Visual and buying sequence features-based product image recommendation using optimization based deep residual network

Indira, D. N. V. S. L. S.; Markapudi, Babu Rao; Chaduvula, Kavitha; F, Rathna Jyothi Chaduvula

doi:10.1016/j.gep.2022.119261

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…6. Model 6 (Indira et al, 2022) proposed an innovative optimization-driven deep residual network integrated CNN, Elephant Herding Feedback Artificial Optimization (EHFAO), and k-means algorithms.…”

Section: Datasets and Baseline Modelsmentioning

confidence: 99%

The Intelligent Advertising Image Generation Using Generative Adversarial Networks and Vision Transformer

Zhang,

Qu,

Long

et al. 2024

Journal of Organizational and End User Computing

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With the continuous evolution of digital marketing, the generation of advertising images has become crucial in capturing user interest and enhancing advertising effectiveness. However, existing methods face limitations in meeting the diverse and creative demands of advertising content, necessitating innovative algorithms to improve advertising generation outcomes. In addressing these challenges, this study proposes a deep learning algorithm framework that cleverly integrates a generative adversarial network and an VGG-based visual transformer model to enhance the effectiveness of advertising image generation. Systematic experimentation shows that the model proposed in this article achieves an AUC metric value of more than 0.7 on several datasets. The results of the experiments demonstrate that the novel algorithm significantly improves the attractiveness of advertising content, particularly showcasing substantial benefits in website operations during online evaluation experiments.

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Section: Datasets and Baseline Modelsmentioning

confidence: 99%

The Intelligent Advertising Image Generation Using Generative Adversarial Networks and Vision Transformer

Zhang,

Qu,

Long

et al. 2024

Journal of Organizational and End User Computing

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show abstract

“…Deep residual networks address network degradation using residual learning with identity connections [ 99 ]. CNN-LSTM provides solutions to complex problems with large amounts of data [ 96 ].…”

Section: Deep Neural Network-based Approachmentioning

confidence: 99%

A Survey on Artificial Intelligence in Posture Recognition

Jiang¹,

Hu²,

Wang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Over the years, the continuous development of new technology has promoted research in the field of posture recognition and also made the application field of posture recognition have been greatly expanded. The purpose of this paper is to introduce the latest methods of posture recognition and review the various techniques and algorithms of posture recognition in recent years, such as scale-invariant feature transform, histogram of oriented gradients, support vector machine (SVM), Gaussian mixture model, dynamic time warping, hidden Markov model (HMM), lightweight network, convolutional neural network (CNN). We also investigate improved methods of CNN, such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and high-resolution nets. The general process and datasets of posture recognition are analyzed and summarized, and several improved CNN methods and three main recognition techniques are compared. In addition, the applications of advanced neural networks in posture recognition, such as transfer learning, ensemble learning, graph neural networks, and explainable deep neural networks, are introduced. It was found that CNN has achieved great success in posture recognition and is favored by researchers. Still, a more in-depth research is needed in feature extraction, information fusion, and other aspects. Among classification methods, HMM and SVM are the most widely used, and lightweight network gradually attracts the attention of researchers. In addition, due to the lack of 3D benchmark data sets, data generation is a critical research direction.

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Attention-Driven Fusion of Pre-Trained Model Features for Superior Recommender Systems

Buradagunta,

Balakrishna

2024

J. Inst. Eng. India Ser. B

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Visual and buying sequence features-based product image recommendation using optimization based deep residual network

Cited by 5 publications

References 24 publications

The Intelligent Advertising Image Generation Using Generative Adversarial Networks and Vision Transformer

The Intelligent Advertising Image Generation Using Generative Adversarial Networks and Vision Transformer

A Survey on Artificial Intelligence in Posture Recognition

Attention-Driven Fusion of Pre-Trained Model Features for Superior Recommender Systems

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