Artificial intelligence in deep learning algorithms for multimedia analysis

Jeon, Gwanggil; Anisetti, Marco; Damiani, Ernesto; Kantarcı, Burak

doi:10.1007/s11042-020-09232-7

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…Significant advances are being made in several areas thanks to deep learning (DL), including text processing ( Nassif et al, 2021 ) and image recognition ( Coccia, 2020 ). Traditional machine learning algorithms have failed or shown mediocre performance ( Jeon et al, 2020 ). Still, DL algorithms are being developed because they can tackle complex artificial intelligence tasks like speech recognition or object identification.…”

Section: Related Workmentioning

confidence: 99%

Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems

Angamuthu,

Trojovský

2023

PeerJ Computer Science

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Expert assessments with pre-defined numerical or language terms can limit the scope of decision-making models. We propose that decision-making models can incorporate expert judgments expressed in natural language through sentiment analysis. To help make more informed choices, we present the Sentiment Analysis in Recommender Systems with Multi-person, Multi-criteria Decision Making (SAR-MCMD) method. This method compiles the opinions of several experts by analyzing their written reviews and, if applicable, their star ratings. The growth of online applications and the sheer amount of available information have made it difficult for users to decide which information or products to select from the Internet. Intelligent decision-support technologies, known as recommender systems, leverage users’ preferences to suggest what they might find interesting. Recommender systems are one of the many approaches to dealing with information overload issues. These systems have traditionally relied on single-grading algorithms to predict and communicate users’ opinions for observed items. To boost their predictive and recommendation abilities, multi-criteria recommender systems assign numerous ratings to various qualities of products. We created, manually annotated, and released the technique in a case study of restaurant selection using ‘TripAdvisor reviews’, ‘TMDB 5000 movies’, and an ‘Amazon dataset’. In various areas, cutting-edge deep learning approaches have led to breakthrough progress. Recently, researchers have begun to focus on applying these methods to recommendation systems, and different deep learning-based recommendation models have been suggested. Due to its proficiency with sparse data in large data systems and its ability to construct complex models that characterize user performance for the recommended procedure, deep learning is a formidable tool. In this article, we introduce a model for a multi-criteria recommender system that combines the best of both deep learning and multi-criteria decision-making. According to our findings, the suggested system may give customers very accurate suggestions with a sentiment analysis accuracy of 98%. Additionally, the metrics, accuracy, precision, recall, and F1 score are where the system truly shines, much above what has been achieved in the past.

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Section: Related Workmentioning

confidence: 99%

Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems

Angamuthu,

Trojovský

2023

PeerJ Computer Science

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“…Modern artificial intelligence experts in China and other countries no longer use familiar patterns, expression subjects, and styles to attract people to love them [8]. However, many innovations are quite the contrary.…”

Section: Application Of Digital Multimedia Technology In Intelligent Systemmentioning

confidence: 99%

Artificial Intelligence Interactive Design System Based on Digital Multimedia Technology

Liu

2022

Advances in Multimedia

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The current extraction speed of artificial intelligence interactive elements is low, with the low effect, resulting in the poor effect of artificial intelligence interaction. Therefore, a new artificial intelligence interaction method has been developed for digital multimedia technology, performing analysis based on the current background of artificial intelligence, providing a good environment foundation for the interactive place, so that it can integrate artificial intelligence technology after interaction. Aiming at the current problems of digital multimedia technology in the use of artificial intelligence interaction design, innovative exploration of artificial intelligence technology will be carried out based on multimedia technology and innovative thinking. Based on the in-depth analysis of digital multimedia technology, the relationship between artificial intelligence technology and digital multimedia technology is analyzed, and an artificial intelligence interactive design system based on digital multimedia technology is proposed. Finally, the digital multimedia technology is verified on the artificial intelligence interactive design through case analysis.

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“…Li et al added a multiscale conditional random field (CRF) to the network structure to optimize the details of the prediction results by one [ 20 ]. Jeon et al were inspired by the traditional structure from motion approach and proposed a new idea to achieve unsupervised training of deep prediction networks [ 21 ]. With the powerful modeling ability of deep neural networks, these single-view depth prediction methods based on deep learning can obtain dense and smooth prediction results [ 22 , 23 ].…”

Section: Related Researchmentioning

confidence: 99%

Construction and Research of Constructive English Teaching Model Applying Multimodal Neural Network Algorithm

Zhang

Wang²

2022

Computational Intelligence and Neuroscience

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This article draws on previous research on constructive English teaching models and uses multimodal neural network algorithm theory and constructive English teaching as the theoretical basis, experimental research method, questionnaire survey method, and evaluation method. In this article, we propose a multimodal neural network consisting of a multiscale FCN module and an LSTM module. The network focuses on both the multiscale geometric spatial features and the numerical time-dependent features of the time series curves, and with the comprehensive knowledge of their characteristics, it can better distinguish the classes to which the series belong. A large-scale perceptual field is achieved by null convolution in the model to ensure that the training pressure does not increase significantly. A series of experiments on the UCR dataset verifies the effectiveness and superiority of the model. Simulation experiments were conducted to build the proposed constructive English teaching model based on a multimodal neural network algorithm, and a test environment was built for use case testing. The experimental results showed that the algorithm can be better applied to constructive English teaching and has better adaptability and accuracy in various scenarios. At the end of the experiment, a posttest of grammar level was conducted in two classes to test whether the constructive English teaching model based on the multimodal neural network model could help students improve their English grammar skills. The results of the data analysis showed that the mean score of the experimental class was significantly higher than that of the control class, and the experimental class showed a more significant improvement, indicating that this new constructive English teaching model was beneficial to improving students’ English grammar skills. The interaction strategy proposed under the constructive English teaching model can effectively improve the interaction between teachers and students. This positive feedback effect can provide front-line teachers with corresponding teaching references.

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Artificial intelligence in deep learning algorithms for multimedia analysis

Cited by 10 publications

References 23 publications

Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems

Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems

Artificial Intelligence Interactive Design System Based on Digital Multimedia Technology

Construction and Research of Constructive English Teaching Model Applying Multimodal Neural Network Algorithm

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