Enhancing multimedia management: cloud-based movie type recognition with hybrid deep learning architecture DOI Creative Commons

Fangru Lin,

Jie Yuan, Zhiwei Chen

et al.

Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: May 17, 2024

Abstract Film and movie genres play a pivotal role in captivating relevant audiences across interactive multimedia platforms. With focus on entertainment, streaming providers are increasingly prioritizing the automatic generation of within cloud-based media services. In service management, integration hybrid convolutional network proves to be instrumental effectively distinguishing between diverse array video genres. This classification process not only facilitates more refined recommendations content filtering but also enables targeted advertising. Furthermore, given frequent amalgamation components from various cinema, there arises need for social networks incorporate real-time mechanisms accurate genre identification. this study, we propose novel architecture leveraging deep learning techniques detection films. Our approach entails utilization bidirectional long- short-term memory (BiLSTM) network, augmented with descriptors extracted EfficientNet-B7, an ImageNet pre-trained neural (CNN) model. By employing BiLSTM, acquires robust representations proficiently categorizes movies into multiple Evaluation LMTD dataset demonstrates substantial improvement performance classifier system achieved by our proposed architecture. Notably, achieves both computational efficiency precision, outperforming even most sophisticated models. Experimental results reveal that EfficientNet-BiLSTM precision rate 93.5%. attains state-of-the-art performance, as evidenced its F1 score 0.9012.

Language: Английский

Edge intelligence-assisted animation design with large models: a survey DOI Creative Commons

Jing Zhu,

Chuanjiang Hu,

Edris Khezri

et al.

Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: Feb. 21, 2024

Abstract The integration of edge intelligence (EI) in animation design, particularly when dealing with large models, represents a significant advancement the field computer graphics and animation. This survey aims to provide comprehensive overview current state future prospects EI-assisted focusing on challenges opportunities presented by model implementations. Edge intelligence, characterized its decentralized processing real-time data analysis capabilities, offers transformative approach handling computational data-intensive demands modern paper explores various aspects EI then delves into specifics models animation, examining their evolution, trends, inherent implementation. Finally, addresses solutions integrating proposing research directions. serves as valuable resource for researchers, animators, technologists, offering insights potential revolutionizing design opening new avenues creative efficient production.

Language: Английский

Citations

34
Personalized Self‐Directed Learning Recommendation System Based on Social Knowledge in Distributed Web DOI Open Access

Baoqing Tai,

Yang Xun,

Ju Chong

et al.

Concurrency and Computation Practice and Experience, Journal Year: 2025, Volume and Issue: 37(6-8)

Published: March 13, 2025

ABSTRACT Personalized self‐directed learning recommender systems help users manage their paths more effectively. This paper proposed a personalized recommendation system based on social knowledge in cloud‐supported web databases. The leverages Long Short‐Term Memory (LSTM) neural networks and Graph Attention Networks (GAT) to enhance the accuracy effectiveness of recommendations. LSTM network is used for modeling temporal sequences activities, while Network employed extract from interactions relationships among users. By combining these two models, can provide precise recommendations Experimental results demonstrate that this improve efficiency by delivering appropriate timely content, thereby enhancing experience. use cloud databases also ensures easy access high scalability over distributed web.

Language: Английский

Citations

0
Edge-Enabled Personalized Fitness Recommendations and Training Guidance for Athletes with Privacy Preservation DOI

Yuncheng Li,

Cong Li, Fan Wang

et al.

Information Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 122032 - 122032

Published: Feb. 1, 2025

Language: Английский

Citations

0
Fine‐Grained Dance Style Classification Using an Optimized Hybrid Convolutional Neural Network Architecture for Video Processing Over Multimedia Networks DOI Creative Commons

Na Guo,

Ahong Yang,

Yan Wang

et al.

