Cancelable Multi-Branch Deep Learning Framework for Privacy-Preserving ECG Biometric Authentication by Mohamed Hammad, Ali Abdullah S. AlQahtani, Mohammed ELAffendi, Abdelhamied A. Ateya, Nebojsa Bacanin

“…The proposed model captures varied ECG feature representations via parallel convolutional branches and transforms them into non-invertible templates using a fixed-key CDBT method. …”
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TDR-Model: Tomato Disease Recognition Based on Image Dehazing and Improved MobileNetV3 Model by Zhixiang Zhang, Tong Liu, Jinyu Gao, Meng Yang, Wenjun Luo, Fanqiang Lin

“…Moreover, MobileNetV3 is improved by the incorporation of the convolutional block attention module (CBAM) and Omni-Dimensional Dynamic Convolution(ODC), improving the accuracy of disease feature recognition. …”
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Neural signals, machine learning, and the future of inner speech recognition by Adiba Tabassum Chowdhury, Ahmed Hassanein, Aous N. Al Shibli, Youssuf Khanafer, Mohannad Natheef AbuHaweeleh, Shona Pedersen, Muhammad E. H. Chowdhury

“…We analyze both traditional methods such as support vector machines (SVMs) and random forests, as well as advanced deep learning approaches like convolutional neural networks (CNNs), which are particularly effective at capturing the dynamic and non-linear patterns of inner speech-related brain activity. …”
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scSMD: a deep learning method for accurate clustering of single cells based on auto-encoder by Xiaoxu Cui, Renkai Wu, Yinghao Liu, Peizhan Chen, Qing Chang, Pengchen Liang, Changyu He

“…Results We propose the SMD deep learning model, which integrates nonlinear dimensionality reduction techniques with a porous dilated attention gate component. Built upon a convolutional autoencoder and informed by the negative binomial distribution, the SMD model efficiently captures essential cell clustering features and dynamically adjusts feature weights. …”
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An industrial carbon block instance segmentation algorithm based on improved YOLOv8 by Runjie Shi, Zhengbao Li, Zewei Wu, Wenxin Zhang, Yihang Xu, Gan Luo, Pingchuan Ma, Zheng Zhang

“…YOLOv8-HDSA introduces Focaler-IoU as a loss function to dynamically adjust sample weights to optimize regression performance. …”
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Multi-View Contrastive Fusion POI Recommendation Based on Hypergraph Neural Network by Luyao Hu, Guangpu Han, Shichang Liu, Yuqing Ren, Xu Wang, Ya Liu, Junhao Wen, Zhengyi Yang

“…Subsequently, a targeted hypergraph convolutional network is designed for aggregation and propagation, learning the latent factors within each view. …”
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Adaptive Outdoor Cleaning Robot with Real-Time Terrain Perception and Fuzzy Control by Raul Fernando Garcia Azcarate, Akhil Jayadeep, Aung Kyaw Zin, James Wei Shung Lee, M. A. Viraj J. Muthugala, Mohan Rajesh Elara

“…This paper proposes a terrain-aware cleaning framework that dynamically adjusts robot behavior based on real-time surface classification and slope estimation. …”
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Spatiotemporal information conversion machine for time-series forecasting by Hao Peng, Pei Chen, Rui Liu, Luonan Chen

“…STICM combines the advantages of both the STI equation and the temporal convolutional network, which maps the high-dimensional/spatial data to the future temporal values of a target variable, thus naturally providing the forecasting of the target variable. …”
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Machine learning-based state of charge estimation: A comparison between CatBoost model and C-BLSTM-AE model by Abderrahim Zilali, Mehdi Adda, Khaled Ziane, Maxime Berger

“…This paper proposes the application of two machine learning-based approaches for SOC estimation that perform well at wide range of temperatures (positive and negative) and varying dynamic loads. The first one is a hybrid deep learning approach based on the Convolutional BLSTM Auto-Encoder (C-BLSTM-AE) model that relies on extracting abstract features from input data. …”
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Non-Contact Blood Pressure Monitoring Using Radar Signals: A Dual-Stage Deep Learning Network by Pengfei Wang, Minghao Yang, Xiaoxue Zhang, Jianqi Wang, Cong Wang, Hongbo Jia

“…Specifically, Stage 1 deploys convolutional depth-adjustable lightweight residual blocks to extract spatial features from micro-motion characteristics, while Stage 2 employs a transformer architecture to establish correlations between these spatial features and BP periodic dynamic variations. …”
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Integrating deformable CNN and attention mechanism into multi-scale graph neural network for few-shot image classification by Yongmin Liu, Fengjiao Xiao, Xinying Zheng, Weihao Deng, Haizhi Ma, Xinyao Su, Lei Wu

