A fake news detection model using the integration of multimodal attention mechanism and residual convolutional network by Ying Lu, Naiwei Yao

“…Third, it optimizes the feature fusion structure for more effective integration. …”
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Advancing plant leaf disease detection integrating machine learning and deep learning by R. Sujatha, Sushil Krishnan, Jyotir Moy Chatterjee, Amir H. Gandomi

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Deep image features sensing with multilevel fusion for complex convolution neural networks & cross domain benchmarks. by Aiza Shabir, Khawaja Tehseen Ahmed, Arif Mahmood, Helena Garay, Luis Eduardo Prado González, Imran Ashraf

“…The presented method encompass feature vectors for information synthesis and improves the discriminating strength of the retrieval system by extracting deep image features including primitive, spatial, and overlayed using multilayer fusion of Convolutional Neural Networks(CNNs). …”
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Optimizing Tumor Detection in Brain MRI with One-Class SVM and Convolutional Neural Network-Based Feature Extraction by Azeddine Mjahad, Alfredo Rosado-Muñoz

“…Experimental results demonstrate that combining Convolutional Neural Network (CNN)-based feature extraction with OCSVM significantly improves anomaly detection performance compared with simpler handcrafted approaches. …”
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Semantics-Assisted Training Graph Convolution Network for Skeleton-Based Action Recognition by Huangshui Hu, Yu Cao, Yue Fang, Zhiqiang Meng

“…These extracted features are then integrated through feature fusion. …”
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Improving brain tumor classification: An approach integrating pre-trained CNN models and machine learning algorithms by Mohamed R. Shoaib, Jun Zhao, Heba M. Emara, Ahmed S. Mubarak, Osama A. Omer, Fathi E. Abd El-Samie, Hamada Esmaiel

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Adaptive spatial-channel feature fusion and self-calibrated convolution for early maize seedlings counting in UAV images by Zhenyuan Sun, Zhenyuan Sun, Zhenyuan Sun, Zhi Yang, Zhi Yang, Yimin Ding, Yimin Ding, Boyan Sun, Boyan Sun, Saiju Li, Saiju Li, Zhen Guo, Zhen Guo, Lei Zhu, Lei Zhu

“…RC-Dino introduces two innovative components: a novel self-calibrating convolutional layer named RSCconv and an adaptive spatial feature fusion module called ASCFF. …”
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Optimizing Fire Scene Analysis: Hybrid Convolutional Neural Network Model Leveraging Multiscale Feature and Attention Mechanisms by Shakhnoza Muksimova, Sabina Umirzakova, Mirjamol Abdullaev, Young-Im Cho

“…The proposed model integrates advanced convolutional neural networks with multiscale feature extraction, attention mechanisms, and ensemble learning to achieve superior performance in real-time fire detection. …”
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Multiscale Wavelet and Graph Network With Spectral Self-Attention for Hyperspectral Image Classification by Anyembe C. Shibwabo, Zou Bin, Tahir Arshad, Jorge Abraham Rios Suarez

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SLG-Net: Small-Large-Global Feature-Based Multilevel Feature Extraction Network for Ultrasound Image Segmentation by Xinya Fan, Jianwen Hu, Kai Hu

“…In addition, at the skip connection, a multi-scale attention module is integrated to retain significant shallow features for subsequent fusion of deep and shallow features. …”
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LS-MambaNet: Integrating Large Strip Convolution and Mamba Network for Remote Sensing Object Detection by Lingyu Yan, Zijian He, Zhiqi Zhang, Guangqi Xie

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A compact deep learning approach integrating depthwise convolutions and spatial attention for plant disease classification by Amreen Batool, Jisoo Kim, Yung-Cheol Byun

“…By integrating spatial attention and depthwise separable convolution, the LWDSC-SA model improves the ability to detect and classify plant diseases. …”
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ICT-Net: An Integrated Convolution and Transformer-Based Network for Complex Liver and Liver Tumor Region Segmentation by Chukwuemeka Clinton Atabansi, Hui Li, Sheng Wang, Jing Nie, Haijun Liu, Bo Xu, Xichuan Zhou, Dewei Li

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Hybrid Integrated Feature Fusion of Handcrafted and Deep Features for Rice Blast Resistance Identification Using UAV Imagery by Peng Zhang, Zibin Zhou, Huasheng Huang, Yuanzhu Yang, Xiaochun Hu, Jiajun Zhuang, Yu Tang

“…To address these issues, this article proposes a hybrid integrated feature fusion (HIFF) method, in which a novel handcrafted-design-guided convolutional neural network module was employed to alleviate the problem of image degradation. …”
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Transfer learning based hybrid feature learning framework for enhanced skin cancer diagnosis using deep feature integration by Maridu Bhargavi, Sivadi Balakrishna

“…To address these issues, this research proposes the DRMv2Net model, a feature fusion deep learning-based technique that integrates multiple pre-trained convolutional neural networks to enhance skin cancer diagnosis. …”
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Osteoarthritis Classification Using Hybrid Quantum Convolutional Neural Network by Devansh Tikariha, Abdul Moomin, D. Jeyamani, P. Rukmani

“…This work explores the integration of quantum computing within a classical Convolutional Neural Network (CNN) framework, leveraging a VGG16 model enhanced with a quantum Convolutional Neural Network (QCNN) for the classification of knee osteoarthritis (OA) severity. …”
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Highly accurate anomaly based intrusion detection through integration of the local outlier factor and convolutional neural network by Rahimullah Rabih, Hamed Vahdat-Nejad, Wathiq Mansoor, Javad Hassannataj Joloudari

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Optimized Demand Forecasting for Bike-Sharing Stations Through Multi-Method Fusion and Gated Graph Convolutional Neural Networks by Hebin Guo, Kexin Li, Yutong Rou

“…A detailed analysis of the temporal, spatial, and user-related factors is conducted to examine how these features interact and shape demand patterns in the bike-sharing network. …”
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Crack-ConvT Net: A Convolutional Transformer Network for Crack Segmentation in Underwater Dams by Pengfei Shi, Hongzhu Chen, Zaiming Geng, Xinnan Fan, Yuanxue Xin

“…However, challenges like uneven underwater lighting, sediment interference, and complex backgrounds often hinder traditional detection methods, leading to feature loss and false detections. To address these issues, this paper proposes Crack-ConvT Net, a U-Shape architecture that integrates Convolutional Neural Networks (CNNs) and Transformers for underwater dam crack segmentation. …”
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Local-Global Feature Extraction Network With Dynamic 3-D Convolution and Residual Attention Transformer for Hyperspectral Image Classification by Qiqiang Chen, Zhengyang Li, Junru Yin, Wei Huang, Tianming Zhan

“…Currently, convolutional neural network (CNN) and transformer-based hyperspectral image (HSI) classification methods have attracted significant attention owing to their effective feature representation capabilities. …”
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