Detecting Fake News Over Job Posts via Bi-Directional Long Short-Term Memory (BIDLSTM)

Divya, T V; Banik, Barnali Gupta

doi:10.4018/ijwltt.287096

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…One of the significant challenges with these models is their inability to simultaneously process information across different time scales, missing both granular short-term variations and overarching long-term trends. Additionally, selecting hyperparameters in models like LSTMs can be complex, often requiring exhaustive trial-and-error methods [9]. To address the above limitations, this work proposed the DWT-DE-LSTM model.…”

Section: Introductionmentioning

confidence: 99%

Using Optimized Long Short-Term Memory For Time-Series Forecasting of Electric Vehicles Battery Charging

Figueroa,

Ghanimi,

et al. 2024

JMC

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The last decade has seen a significant rise in the adoption and development of Electric Vehicles (EVs), driven by environmental concerns, technological advancements, and governmental support. Batteries, central to EVs, have witnessed groundbreaking innovations in terms of energy density, charging speeds, and longevity. Expanding charging infrastructure and the automotive industry's investment in EV research have made them more mainstream. Effective Battery Management (BM), which includes monitoring essential parameters and thermal management, is critical for the longevity and reliability of EVs. Accurate charge prediction, in particular, aids in trip planning, reduces range anxiety and facilitates cost-effective charging coordinated with dynamic electricity pricing. Traditional models like linear regression and Autor-Rgressive Integrated Moving Average (ARIMA) have been standard for EV battery charge prediction. However, these often struggle with the dynamic nature of EV charging data. Even models like the vanilla Long Short-Term Memory (LSTM), which are adept at recognizing long-term patterns, require meticulous hyperparameter tuning. This work introduces the DWT-DE-LSTM model, which utilizes the Discrete Wavelet Transform (DWT) to dissect battery charging data at different resolutions and a Differential Evolution (DE) strategy for model optimization. Tests using the Panasonic 18650PF Li-ion Battery Dataset revealed the superior efficacy of the DWT-DE-LSTM model, emphasizing its suitability for real-world battery charge prediction.

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Section: Introductionmentioning

confidence: 99%

Using Optimized Long Short-Term Memory For Time-Series Forecasting of Electric Vehicles Battery Charging

Figueroa,

Ghanimi,

et al. 2024

JMC

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“…The third and important approach is the usage of GAN models, an unsupervised technique that can generate new images by learning from the patterns that exist in between the input image. GAN's consists of two components: generator to create new images by training the model and discriminator for classification purpose [12,13]. In GAN's, the generator takes input as a fixed-length vector, which is known as "Noise Vector" for producing the salt and pepper noise images because most of the plants have smoked layer above them.…”

Section: Introductionmentioning

confidence: 99%

Data Augmentation Techniques on Chilly Plants to Classify Healthy and Bacterial Blight Disease Leaves

Govathoti¹,

Reddy²,

Kamidi³

et al. 2022

IJACSA

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Designing an automation system for the agriculture sector is difficult using machine learning approach. So many researchers proposed deep learning system which requires huge amount of data for training the system. The proposed system suggests that geometric transformations on the original dataset help the system to generate more images that can replicate the physical circumstances. This process is known as "Image Augmentation". This enhancement of data helps the system to produce more accurate systems in terms of all metrics. In olden days when researchers work with machine learning techniques they used to implement traditional approaches which are a time consuming and expensive process. In deep learning, most of the operations are automatically taken care by the system. So, the proposed system applies neural style and to classify the images it uses the concept of transfer learning. The system utilizes the images available in the open source repository known as "Kaggle", this majorly consists of images related to chilly, tomato and potato. But this system majorly focuses on chilly plants because it is most productive plant in the South Indian regions. Image augmentation creates new images in different scenarios using the existing images and by applying popular deep learning techniques. The model has chosen ResNet-50, which is a pretrained model for transfer learning. The advantage of using pretrained model lies in not to develop the model from scratch. This pre-trained model gives more accuracy with less number of epochs. The model has achieved an accuracy of "100%".

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Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks

Almahadin,

Subburaj,

Hiari

et al. 2024

SN COMPUT. SCI.

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Detecting Fake News Over Job Posts via Bi-Directional Long Short-Term Memory (BIDLSTM)

Cited by 6 publications

References 14 publications

Using Optimized Long Short-Term Memory For Time-Series Forecasting of Electric Vehicles Battery Charging

Using Optimized Long Short-Term Memory For Time-Series Forecasting of Electric Vehicles Battery Charging

Data Augmentation Techniques on Chilly Plants to Classify Healthy and Bacterial Blight Disease Leaves

Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks

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