Deep Multi-Component Neural Network Architecture
Existing neural network architectures often struggle with two critical limitations: (1) information loss during dataset length standardization, where variable-length samples are forced into fixed dimensions, and (2) inefficient feature selection in single-modal systems, which treats all features equ...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/13/4/93 |
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| author | Chafik Boulealam Hajar Filali Jamal Riffi Adnane Mohamed Mahraz Hamid Tairi |
| author_facet | Chafik Boulealam Hajar Filali Jamal Riffi Adnane Mohamed Mahraz Hamid Tairi |
| author_sort | Chafik Boulealam |
| collection | DOAJ |
| description | Existing neural network architectures often struggle with two critical limitations: (1) information loss during dataset length standardization, where variable-length samples are forced into fixed dimensions, and (2) inefficient feature selection in single-modal systems, which treats all features equally regardless of relevance. To address these issues, this paper introduces the Deep Multi-Components Neural Network (DMCNN), a novel architecture that processes variable-length data by regrouping samples into components of similar lengths, thereby preserving information that traditional methods discard. DMCNN dynamically prioritizes task-relevant features through a component-weighting mechanism, which calculates the importance of each component via loss functions and adjusts weights using a SoftMax function. This approach eliminates the need for dataset standardization while enhancing meaningful features and suppressing irrelevant ones. Additionally, DMCNN seamlessly integrates multimodal data (e.g., text, speech, and signals) as separate components, leveraging complementary information to improve accuracy without requiring dimension alignment. Evaluated on the Multimodal EmotionLines Dataset (MELD) and CIFAR-10, DMCNN achieves state-of-the-art accuracy of 99.22% on MELD and 97.78% on CIFAR-10, outperforming existing methods like MNN and McDFR. The architecture’s efficiency is further demonstrated by its reduced trainable parameters and robust handling of multimodal and variable-length inputs, making it a versatile solution for classification tasks. |
| format | Article |
| id | doaj-art-fffa9f773a4d47759310786b1179d98a |
| institution | OA Journals |
| issn | 2079-3197 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj-art-fffa9f773a4d47759310786b1179d98a2025-08-20T02:17:14ZengMDPI AGComputation2079-31972025-04-011349310.3390/computation13040093Deep Multi-Component Neural Network ArchitectureChafik Boulealam0Hajar Filali1Jamal Riffi2Adnane Mohamed Mahraz3Hamid Tairi4LISAC, Department of Computer Science, Faculty of Science Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez 30000, MoroccoLISAC, Department of Computer Science, Faculty of Science Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez 30000, MoroccoLISAC, Department of Computer Science, Faculty of Science Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez 30000, MoroccoLISAC, Department of Computer Science, Faculty of Science Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez 30000, MoroccoLISAC, Department of Computer Science, Faculty of Science Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez 30000, MoroccoExisting neural network architectures often struggle with two critical limitations: (1) information loss during dataset length standardization, where variable-length samples are forced into fixed dimensions, and (2) inefficient feature selection in single-modal systems, which treats all features equally regardless of relevance. To address these issues, this paper introduces the Deep Multi-Components Neural Network (DMCNN), a novel architecture that processes variable-length data by regrouping samples into components of similar lengths, thereby preserving information that traditional methods discard. DMCNN dynamically prioritizes task-relevant features through a component-weighting mechanism, which calculates the importance of each component via loss functions and adjusts weights using a SoftMax function. This approach eliminates the need for dataset standardization while enhancing meaningful features and suppressing irrelevant ones. Additionally, DMCNN seamlessly integrates multimodal data (e.g., text, speech, and signals) as separate components, leveraging complementary information to improve accuracy without requiring dimension alignment. Evaluated on the Multimodal EmotionLines Dataset (MELD) and CIFAR-10, DMCNN achieves state-of-the-art accuracy of 99.22% on MELD and 97.78% on CIFAR-10, outperforming existing methods like MNN and McDFR. The architecture’s efficiency is further demonstrated by its reduced trainable parameters and robust handling of multimodal and variable-length inputs, making it a versatile solution for classification tasks.https://www.mdpi.com/2079-3197/13/4/93deep learning (DL)image classificationmeaningful neural network (MNN)multimodaldeep multi-components neural network architecture (DMCNN)neural network architecture |
| spellingShingle | Chafik Boulealam Hajar Filali Jamal Riffi Adnane Mohamed Mahraz Hamid Tairi Deep Multi-Component Neural Network Architecture Computation deep learning (DL) image classification meaningful neural network (MNN) multimodal deep multi-components neural network architecture (DMCNN) neural network architecture |
| title | Deep Multi-Component Neural Network Architecture |
| title_full | Deep Multi-Component Neural Network Architecture |
| title_fullStr | Deep Multi-Component Neural Network Architecture |
| title_full_unstemmed | Deep Multi-Component Neural Network Architecture |
| title_short | Deep Multi-Component Neural Network Architecture |
| title_sort | deep multi component neural network architecture |
| topic | deep learning (DL) image classification meaningful neural network (MNN) multimodal deep multi-components neural network architecture (DMCNN) neural network architecture |
| url | https://www.mdpi.com/2079-3197/13/4/93 |
| work_keys_str_mv | AT chafikboulealam deepmulticomponentneuralnetworkarchitecture AT hajarfilali deepmulticomponentneuralnetworkarchitecture AT jamalriffi deepmulticomponentneuralnetworkarchitecture AT adnanemohamedmahraz deepmulticomponentneuralnetworkarchitecture AT hamidtairi deepmulticomponentneuralnetworkarchitecture |