Adaptive Hierarchical Multi-Headed Convolutional Neural Network With Modified Convolutional Block Attention for Aerial Forest Fire Detection by Md. Najmul Mowla, Davood Asadi, Shamsul Masum, Khaled Rabie

“…This enhanced framework addresses prior challenges by integrating adaptive pooling, concatenated convolutions for multi-scale feature extraction, and an improved attention mechanism incorporating shared fully connected layers, Glorot initialization, rectified linear units (ReLU), layer normalization, and attention-gating. …”
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Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques. by Yang Wu, Ming Chen, Yufang Qin

“…Then the model integrates various deep learning technologies multi-scale convolutional networks and transformer encoder to extract the properties of drug molecules from different perspectives, while an attention network is devoted to learning complex interactions between the omics features of cell lines and the aforementioned properties of drug molecules. …”
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Integrating Backscattered Electron Imaging and Multi-Feature-Weighted Clustering for Quantification of Hydrated C₃S Microstructure by Xin Wang, Yongjun Luo

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Establishing a Dynamic Recommendation System for E-commerce by Integrating Online Reviews, Product Feature Expansion, and Deep Learning by Tsung-Yin Ou, Chun-Hung Chen, Wen-Lung Tsai

“…The increasing fragmentation of the consumer journey complicates understanding consumer behavior and tracking digital footprints. Product feature tags should dynamically adapt to consumer preferences, marketing campaigns, and trending internet topics, leveraging crawler technology to address this. …”
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Micro-expression spotting based on multi-modal hierarchical semantic guided deep fusion and optical flow driven feature integration by Haolin Chang, Zhihua Xie, Fan Yang

“…By introducing an Optical Flow-Driven fusion feature Integration Module (OF-DIM), the correlation of non-scale fusion features is modeled in the channel dimension. …”
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Integration of Nuclear, Clinical, and Genetic Features for Lung Cancer Subtype Classification and Survival Prediction Based on Machine- and Deep-Learning Models by Bin Xie, Mingda Mo, Haidong Cui, Yijie Dong, Hongping Yin, Zhe Lu

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Multimodal Fall Detection Using Spatial–Temporal Attention and Bi-LSTM-Based Feature Fusion by Jungpil Shin, Abu Saleh Musa Miah, Rei Egawa, Najmul Hassan, Koki Hirooka, Yoichi Tomioka

“…In order to address these challenges, we propose a multimodal fall detection framework that integrates skeleton and sensor data. The system uses a Graph-based Spatial-Temporal Convolutional and Attention Neural Network (GSTCAN) to capture spatial and temporal relationships from skeleton and motion data information in stream-1, while a Bi-LSTM with Channel Attention (CA) processes sensor data in stream-2, extracting both spatial and temporal features. …”
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A multi-model approach integrating whole-slide imaging and clinicopathologic features to predict breast cancer recurrence risk by Manu Goyal, Jonathan D. Marotti, Adrienne A. Workman, Graham M. Tooker, Seth K. Ramin, Elaine P. Kuhn, Mary D. Chamberlin, Roberta M. diFlorio-Alexander, Saeed Hassanpour

“…The proposed novel methodology uses convolutional neural networks for feature extraction and vision transformers for contextual aggregation, complemented by a logistic regression model that analyzes clinicopathologic data for classification into two risk categories. …”
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FD-YOLO11: A Feature-Enhanced Deep Learning Model for Steel Surface Defect Detection by Zichen Dang, Xingshuo Wang

“…To enhance the multiscale feature extraction process, self-calibrated convolution is integrated into the C3k2 module. …”
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A Multimodal Deep Learning Model Integrating CNN and Transformer for Predicting Chemotherapy-Induced Cardiotoxicity by Ahmed Bouatmane, Abdelaziz Daaif, Abdelmajid Bousselham, Bouchra Bouihi, Omar Bouattane

“…We developed three architectures that integrate Convolutional Neural Networks (CNNs) for feature extraction from TDI images with Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer models to capture temporal dependencies and enhance prediction accuracy. …”
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Hybrid feature-time series neural network for predicting ACL forces in martial artists with resistive braces after reconstruction by Dongyue Li, Haojie Li, Yang Hang

“…Comparative analyses demonstrated significant advantages over standalone TCN (R2 = 0.54) and long short-term memory (R2 = 0.51) models.ConclusionThe integration of temporal biomechanical data and static clinical features enables accurate ACL force prediction, particularly for patients using resistive braces. …”
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Study on Photovoltaic Plant Site Selection Models Based on Geographic and Environmental Features by RAO Zhi, YANG Zaimin, YANG Xiongping, LI Jiaming, YANG Ping, WEI Zhichu

“…[Objective] To improve the accuracy of global horizontal irradiance （GHI） prediction and completely explore its application value in solar energy resource assessments， such as photovoltaic site selection， this study proposes a GHI prediction model that integrates the temporal convolutional network （TCN） with the former architecture. …”
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Micro-electrical Discharge Machining of Micro-holes Based on Integrated Orthogonal Experiments and CNN Methods by Yuandong MO, Yazhi WANG, Shuqi HUANG, Jiajun ZHONG

“…Deep learning is applied innovatively to micro-hole micro-EDM, weaving together orthogonal experiments and deep learning to present an intricate methodology, i.e., an integrated fusion of orthogonal experiments and convolutional neural networks (CNNs). …”
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Forest Fire Detection Based on Enhanced Feature Information Capture and Long-Range Dependency by Wangke Lin, Jun Gao, Jingde Huang

“…First, we incorporate a contrast enhancement technique that leverages an atmospheric scattering model and entropy optimization to clarify the blurred smoke edges. Next, we integrate deformable convolutions into the original feature extraction module, enabling more effective capture of long-range dependencies and spatial relationships, thus improving multi-scale fire detection accuracy. …”
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Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information by Ruibin Zhang, Yingshi Guo, Yunze Long, Yang Zhou, Chunyan Jiang

“…A vehicle motion state prediction algorithm integrating point cloud timing multiview features and multitarget interaction information is proposed in this work to effectively predict the motion states of traffic participants around intelligent vehicles in complex scenes. …”
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An explainable hybrid feature aggregation network with residual inception positional encoding attention and EfficientNet for cassava leaf disease classification by M. Sundara Srivathsan, S. Alden Jenish, K. Arvindhan, R. Karthik

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MFHG-DDI: An Enhanced Hybrid Graph Method Leveraging Multiple Features for Predicting Drug–Drug Interactions by Xiang Li, Xiangmin Ji, Chengzhen Xu, Jie Hou, Xiaoyu Zhao, Guodong Peng

“…Specifically, we constructed a heterogeneous network incorporating multiple drug features and DDI information. We employed an enhanced hybrid graph module that integrates a graph convolutional network, graph attention network, and global average pooling to learn latent features, ultimately applying a prediction function to predict DDIs. …”
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Multistage fall detection framework via 3D pose sequences and TCN integration by Leitao Qi, Haibo Sun

“…To address this, we propose a novel multi-stage fall detection framework that integrates 3D pose sequences with temporal convolutional modeling. …”
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Fine-art recognition using convolutional transformers by Yu Liu, Haozhe Bai, Jingchao Wang

“…Our study also highlighted the effectiveness of using convolutional transformers for learning image features.…”
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Integrating Explanations into CNNs by Adopting Spiking Attention Block for Skin Cancer Detection by Inzamam Mashood Nasir, Sara Tehsin, Robertas Damaševičius, Rytis Maskeliūnas

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