International Journal of Intelligent Systems, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Dance style recognition through video analysis during university training can significantly benefit both instructors and novice dancers. Employing in offers substantial advantages, including the potential to train future dancers using innovative technologies. Over time, intricate dance gestures be honed, reducing burden on who would, otherwise, need provide repetitive demonstrations. Recognizing dancers’ movements, evaluating adjusting their gestures, extracting cognitive functions for efficient evaluation classification are pivotal aspects of our model. Deep learning currently stands as one most effective approaches achieving these objectives, particularly with short clips. However, limited research has focused automated videos purposes assisting instructors. In addition, assessing quality accuracy performance recordings presents a complex challenge, especially when judges cannot fully focus on‐stage performance. This paper proposes an alternative manual video‐based approach assessment. By utilizing clips, we conduct employing techniques such fine‐grained frames, convolutional neural networks (CNNs) channel attention mechanisms (CAMs), autoencoders (AEs). These methods enable accurate data gathering, leading precise conclusions. Furthermore, cloud space real‐time processing frames is essential timely styles, enhancing efficiency information processing. Experimental results demonstrate effectiveness method terms F1‐score calculation, exceeding 97.24% reaching 97.30%. findings corroborate efficacy precision analysis.

Language: Английский

Citations

0
Target tracking using video surveillance for enabling machine vision services at the edge of marine transportation systems based on microwave remote sensing DOI Creative Commons
Meiyan Li, Qinyong Wang, Yuwei Liao

et al.

Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: Feb. 19, 2024

Abstract Automatic target tracking in emerging remote sensing video-generating tools based on microwave imaging technology and radars has been investigated this paper. A moving system is proposed to be low complexity fast for implementation through edge nodes a mini-satellite or drone network enabling machine intelligence into large-scale vision systems, particular, marine transportation systems. The uses group of image processing video pre-processing, Kalman filtering do the main task. For testing performance, two measures accuracy false alarms probability are computed real data. Two types scenes analyzed including scene with single target, multiple targets that more complicated automatic detection achieved high performance our tests.

Language: Английский

Citations

1
Students health physique information sharing in publicly collaborative services over edge-cloud networks DOI Creative Commons
Ping Liu, Shi Dai,

Bin Zang

et al.

Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: May 9, 2024

Abstract Data privacy is playing a vital role while facing the digital life aspects. Today, world being extensively inter-connected through internet of things (IoT) technologies. This huge interconnectivity bringing very wonderful capabilities for improving quality (QoL) with itself, instance, in distributed healthcare. On other hand, there are new challenges per use. One most challenging issues IoT use social systems and secure, trustable, reliable interactions over networks such that safety, security, both aspects cyber physical worlds humankind should be planned controlled. Due to less activity people current world, fitness aerobic sports now an important need at any age help them keep healthy their cyber-physical life, specifically, younger student still growth ages. However, these sport activities monitored seriously closely not put danger. Herewith, healthcare services becoming more applicable. Therefore, health information athletes hot topic investigation as we present here. We propose IoT-based physique system considering private sharing based on data hiding edge collaborative system. The proposed pays attention key factors infrastructure but it its suggestions safety. Moreover, many evaluations different kinds provided.

Language: Английский

Citations

0
Enhancing multimedia management: cloud-based movie type recognition with hybrid deep learning architecture DOI Creative Commons

Fangru Lin,

Jie Yuan, Zhiwei Chen

et al.

Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: May 17, 2024

Abstract Film and movie genres play a pivotal role in captivating relevant audiences across interactive multimedia platforms. With focus on entertainment, streaming providers are increasingly prioritizing the automatic generation of within cloud-based media services. In service management, integration hybrid convolutional network proves to be instrumental effectively distinguishing between diverse array video genres. This classification process not only facilitates more refined recommendations content filtering but also enables targeted advertising. Furthermore, given frequent amalgamation components from various cinema, there arises need for social networks incorporate real-time mechanisms accurate genre identification. this study, we propose novel architecture leveraging deep learning techniques detection films. Our approach entails utilization bidirectional long- short-term memory (BiLSTM) network, augmented with descriptors extracted EfficientNet-B7, an ImageNet pre-trained neural (CNN) model. By employing BiLSTM, acquires robust representations proficiently categorizes movies into multiple Evaluation LMTD dataset demonstrates substantial improvement performance classifier system achieved by our proposed architecture. Notably, achieves both computational efficiency precision, outperforming even most sophisticated models. Experimental results reveal that EfficientNet-BiLSTM precision rate 93.5%. attains state-of-the-art performance, as evidenced its F1 score 0.9012.

Language: Английский

Citations

0