacks. Of course, automatic computer intrusion detection systems are deployed. However, it does not make it possible to mobilize the full potential of Machine Learning. Our approach in this maneuver consists of offering a means to select the least expensive ML method in terms of learning in order to optimize the prediction of threats to introduce IoT objects. To do this, we make modular design based on two layers. The first module is a canvas containing the different methods most used in ML such as supervised learning method, unsupervised learning method and reinforcement learning method. The second module introduces a mechanism to measure the learning cost linked to each of these methods in order to choose the least expensive one in order to quickly and efficiently detect intrusions in IoT objects. To prove the validity of the proposed model, we simulated it using the Weka tool. The results obtained illustrate the following behaviors: The classification quality rate is 93.66%. This last result is supported by a classification consistency rate of 0.882 (close to unity 1) demonstrating a trend towards convergence between observation and prediction.

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“…We leverage Bi-Directional Gated Networks (BDGNs) to accurately and efficiently classify cognitive workload levels. We intend to develop a hybrid methodology integrating deep learning models, including LSTM and GRU [25,26], as a BDGN framework. By leveraging these models and the STEW dataset [27], our goal is to attain high accuracy in classifying various levels of cognitive workload.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Workload Classification in Industry 5.0 Applications: Electroencephalography-Based Bi-Directional Gated Network Approach

Afzal,

Gu,

Afzal

et al. 2023

Electronics

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In the era of Industry 5.0, effectively managing cognitive workload is crucial for optimizing human performance and ensuring operational efficiency. Using an EEG-based Bi-directional Gated Network (BDGN) approach, this study tries to figure out how to classify cognitive workload in Industry 5.0 applications. The proposed approach incorporates LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) models in a hybrid architecture to leverage their complementary strengths. This research highlights the utilization of the developed model alongside the MQTT (Message Queuing Telemetry Transport) protocol to facilitate real-time end-to-end data transmission. The deployed AI model performs the classification of cognitive workload based on the received data. The main findings of this research reveal an impressive accuracy of 98% in cognitive workload classification, validating the efficacy of the suggested BDGN approach. This study emphasizes the significance of leveraging EEG-based approaches in Industry 5.0 applications for cognitive workload management.

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Fake news detection: A RNN-LSTM, Bi-LSTM based deep learning approach

Cited by 9 publications

References 12 publications

Task Offloading and Resource Optimization Based on Predictive Decision Making in a VIoT System

Task Offloading and Resource Optimization Based on Predictive Decision Making in a VIoT System

Proposal of a Machine Learning-based Model to Optimize the Detection of Cyber-attacks in the Internet of Things

Cognitive Workload Classification in Industry 5.0 Applications: Electroencephalography-Based Bi-Directional Gated Network Approach

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