Optimization of LightGBM for Song Suggestion Based on Users’ Preferences DOI Open Access
Ömer Mintemur

Journal of Intelligent Systems Theory and Applications, Journal Year: 2024, Volume and Issue: 7(2), P. 56 - 65

Published: Sept. 24, 2024

Undoubtedly, music possesses the transformative ability to instantly influence an individual's mood. In era of incessant flow substantial data, novel compositions surface on hourly basis. It is impossible know for individual whether he/she will like song or not before listening. Moreover, cannot keep up with this flow. However, help Machine Learning (ML) techniques, process can be eased. study, a dataset presented, and suggestion problem was treated as binary classification problem. Unlike other datasets, presented solely based users' preferences, indicating likeness specified by user. The LightGBM algorithm, along two ML algorithms, Extra Tree Random Forest, selected comparison. These algorithms were optimized using three swarm-based optimization algorithms: Grey Wolf, Whale, Particle Swarm optimizers. Results indicated that attributes new effectively discriminated songs. Furthermore, algorithm demonstrated superior performance compared employed in study.

Language: Английский

AD-YOLOv5: An object detection approach for key parts of sika deer based on deep learning DOI
Haitao Xiong, Ying Xiao, Zhao Hai-ping

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 217, P. 108610 - 108610

Published: Jan. 8, 2024

Language: Английский

Citations

8
Application of novel hybrid deep learning architectures combining convolutional neural networks (CNN) and recurrent neural networks (RNN): construction duration estimates prediction considering preconstruction uncertainties DOI Creative Commons

Belachew Asteray Demiss,

Walied A. Elsaigh

Engineering Research Express, Journal Year: 2024, Volume and Issue: 6(3), P. 032102 - 032102

Published: Aug. 20, 2024

Abstract Construction duration estimation plays a pivotal role in project planning and management, yet it is often fraught with uncertainties that can lead to cost overruns delays. To address these challenges, this review article proposes three advanced conceptual models leveraging hybrid deep learning architectures combine Convolutional Neural Networks (CNNs) Recurrent (RNNs) while considering construction delivery uncertainties. The first model introduces Spatio-Temporal Attention CNN-RNN Hybrid Model Probabilistic Uncertainty Modeling, which integrates attention mechanisms probabilistic uncertainty modeling provide accurate estimates of duration, offering insights into critical areas uncertainty. second presents Multi-Modal Graph Bayesian Integration, harnesses multi-modal data sources graph representations offer comprehensive incorporating measures, facilitating informed decision-making optimized resource allocation. Lastly, the third Hierarchical Transformer Fuzzy Logic Handling, addresses inherent vagueness imprecision by hierarchical spatio-temporal transformer architecture fuzzy logic handling, leading more nuanced adaptable management practices. These represent significant advancements addressing providing valuable recommendations for future research industry applications. Moreover, critically examines application architectures, specifically combination CNNs RNNs, predicting at preconstruction stage systems.

Language: Английский

Citations

8
Development Status, Frontier Hotspots, and Technical Evaluations in the Field of AI Music Composition Since the 21st Century: A Systematic Review DOI Creative Commons
Weijia Yang, L. Shen, Chih-Fang Huang

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 89452 - 89466

Published: Jan. 1, 2024

Language: Английский

Citations

6
Fault Reconstruction Method of Neural Network Observer Group for High-Speed Vehicle DOI

Cong Li,

Yibo Ding, Cheng Bi

et al.

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 292 - 301

Published: Jan. 1, 2025

Language: Английский

Citations

0
Dense dynamic convolutional network for Bel canto vocal technique assessment DOI Creative Commons
Zhiyong Hou, Xu Zhao, Sheng Jiang

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 5, 2025

The Bel Canto performance is a complex and multidimensional art form encompassing pitch, timbre, technique, affective expression. To accurately reflect performer's singing proficiency, it essential to quantify evaluate their vocal technical execution precisely. Convolutional Neural Networks (CNNs), renowned for robust ability capture spatial hierarchical information, have been widely adopted in various tasks, including audio pattern recognition. However, existing CNNs exhibit limitations extracting intricate spectral features, particularly performance. address the challenges posed by features meet demands objective technique assessment, we introduce Omni-Dimensional Dynamic Convolution (ODConv). Additionally, employ densely connected layers optimize framework, enabling efficient utilization of multi-scale across multiple dynamic convolution layers. validate effectiveness our method, conducted experiments on tasks music classification, acoustic scene sound event detection. experimental results demonstrate that Dense Network (DDNet) outperforms traditional CNN Transformer models, achieving 90.11%, 73.95%, 89.31% (Top-1 Accuracy), 41.89% (mAP), respectively. Our research not only significantly improves accuracy efficiency assessment but also facilitates applications teaching remote education.

Language: Английский

Citations

0
Lightweight Deep-Learning Based Music Genre Classification: A Study DOI
A. Rama,

N. Mythili,

M. P. Rajakumar

et al.

2021 International Conference on System, Computation, Automation and Networking (ICSCAN), Journal Year: 2023, Volume and Issue: unknown, P. 1 - 5

Published: Nov. 17, 2023

Deep-learning (DL) applications that are used real-time across various industries have gained a lot of traction and become increasingly popular, especially when it comes to data-driven recommendation systems. This work aims develop DL scheme support the music-recommendation system (MS) based on music data. The phases this includes; (i) data collection signal-image conversion get necessary RGB scale images from data, (ii) pre-trained feature extraction, (iii) deep-features detection recommend appropriate music. research considered classic- (CL) pop-music (PO) for examination achieved results evaluated substantiate performance arrangement. In work, procedure is implemented convert 1D signal 2D image then examined using proposed technique. experimental outcome separately presented spectrogram synchro-extracting-transform obtained presented. investigation with MobileNet variants study authorizes better MobileNetV2 (>99%) compared other schemes in study.

Language: Английский

Citations

1
Optimization of LightGBM for Song Suggestion Based on Users’ Preferences DOI Open Access
Ömer Mintemur

Journal of Intelligent Systems Theory and Applications, Journal Year: 2024, Volume and Issue: 7(2), P. 56 - 65

Published: Sept. 24, 2024

Undoubtedly, music possesses the transformative ability to instantly influence an individual's mood. In era of incessant flow substantial data, novel compositions surface on hourly basis. It is impossible know for individual whether he/she will like song or not before listening. Moreover, cannot keep up with this flow. However, help Machine Learning (ML) techniques, process can be eased. study, a dataset presented, and suggestion problem was treated as binary classification problem. Unlike other datasets, presented solely based users' preferences, indicating likeness specified by user. The LightGBM algorithm, along two ML algorithms, Extra Tree Random Forest, selected comparison. These algorithms were optimized using three swarm-based optimization algorithms: Grey Wolf, Whale, Particle Swarm optimizers. Results indicated that attributes new effectively discriminated songs. Furthermore, algorithm demonstrated superior performance compared employed in study.

Language: Английский

Citations

0