An approach to arousal disorder classification using deformable convolution and adaptive multiscale features in EEG signals by Andia Foroughi, Fardad Farokhi, Fereidoun Nowshiravan Rahatabad, Alireza Kashaninia

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HFF-Net: A hybrid convolutional neural network for diabetic retinopathy screening and grading by Muhammad Hassaan Ashraf, Hamed Alghamdi

“…This approach can lead to information loss in the initial stages due to limited feature utilization across adjacent layers. To address this limitation, we propose a Hierarchical Features Fusion Convolutional Neural Network (HFF-Net) within a Diabetic Retinopathy Screening and Grading (DRSG) framework. …”
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Applying a Convolutional Vision Transformer for Emotion Recognition in Children with Autism: Fusion of Facial Expressions and Speech Features by Yonggu Wang, Kailin Pan, Yifan Shao, Jiarong Ma, Xiaojuan Li

“…Consequently, we propose a multimodal data fusion strategy for emotion recognition and construct a feature fusion model based on an attention mechanism, which attains a recognition accuracy of 90.73%. …”
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Urban Land Use Classification Model Fusing Multimodal Deep Features by Yougui Ren, Zhiwei Xie, Shuaizhi Zhai

“…Subsequently, VGG-16 (Visual Geometry Group 16) is used to extract the image convolutional features of the block units, obtaining the raster structure deep features. …”
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PCCNN: A CNN classification model integrating EEG time-frequency features for stroke classification by Teng Wang, Fenglian Li, Jia Yang, Wenhui Jia, Fengyun Hu

“…Each DWT and EMD feature is processed by an independent one-dimensional convolutional neural networks (1D-CNN) branch for targeted extraction. …”
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Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification by Ahmad Muhammad, Qi Jin, Osman Elwasila, Yonis Gulzar

“…Conclusions: This framework enables precise early Alzheimer’s disease (AD) diagnosis by integrating multi-scale neuroimaging features, empowering clinicians to optimize patient care through timely and targeted interventions.…”
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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

“…The GSTCAN model uses AlphaPose for skeleton extraction, calculates motion between consecutive frames, and applies a graph convolutional network (GCN) with a CA mechanism to focus on relevant features while suppressing noise. …”
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Lumbar and pelvic CT image segmentation based on cross-scale feature fusion and linear self-attention mechanism by Chaofan Li, Liping Chen, Qiong Liu, Jinnan Teng

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EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection by Alanoud Al Mazroa, Majdy M. Eltahir, Shouki A. Ebad, Faiz Abdullah Alotaibi, Venkatachalam K, Jaehyuk Cho

“…This paper proposes a new deep-learning method called Cascaded Atrous Convolutional Network with Adaptive Weight Fusion (CA-AWFM) for classifying schizophrenia from electroencephalogram (EEG) data that combines cascaded networks with atrous convolutions and an adaptive weight fusion module (AWFM). …”
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Deep learning model for grading carcinoma with Gini-based feature selection and linear production-inspired feature fusion by Shreyan Kundu, Souradeep Mukhopadhyay, Rahul Talukdar, Dmitrii Kaplun, Alexander Voznesensky, Ram Sarkar

“…To enhance the grading accuracy for liver and renal cell carcinoma, this research introduces a novel feature selection and fusion framework inspired by economic theories, incorporating attention mechanisms into three Convolutional Neural Network (CNN) architectures-MobileNetV2, DenseNet121, and InceptionV3-as foundational models. …”
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AirQuaNet: A Convolutional Neural Network Model With Multi-Scale Feature Learning and Attention Mechanisms for Air Quality-Based Health Impact Prediction by Sreeni Chadalavada, Suleyman Yaman, Abdulkadir Sengur, Ravinesh C. Deo, Abdul Hafeez-Baig, Tracy Kolbe-Alexander, Niranjana Sampathila, U. Rajendra Acharya

“…The MSCBs employ four parallel 1D convolutional layers with different kernel sizes, enabling the model to extract multi-scale features critical for learning patterns in complex environmental data. …”
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Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features by Abdullah Baktash, Yashar Sarbaz, Saeed Meshgini, Reza Afrouzian

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Hybrid CNN-Transformer-WOA model with XGBoost-SHAP feature selection for arrhythmia risk prediction in acute myocardial infarction patients by Li Li, Wenjun Ren, Yuying Lei, Lixia Xu, Xiaohui Ning

“…A two-stage feature selection using XGBoost and SHAP identified the top 10 clinical predictors from 45 variables. …”
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Crash severity prediction using a virtual geometry-group-based deep learning approach with images-based feature representation by Nanon Sonnatthanon, Kasem Choocharukul

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Enhanced Blade Fault Diagnosis Using Hybrid Deep Learning: A Comparative Analysis of Traditional Machine Learning and 1D Convolutional Transformer Architecture by Syed Asad Imam, Meng Hee Lim, Ahmed Mohammed Abdelrhman, Iftikhar Ahmad, Mohd Salman Leong

“…Noise and complex design in multistage rotors can mask blade faults in vibration signals, necessitating automated feature extraction and expert diagnosis. This research investigates blade FD, comparing traditional machine learning approaches with a novel hybrid deep learning fused model based on a one‐dimensional (1D) convolutional transformer. …”
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Ensemble of features for efficient classification of high-resolution remote sensing image by Gladima Nisia T, Rajesh S

“…Our approach uses the deep convolutional neural network for extracting deep features. …”
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DB-Net: A Dual-Branch Hybrid Network for Stroke Lesion Segmentation on Non-Contrast CT Images by Xiao Jia, He Dong, Jiashu Xu, Yanhong Zhang, Yihua Lan

“…These limitations can lead to omissions, misdiagnoses, or inaccurate segmentations, directly impacting clinical assessment and timely intervention. To address these challenges, this study proposes a two-branch hybrid network combining a convolutional neural network (CNN) with a Transformer framework. …”
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Detecting Lameness in Dairy Cows Based on Gait Feature Mapping and Attention Mechanisms by Xi Kang, Junjie Liang, Qian Li, Gang Liu

“…The proposed system comprises (1) a Cow Lameness Feature Map (CLFM) model extracting holistic gait kinematics (hoof trajectories and dorsal contour) from walking sequences, and (2) a DenseNet-Integrated Convolutional Attention Module (DCAM) that mitigates inter-individual variability through multi-feature fusion. …”
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Listening deeper: neural networks unravel acoustic features in preterm infant crying by Yuta Shinya, Taiji Ueno, Masahiko Kawai, Fusako Niwa, Seiichi Tomotaki, Masako Myowa

“…These findings suggest that decoding spectrotemporal features in infant crying through deep learning may offer valuable insights into atypical neurodevelopment in preterm infants, with potential to enhance early detection and intervention strategies in clinical practice.…”
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Learning Deep Embedding with Acoustic and Phoneme Features for Speaker Recognition in FM Broadcasting by Xiao Li, Xiao Chen, Rui Fu, Xiao Hu, Mintong Chen, Kun Niu

“…The hybrid DNN consists of a convolutional neural network architecture for generating acoustic features and a multilayer perceptron architecture for extracting phoneme features sequentially, which represent significant pronunciation attributes. …”
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