Corn variety identification based on improved EfficientNet lightweight neural network by Jinpu Xu, Jinpu Xu, Jinhao Lan, Guangjie Lv, Dexin Ma

“…Secondly, the number of MBConv modules in the EfficientNetB0 model was reduced, and the CBAM attention mechanism and dilation convolution were introduced to enhance the feature extraction capability. …”
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Segmentation of Stone Slab Cracks Based on an Improved YOLOv8 Algorithm by Qitao Tian, Runshu Peng, Fuzeng Wang

“…In addition, the dynamic snake convolution head (DSConv) improves the model’s ability to follow irregular crack shapes. …”
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Dual-Stream Architecture Enhanced by Soft-Attention Mechanism for Plant Species Classification by Imran Ullah Khan, Haseeb Ali Khan, Jong Weon Lee

“…The proposed model utilizes residual and inception blocks enhanced with dilated convolutional layers for acquiring both local and global information. …”
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In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module by Junwei Zhu, Xupeng Ouyang, Zongkang Jiang, Yanlong Xu, Hongtao Xue, Huiyu Yue, Huayuan Feng

“…To address the challenge of fault diagnosis for in-wheel motors in four-wheel independent driving systems under variable driving conditions and harsh environments, this paper proposes a novel method based on two-stream 2DCNNs (two-dimensional convolutional neural networks) with a DCBA (depthwise convolution block attention) module. …”
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CDUNeXt: efficient ossification segmentation with large kernel and dual cross gate attention by Hailiang Xia, Chuantao Wang, Zhuoyuan Li, Yuchen Zhang, Shihe Hu, Jiliang Zhai

“…By designing lightweight module structures, utilizing large-kernel convolutions to extracts the long-distance dependencies of different features of the image, and adopting dual-cross-gate-attention(DCGA) to sequentially capture the channel and spatial dependencies so as to fast and accurate segmentation while maintaining fewer parameters and lower complexity. …”
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Automatic license-plate recognition by A. V. Poltavskii, T. G. Yurushkina, M. V. Yurushkin

“…Quality of the system is provided through the optimization of various models with different modifications. In particular, convolution neural networks were trained using images from several datasets. …”
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Lightweight and Low-Parametric Network for Hardware Inference of Obstructive Sleep Apnea by Tanmoy Paul, Omiya Hassan, Christina S. McCrae, Syed Kamrul Islam, Abu Saleh Mohammad Mosa

“…Using each type of convolution, three different models were developed using ECG, SpO₂, and model fusion. …”
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Wind Power Forecasting Based on Multi-Graph Neural Networks Considering External Disturbances by Xiaoyin Xu, Zhumei Luo, Menglong Feng

“…Extensive experiments across diverse wind farm clusters and different weather conditions indicate that GCN-EIF achieves an 18.99% lower RMSE and 5.08% lower MAE than state-of-the-art methods. …”
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FruitsMultiNet: A deep neural network approach to identify fruits through multi-scale feature fusion using mobile interface by Tasauf Mim, Md Mahbubur Rahman, Jahanur Biswas, Ahmad Shafkat, Khandaker Mohammad Mohi Uddin

“…The proposed research suggests a groundbreaking autonomous fruit classification method grounded on convolutional neural networks (CNNs). The classification of fruits is often challenging due to variations that occur in the same fruit throughout its life cycle. …”
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Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography by Anh T. Tran, Dmitriy Desser, Tal Zeevi, Gaby Abou Karam, Julia Zietz, Andrea Dell’Orco, Min-Chiun Chen, Ajay Malhotra, Adnan I. Qureshi, Santosh B. Murthy, Shahram Majidi, Guido J. Falcone, Kevin N. Sheth, Jawed Nawabi, Seyedmehdi Payabvash

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A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n by Lei Yang, Wenhao Cui, Jingqian Li, Guotao Han, Qi Zhou, Yubin Lan, Jing Zhao, Yongliang Qiao

“…A dataset of cotton boll diseases under different lighting conditions and shooting angles in the field was constructed. …”
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Video Action Recognition Based on Two‑stream Feature Enhancement Network by ZHAO Chen, FENG Xiufang, DONG Yunyun, WEN Xin, CAO Ruochen

“…[Purposes] Two-stream convolutional networks primarily achieve high recognition accuracy by fusing spatial and temporal features of videos. …”
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A Deep Neural Network Framework for Dynamic Two-Handed Indian Sign Language Recognition in Hearing and Speech-Impaired Communities by Vaidhya Govindharajalu Kaliyaperumal, Paavai Anand Gopalan

“…This challenge can be met with a novel Enhanced Convolutional Transformer with Adaptive Tuna Swarm Optimization (ECT-ATSO) recognition framework proposed for double-handed sign language. …”
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<italic>DynaTrack</italic>: Low-Power Channel-Aware Dynamic Smartphone Tracking Using UWB DL-TDOA by Junyoung Choi, Sagnik Bhattacharya, Joohyun Lee

“…Among the various Ultra-wideband (UWB) ranging methods, the absence of uplink communication or centralized computation makes downlink time-difference-of-arrival (DL-TDOA) localization the most suitable for large-scale industrial deployments. …”
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Fault Classification of 3D-Printing Operations Using Different Types of Machine and Deep Learning Techniques by Satish Kumar, Sameer Sayyad, Arunkumar Bongale

“…In this work, the multi-sensory data are gathered using different sensors such as vibration, current, temperature, and sound sensors. …”
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BAM-SLDK: biologically inspired attention mechanism with spiking learnable delayed kernel synapses by Mario Chacón-Falcón, Alberto Patiño-Saucedo, Luis Camuñas-Mesa, Teresa Serrano-Gotarredona, Bernabé Linares-Barranco

“…More precisely, our main technical contributions are: (1) we add kernels to the temporal dimension to enlarge the receptive field of the convolution; (2) we time kernels activations to mimic multiple delayed times; and (3) we introduce three different pruning techniques to optimize the number of delays and parameters used. …”
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A Hybrid Deep Learning Framework for Deepfake Detection Using Temporal and Spatial Features by Fazeel Zafar, Talha Ahmed Khan, Salas Akbar, Muhammad Talha Ubaid, Sameena Javaid, Kushsairy Abdul Kadir

“…To enhance the model&#x2019;s adaptability, to different scenarios and datasets we implement data augmentation techniques such as CutMix, MixUp and Random Erasing. …”
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A feature enhancement FCOS algorithm for dynamic traffic object detection by Tuqiang Zhou, Wei Liu, Haoran Li

“…There has been significant difference and scale variation of object features for different road traffic participants (RTPs), meanwhile traditional Convolutional Neural Networks (CNNs) was difficult to extract object features efficiently for small targets. …”
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RDSF-Net: Residual Wavelet Mamba-Based Differential Completion and Spatio-Frequency Extraction Remote Sensing Change Detection Network by Shuo Wang, Dapeng Cheng, Genji Yuan, Jinjiang Li

“…Remote sensing change detection is a task of identifying and analyzing the area of surface change by comparing remote sensing images from different periods. It is widely used in many fields such as environmental monitoring, urban planning, and agricultural management. …”
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Optimization of energy acquisition system in smart grid based on artificial intelligence and digital twin technology by Zhen Jing, Qing Wang, Zhiru Chen, Tong Cao, Kun Zhang

“…Abstract In response to the low operating speed and poor stability of energy harvesting systems in smart grids, an energy harvesting optimization method based on improved convolutional neural networks and digital twin technology is proposed in the experiment. …”
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