An Overview of Different Types of Recommendations Systems - A Survey

Duraisamy, Premkumar; Yuvaraj, S.; Natarajan, Yuvaraj; Niranjani, V.

doi:10.1109/icitiit57246.2023.10068631

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…Collaborative filtering analyzes patterns of past user behavior to recommend artworks that similar users like, while content-based filtering recommends art based on the characteristics of artworks the user has expressed interest in. Hybrid models combine both approaches, refining recommendations to provide a more nuanced and personalized art discovery experience [6,7].…”

Section: Recommendation Systems In Art Personalizationmentioning

confidence: 99%

Personalization of Visual Content of Interactive Art in Augmented Reality Based on Individual User Preferences

Kuliahin

2024

CNCS

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Topicality. In connection with the development of AR technologies and their use in interactive art, there is a growing need to develop methods of personalizing visual content, focused on the individual preferences of users. Research methods. Neural collaborative filtering method, generalized matrix factorization method, mood analysis on video. The purpose of the article: Researching the possibilities of improving the personalization of visual content in interactive art by evaluating the emotional reactions of users and their implicit feedback. The results obtained. The application of neural collaborative filtering and generalized matrix factorization to create adapted visual content in interactive art in AR was considered, which will significantly increase the relevance and immersion of users in interactive works. Conclusion. The considered approach can be used to improve immersiveness and personalization during user interaction with interactive art in AR.

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Section: Recommendation Systems In Art Personalizationmentioning

confidence: 99%

Personalization of Visual Content of Interactive Art in Augmented Reality Based on Individual User Preferences

Kuliahin

2024

CNCS

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“…Figure 6 shows the layers of the CNN model. These methodologies demonstrate exceptional performance in various tasks, including the categorization of images, the identification of objects, and the division of images into different segments (23) . This is primarily due to their innate ability to extract hierarchical representations of features from visual data.…”

Section: Model Architecturementioning

confidence: 99%

Transforming Sign Language into Text and Speech through Deep Learning Technologies

Duraisamy,

Abinayasrijanani,

Candida

et al. 2023

IJST

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Objective:The goal of the proposed work is to leverage deep learning technologies to create an efficient and accurate system for transforming sign language into text and speech. People deliver their ideas, feelings, and experiences to others around them via their interactions with each other. The hand gesture plays a significant role since it reflects the user's thoughts more rapidly than other motions (head, face, eye, and body). For deaf-mute people with disabilities, this is still not the case. Sign language facilitates communication among deaf-mute individuals. An individual who is deafmute can communicate without the use of acoustic noises by using sign language. Methods: Convolutional neural networks (CNNs) are generally used to recognize and extract characteristics from sign language motions. These neural networks are employed to recognize and extract critical features from sign language gestures. These features are processed by natural language processing models for textual translation. Finally, neural text-to-speech (TTS) technology is used to translate the textual translations into synthesized speech, thereby bridging the communication gap for the Deaf community. To establish an inclusive and accessible communication system, this technique combines computer vision, natural language processing, and speech synthesis. Findings: The datasets used in this technique include hand gesture images, which contain different hand poses and expressions. It is used to train and assess the model. The experiment findings show an accuracy of 97.6% with a precision of 94.1%, a recall of 96.8%, and an F1-score of 95.9%. Novelty: This approach displays a cogent translation from text to speech and achieves an outstanding translation accuracy of 97.6% from sign language to text, producing a natural and understandable output.

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Twitter Disaster Prediction Using Different Deep Learning Models

Duraisamy,

Natarajan

2024

SN COMPUT. SCI.

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An Overview of Different Types of Recommendations Systems - A Survey

Cited by 8 publications

References 23 publications

Personalization of Visual Content of Interactive Art in Augmented Reality Based on Individual User Preferences

Personalization of Visual Content of Interactive Art in Augmented Reality Based on Individual User Preferences

Transforming Sign Language into Text and Speech through Deep Learning Technologies

Twitter Disaster Prediction Using Different Deep Learning Models

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