Nonlinear 2D Chaotic Map and DNA (NL2DCM-DNA) Sequences-Based Fast and Secure Block Image Encryption

Stalin, Shalini; Maheshwary, Priti; Shukla, Piyush Kumar

doi:10.1007/978-981-33-4367-2_8

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…When it comes to this HAC method, adjectives are considered to be the most essential factor, and the sum of all adjectives makes it easier to discover the perspective characteristics [ 8 , 9 ]. All of the reviews are dealt with one by one to determine the relative importance of the various points of view.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The use of deep learning with CNN has shown its effectiveness in the challenge of image-based opinion categorization by proving the benefit of learning in-depth characteristics and executing the learning process in a real-world context. When dealing with large datasets [ 8 , 9 ], the classification accuracy improves as a consequence of the increased processing power made accessible to the algorithm by the increased processing capacity [ 9 ]. Because it requires a significant commitment of both time and money, opinion analysis is usually seen as a difficult undertaking [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Sentiment Analysis of Image with Text Caption using Deep Learning Techniques

Chaubey¹,

Arora

Raj

et al. 2022

Computational Intelligence and Neuroscience

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People are actively expressing their views and opinions via the use of visual pictures and text captions on social media platforms, rather than just publishing them in plain text as a consequence of technical improvements in this field. With the advent of visual media such as images, videos, and GIFs, research on the subject of sentiment analysis has expanded to encompass the study of social interaction and opinion prediction via the use of visuals. Researchers have focused their efforts on understanding social interaction and opinion prediction via the use of images, such as photographs, films, and animated GIFs (graphics interchange formats). The results of various individual studies have resulted in important advancements being achieved in the disciplines of text sentiment analysis and image sentiment analysis. It is recommended that future studies investigate the combination of picture sentiment analysis and text captions in more depth, and further research is necessary for this field. An intermodal analysis technique known as deep learning-based intermodal (DLBI) analysis is discussed in this suggested study, which may be used to show the link between words and pictures in a variety of scenarios. It is feasible to gather opinion information in numerical vector form by using the VGG network. Afterward, the information is transformed into a mapping procedure. It is necessary to predict future views based on the information vectors that have been obtained thus far, and this is accomplished through the use of active deep learning. A series of simulation tests are being conducted to put the proposed mode of operation to the test. When we look at the findings of this research, it is possible to infer that the model outperforms and delivers a better solution with more accuracy and precision, as well as reduced latency and an error rate, when compared to the alternative model (the choice).

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Image with Text Caption using Deep Learning Techniques

Chaubey¹,

Arora

Raj

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…A multimodal sentiment analysis approach based on interactive transformers and soft mapping is presented in. [11] After data fusion, their model is able to fully account for the link between data from various modalities, which is useful for sentiment analysis. It has given equivalent findings on the CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) dataset; however, it still has significant issues.…”

Section: Related Workmentioning

confidence: 99%

FPN_GCT: Feature Pyramid Network Gated Channel Transformation for Automatic Image Sentiment Analysis

2024

IJIES

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The field of aspect-level sentiment classification has gained significant attention in recent years, but limited image datasets have hindered the effectiveness of neural network models. To address the issue, we present a novel approach, Feature Pyramid Network Gated Channel Transformation (FPN_GCT) that can automatically extract highly fine-grained sentiment information from images. Our approach utilizes Gated Channel Transformation (GCT) for accurate image sentiment analysis and incorporates ResNet and Reduced Layer to enhance the model's novelty. We validate the performance of our model on the Twitter and CrowdFlower sentiment analysis datasets and demonstrate that it outperforms existing techniques including VGG-19, DenseNet121, ResNet50V2, SVM, MemNet, and RAM, with an accuracy of 91.21 for the CrowdFlower dataset and 74.47 for the Twitter dataset. Our approach offers a substantial advancement in the field of image sentiment analysis and has the potential to enhance the accuracy of aspect-level sentiment classification tasks, paving the way for more accurate and efficient sentiment analysis in various applications.

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Nonlinear 2D Chaotic Map and DNA (NL2DCM-DNA) Sequences-Based Fast and Secure Block Image Encryption

Cited by 2 publications

References 8 publications

Sentiment Analysis of Image with Text Caption using Deep Learning Techniques

Sentiment Analysis of Image with Text Caption using Deep Learning Techniques

FPN_GCT: Feature Pyramid Network Gated Channel Transformation for Automatic Image Sentiment Analysis

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