“…The feature extraction module of graph neural networks has always been designed as a fixed convolutional neural network (CNN), but due to the intrinsic properties of convolution operations, its receiving domain is limited. …”
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An Improved YOLOv8n-Based Method for Detecting Rice Shelling Rate and Brown Rice Breakage Rate by Zhaoyun Wu, Yehao Zhang, Zhongwei Zhang, Fasheng Shen, Li Li, Xuewu He, Hongyu Zhong, Yufei Zhou

“…The Detect_Rice lightweight head compresses parameters via group normalization and dynamic convolution sharing, optimizing small-target response. …”
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TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion by Yong Huang, Xiewen Hu, Shilong Pang, Wei Fu, Shuaipeng Chang, Bin Gao, Weihua Hua

“…The results show that the prediction method proposed in this paper can effectively predict the surrounding rock grade of the tunnel face during TBM tunnelling, and provide decision support for the dynamic regulation of tunnelling parameters.…”
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Real-Time Lightweight Morphological Detection for Chinese Mitten Crab Origin Tracing by Xiaofei Ma, Nannan Shen, Yanhui He, Zhuo Fang, Hongyan Zhang, Yun Wang, Jinrong Duan

“…In the first stage, an improved YOLOv10n-based model is designed by incorporating omni-dimensional dynamic convolution, a SlimNeck structure, and a Lightweight Shared Convolutional Detection head, which effectively enhances the detection accuracy of crab targets under complex multi-scale environments while reducing computational cost. …”
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NPI-WGNN: A Weighted Graph Neural Network Leveraging Centrality Measures and High-Order Common Neighbor Similarity for Accurate ncRNA–Protein Interaction Prediction by Fatemeh Khoushehgir, Zahra Noshad, Morteza Noshad, Sadegh Sulaimany

“…To optimize prediction accuracy, we employ a hybrid GNN architecture that combines graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE layers, each contributing unique advantages: GraphSAGE offers scalability, GCN provides a global structural perspective, and GAT applies dynamic neighbor weighting. …”
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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

“…The attention mechanisms dynamically identify crucial image regions, leveraging each CNN’s unique strengths. …”
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Time series changes and influencing factors of fractional vegetation coverage under weed competition in wheat field ecosystems through remote sensing by Guofeng Yang, Yong He, Zhenjiang Zhou, Lingzhen Ye, Hui Fang, Xuping Feng

“…The Transformer-based PoolFormer model outperformed convolutional neural networks, achieving a two-year average mIoU of 93.1% using full-band multispectral data. …”
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A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction by Sangkeum Lee, Mohammad H. Almomani, Saleh Ali Alomari, Kashif Saleem, Aseel Smerat, Vaclav Snasel, Amir H. Gandomi, Laith Abualigah

“…To address these, this study proposes a novel hybrid deep learning framework, IAPO-LSTM, which combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Gated Recurrent Units (GRUs) for temporal sequence modeling. …”
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Design of Chinese traditional Jiaoyi (Folding chair) based on Kansei Engineering and CNN-GRU-attention by Xinyan Yang, Nan Zhang, Jiufang Lv, Jiufang Lv, Jiufang Lv

“…BackgroundsThis study innovatively enhances personalized emotional responses and user experience quality in traditional Chinese folding armchair (Jiaoyi chair) design through an interdisciplinary methodology.GoalTo systematically extract user emotional characteristics, we developed a hybrid research framework integrating web-behavior data mining.Methods1) the KJ method combined with semantic crawlers extracts emotional descriptors from multi-source social data; 2) expert evaluation and fuzzy comprehensive assessment reduce feature dimensionality; 3) random forest and K-prototype clustering identify three core emotional preference factors: “Flexible Refinement,” “Uncompromising Quality,” and “ergonomic stability.”DiscussionA CNN-GRU-Attention hybrid deep learning model was constructed, incorporating dynamic convolutional kernels and gated residual connections to address feature degradation in long-term semantic sequences. …”
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Unlocking transcranial FUS-EEG feature fusion for non-invasive sleep staging in next-gen clinical applications by Suneet Gupta, Praveen Gupta, Bechoo Lal, Aniruddha Deka, Hirakjyoti Sarma, Sheifali Gupta

“…The proposed framework integrates two one-dimensional convolutional neural networks (1D-CNNs) to extract sleep-relevant features from EEG and EOG signals, followed by an adaptive feature fusion module that dynamically assigns weights based on feature significance. …”
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