Research on CNC Machine Tool Spindle Fault Diagnosis Method Based on DRSN–GCE Model by Xiaoxu Li, Jiahao Wang, Jianqiang Wang, Jixuan Wang, Jiaming Chen, Xuelian Yu

“…In the first step, the data are preprocessed by adding different noises with different ratios of signal to noise and different frequencies to the vibration signals. …”
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A Lightweight Forward–Backward Independent Temporal-Aware Causal Network for Speech Emotion Recognition by Sijia Fei, Qiang Feng, Fei Gao

“…Finally, different levels of forward-backward features are fused to refine historical-future emotion change trends and better perceive the details of emotion changes. …”
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An incremental learning framework for pipeline weld crack damage identification and leakage rate prediction by Jing Huang, Zhifen Zhang, Yanlong Yu, Yongjie Li, Shuai Zhang, Rui Qin, Ji Xing, Wei Cheng, Guangrui Wen, Xuefeng Chen

“…Then, according to the attenuation characteristics of acoustic emission signals, a portable input-attention module is designed to add different weights to the input sequence. Finally, the accurate prediction of leakage rate under different conditions is realized based on the temporal convolutional network. …”
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Corn variety identification based on improved EfficientNet lightweight neural network by Jinpu Xu, Jinpu Xu, Jinhao Lan, Guangjie Lv, Dexin Ma

“…The phenotypic characteristics of corn ears can be used to better classify and identify different varieties of corn, providing a reference for intelligent sorting of corn ears.…”
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Segmentation of Stone Slab Cracks Based on an Improved YOLOv8 Algorithm by Qitao Tian, Runshu Peng, Fuzeng Wang

“…Comparative tests and industrial validation demonstrate stable performance across different stone materials and textures and a 30% false-positive reduction in real production environments. …”
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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

“…Moreover, we have contributed a novel dataset that comprises 48 classes of different plant species. The results demonstrate a higher level of performance when compared to current models, emphasizing the capability of the dual-stream design in improving accuracy and model generalization. …”
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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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Hyperspectral Signatures for Detecting the Concrete Hydration Process Using Neural Networks by Shiming Li, Alfred Strauss, Damjan Grba, Maximilian Granzner, Benjamin Täubling-Fruleux, Thomas Zimmermann

“…This means that inadequate curing conditions lead to a loss of concrete quality and negative consequences in structural engineering. In addition, different state-of-the-art (SOTA) curing surface treatments and hydration periods have a significant effect on durability. …”
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Audio-Based Engine Fault Diagnosis with Wavelet, Markov Blanket, ROCKET, and Optimized Machine Learning Classifiers by Bernardo Luis Tuleski, Cristina Keiko Yamaguchi, Stefano Frizzo Stefenon, Leandro dos Santos Coelho, Viviana Cocco Mariani

“…Developing appropriate fault-labeled datasets can be challenging due to nonlinearity variations and divergence in feature distribution among different engine kinds or operating scenarios. To solve this task, this study experimentally measures audio emission signals from compression ignition engines in different vehicles, simulating injector failures, intake hose failures, and absence of failures. …”
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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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SFRADNet: Object Detection Network with Angle Fine-Tuning Under Feature Matching by Keliang Liu, Yantao Xi, Donglin Jing, Xue Zhang, Mingfei Xu

“…Existing detectors often utilize feature pyramid networks (FPN) and deformable (or rotated) convolutions to adapt to variations in object scale and orientation. …”
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Comparative Analysis of Hybrid Attention and Progressive Layering Through a Comprehensive Evaluation of ARU-Net and PLU-Net in Brain Tumour Segmentation by Noman Ahmed Siddiqui, Muhammad Tahir Qadri, Muhammad Ovais Akhter, Tahira Anwar, Fahad Farooq

“…The preprocessing steps are quite different as ARU-Net uses simplified Z-score normalisation and resizing, and PLU-Net involves a full pipeline, involving skull stripping, bias field correction, and two normalisations. …”
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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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Dual-Branch Deep Learning with Dynamic Stage Detection for CT Tube Life Prediction by Zhu Chen, Yuedan Liu, Zhibin Qin, Haojie Li, Siyuan Xie, Litian Fan, Qilin Liu, Jin Huang

“…In terms of optimization strategy, a dynamically weighted triplet mixed loss function is designed to adjust the weight ratios of different prediction tasks, effectively solving the problems of sample imbalance and uneven prediction accuracy. …”
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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 data were fragmented into training, testing, and validation sets for eight different fruits. The proposed FruitsMultiNet achieved a 99.84 % accuracy rate, which is greater than the accuracy rates of individual Deep Learning (DL) models. …”
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Unbalancing Datasets to Enhance CNN Models Learnability: A Class-Wise Metrics-Based Closed-Loop Strategy Proposal by Somayeh Shahrabadi, Victor Alves, Emanuel Peres, Raul Morais Dos Santos, Telmo Adao

“…Using these datasets, 72 models with varying configurations – including different convolutional neural network architectures, initial learning rates, and optimizers – were initially trained and then evaluated against imagery test sets. …”
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A Malware Detection Method Based on Genetic Algorithm Optimized CNN-SENet Network by Zheng Yang, Hua Zhu, Zhao Li, Gang Wang, Meng Su

“…To this end, this paper proposes a malware detection method based on genetic algorithm optimization of the CNN-SENet network, which firstly introduces the SENet attention mechanism into the convolutional neural network to enhance the spatial feature extraction capability of the model; then, the application programming interface (API) sequences corresponding to different software behaviors are processed by segmentation and de-duplication, which in turn leads to the sequence feature extraction through the CNN-SENet model; finally, genetic algorithm is used to optimize the hyperparameters of CNN-SENet network to reduce the computational overhead of CNN and to achieve the recognition and classification of different malware at the output layer. …”
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TraitBertGCN: Personality Trait Prediction Using BertGCN with Data Fusion Technique by Muhammad Waqas, Fengli Zhang, Asif Ali Laghari, Ahmad Almadhor, Filip Petrinec, Asif Iqbal, Mian Muhammad Yasir Khalil

“…Abstract Personality prediction via different techniques is an established and trending topic in psychology. …”
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