Advanced AI techniques for video classification: a comprehensive framework using multiple feature extraction and classification methods
Na minha lista:
| Publicado no: | Journal of Electrical Systems and Information Technology vol. 12, no. 1 (Dec 2025), p. 97 |
|---|---|
| Autor principal: | |
| Outros Autores: | , |
| Publicado em: |
Springer Nature B.V.
|
| Assuntos: | |
| Acesso em linha: | Citation/Abstract Full Text Full Text - PDF |
| Tags: |
Sem tags, seja o primeiro a adicionar uma tag!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3276607581 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2314-7172 | ||
| 024 | 7 | |a 10.1186/s43067-025-00295-w |2 doi | |
| 035 | |a 3276607581 | ||
| 045 | 2 | |b d20251201 |b d20251231 | |
| 100 | 1 | |a Khairy, Mayada |u Electronics Research Institute, Cairo, Egypt (GRID:grid.463242.5) (ISNI:0000 0004 0387 2680) | |
| 245 | 1 | |a Advanced AI techniques for video classification: a comprehensive framework using multiple feature extraction and classification methods | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The increasing popularity of multimedia applications, such as video classification, has underscored the need for efficient methods to manage and categorize vast video datasets. Video classification simplifies video categorization, enhancing searchability and retrieval by leveraging distinctive features extracted from textual, audio, and visual components. This paper introduces an automated video recognition system that classifies video content based on motion types (low, medium, and high) derived from visual component characteristics. The proposed system utilizes advanced artificial intelligence techniques with four feature extraction methods; MFCC alone, (2) MFCC after applying DWT, (3) denoised MFCC, and (4) MFCC after applying denoised DWT. And seven classification algorithms to optimize accuracy. A novel aspect of this study is the application of Mel Frequency Cepstral Coefficients (MFCC) to extract features from the video domain rather than their traditional use in audio processing, demonstrating the effectiveness of MFCC for video classification. Seven classification techniques, including K-Nearest Neighbors (KNN), Radial Basis Function Support Vector Machines (SVM-RBF), Parzen Window Method, Neighborhood Components Analysis (NCA), Multinomial Logistic Regression (ML), Linear Support Vector Machines (SVM Linear), and Decision Trees (DT), are evaluated to establish a robust classification framework. Experimental results indicate that this denoising-enhanced system significantly improves classification accuracy, providing a comprehensive framework for future applications in multimedia management and other fields. | |
| 653 | |a Feature extraction | ||
| 653 | |a Video data | ||
| 653 | |a Classification | ||
| 653 | |a Radial basis function | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Sentiment analysis | ||
| 653 | |a Support vector machines | ||
| 653 | |a Noise reduction | ||
| 653 | |a Signal processing | ||
| 653 | |a Decision trees | ||
| 653 | |a Multimedia | ||
| 700 | 1 | |a Talaat, Amira Samy |u Electronics Research Institute, Cairo, Egypt (GRID:grid.463242.5) (ISNI:0000 0004 0387 2680) | |
| 700 | 1 | |a Al-Makhlasawy, Rasha M. |u Electronics Research Institute, Cairo, Egypt (GRID:grid.463242.5) (ISNI:0000 0004 0387 2680) | |
| 773 | 0 | |t Journal of Electrical Systems and Information Technology |g vol. 12, no. 1 (Dec 2025), p. 97 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3276607581/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3276607581/fulltext/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3276607581/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |