An efficient spam detector model for accurate categorization of spam tweets using quantum chaotic optimization-based stacked recurrent network

Babu, R.; Kannappan, Jayashree; Krishna, Brahmadesam Viswanathan; Vijay, K.

doi:10.1007/s11071-023-08697-z

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…In addition, a bar plot is used to estimate the importance of those features, and heatmaps and bar graphs are used to visualize the confusion matrix and recall, respectively. The architectural layout of the proposed work is shown in Figure 1 [15][16][17][18].…”

Section: Proposed Methodologymentioning

confidence: 99%

An Elderly Fall Detection System Using Enhanced Random Forest in Machine Learning

Subburam,

Chandralekha,

Kandasamy

2024

RAiSE-2023

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Section: Proposed Methodologymentioning

confidence: 99%

An Elderly Fall Detection System Using Enhanced Random Forest in Machine Learning

Subburam,

Chandralekha,

Kandasamy

2024

RAiSE-2023

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“…CNNs were used to identify images and extract features. These days, we simulate time sequences, events, and other phenomena using Recurrent Neural Networks [18]. Between the input and output layers of a CNN, there are several hidden layers.…”

Section: Proposed Workmentioning

confidence: 99%

Detection of Female Anopheles Mosquito-Infected Cells: Exploring CNN, ReLU, and Sigmoid Activation Methods

Leena Jenifer,

Indumathi,

Mahalakshmi

2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Deep learning uses multi-layer neural networks where the algorithm decides for itself whether aspects are essential for analysis based on the raw input. In general, deep learning networks get better as more data is used to train them. For a variety of applications, convolutional neural networks are frequently used to analyse, categorize, and detect images. OBJECTIVES: The proposed system technique is used for automated analysis of malaria-detecting frameworks. A female Anopheles mosquito bite is the primary method of transmission of the blood disease malaria. It is still common to manually count and identify parasitized cells during microscopic examination of either thick or thin layers of haemoglobin, which takes time for disease prognosis. METHODS: The current research uses a neural network based on convolution to catalogue images of cells with and without malaria infection. This method improves the precision of classification for the datasets under study. The ReLu activation function used by this model enables it to learn more quickly and perform more effectively. RESULTS: The prediction of infected and healthy cells was done accurately by the proposed model, which uses only 3 layers of convolution, and this was the idea behind the implementation. The model achieved an improved accuracy of 99.77% across 12 iterations (epochs). CONCLUSION: The proposed model is straightforward and successful in differentiating between malaria-infected and uninfected cells.

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Image Captioning System for Movie Subtitling Using Neural Networks and LSTM

Vijay,

Manohar,

Saiganesh

et al. 2024

Proceedings of Third International Conference on Computing and Communication Networks

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An efficient spam detector model for accurate categorization of spam tweets using quantum chaotic optimization-based stacked recurrent network

Cited by 8 publications

References 19 publications

An Elderly Fall Detection System Using Enhanced Random Forest in Machine Learning

An Elderly Fall Detection System Using Enhanced Random Forest in Machine Learning

Detection of Female Anopheles Mosquito-Infected Cells: Exploring CNN, ReLU, and Sigmoid Activation Methods

Image Captioning System for Movie Subtitling Using Neural Networks and LSTM

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