LCC-Net: Swin transformer-CNN hybrid for enhanced land cover classification in natural disaster monitoring by P. Shailaja, Pala Mahesh Kumar, Nalla Nikhitha, Kunta Neeraj Kumar Reddy, Enthala Mukesh Reddy, Goli Ganesh Reddy, Vadde Indu

“…However, existing methods need more accuracy due to varying image resolutions and difficulty distinguishing between similar land cover types under different disaster conditions. This research proposes a specialized network named the land cover classification network, referred to as LCC-Net, for classifying the land covers from satellite images. …”
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Software Defect Prediction Based on Effective Fusion of Multiple Features by Chaozheng Zhang, Junhua Wu

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Deep Learning Applications in Medical Image Processing by Ayşe Gül Eker, Nevcihan Duru

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A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot Learning by PAN Meiqi, HE Xing

“…In engineering practice, wind turbine fault diagnosis encounters situations where the fault category in the training data is different from the actual one. To diagnose unknown wind turbine faults, it is necessary to transfer the fault feature information learned during training to the unknown fault category. …”
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From Image to Sequence: Exploring Vision Transformers for Optical Coherence Tomography Classification by Amirali Arbab, Aref Habibi, Hossein Rabbani, Mahnoosh Tajmirriahi

“…Current methods for OCT image classification encounter specific challenges, such as the inherent complexity of retinal structures and considerable variability across different OCT datasets. Methods: This paper introduces a novel hybrid model that integrates the strengths of convolutional neural networks (CNNs) and vision transformer (ViT) to overcome these obstacles. …”
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Improved Technique in Arabic Handwriting Recognition by Ammar A. Al-Hamadani, Maad Kamal Al-Anni, Gamil R. S. Qaid, Najran Nasser Hamood

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Recent Advances in Fault Diagnosis Methods for Electrical Motors- A Comprehensive Review with Emphasis on Deep Learning by Jawad Faiz, F. Parvin

“…Additionally, it examines different datasets and features used in these methods, highlighting their advantages and limitations. …”
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Lamb wave ultrasonic testing technology for diagnosis and prediction of hidden cracks in metal structures by Dawen Yao, Peiwen Meng, Shuqi Li, Jinggang Xu

“…The system has a detection success rate of more than 88% for cracks of different sizes and types, and the error is controlled within 0.1 mm; the crack expansion prediction accuracy rate is 87.5%, which effectively predicts the development trend of cracks. …”
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Identification of Nanoparticle Agglomeration in Polymer Plates Reinforced with Carbon Nanotubes by Use of Active Thermography and Deep Learning-Based Image Processing by Mazharul Islam, Raihan Sarker

“…An eight-layer DCNN framework was implemented using three different window sizes of 41×41, 51×51, and 61×61 pixels, which was trained using 175,598 segments of images, of which 85% was made available for training, while 15% was used for evaluation. …”
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Building Fire Location Predictions Based on FDS and Hybrid Modelling by Yanxi Cao, Hongyan Ma, Shun Wang, Yingda Zhang

“…With the goal of addressing the difficulty of rapidly identifying the source of fire in commercial buildings, this study builds a numerical fire model based on the fire dynamics simulator (FDS) and combines it with a hybrid model to predict the location of a fire source. Different scenarios were built to simulate the spatial and temporal distributions of key parameters such as temperature, smoke, and CO concentration during the fire process. …”
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Two-Dimensional Coherent Polarization–Direction-of-Arrival Estimation Based on Sequence-Embedding Fusion Transformer by Zihan Wu, Jun Wang, Zhiquan Zhou

“…Additionally, an SEF module was proposed to fuse the spatial-polarization domain features from different dimensions. The module is a combination of a convolutional neural network (CNN) with local information extraction capabilities and a feature dimension transformation function, serving to improve the model’s ability to fuse information about features in the spatial-polarization domain. …”
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A new approach to estimate neighborhood socioeconomic status using supermarket transactions and GNNs by Eduardo Cruz, Monica Villavicencio, Carmen Vaca, Lisette Espín-Noboa, Nervo Verdezoto

“…Our proposed approach contributes to measuring socioeconomic status at the neighborhood level to support policymakers in making informed decisions about resource allocation according to the needs of different geographical areas.…”
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Improved Multi-Grained Cascade Forest Model for Transformer Fault Diagnosis by Yiyi Zhang, Yuxuan Wang, Jiefeng Liu, Heng Zhang, Xianhao Fan, Dongdong Zhang

“…Moreover, the proposed method still has high fault diagnosis accuracy for datasets of different sizes.…”
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Hyperspectral Image Classification Based on Two-Branch Feature Fusion Network by Qiongdan Huang, Liang Li, Mengyang Zhao, Jiapeng Wang, Shilin Kang

“…The spatial branch utilizes distance similarity metrics to capture the spatial relationships between central and neighboring pixels, and utilizes multiscale convolutional modules to expand the receptive field, capturing different levels of features and contextual information, resulting in more robust spatial information. …”
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Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments by Qianming SHANG, Wanying JIANG, Yi ZHOU, Zhengqiang WANG, Yubo SUN

“…Meanwhile, a novel parallel dual-channel convolutional neural network structure is designed to explore both global features and deeper, finer details of the data, thereby enhancing the diagnostic performance of the method in strong noise environments.ResultsExperimental evaluation results under different noise conditions show that the proposed method achieves a fault diagnosis accuracy of over 98% in environments with strong noise. …”
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CNN Issues in Skin Lesion Classification: Data Distribution and Quantity by Giuliana Ramella, Luca Serino

“…The experimental results, regarding four different scenarios, provide valuable insights into the design and utilization of CNNs for skin lesion classification, laying the groundwork for further investigations.…”
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Steady-State Visual-Evoked-Potential–Driven Quadrotor Control Using a Deep Residual CNN for Short-Time Signal Classification by Jiannan Chen, Chenju Yang, Rao Wei, Changchun Hua, Dianrui Mu, Fuchun Sun

“…The FB designed in this paper is composed of four sixth-order Butterworth filters with different bandpass ranges, and the four bandwidths are 19–50 Hz, 14–38 Hz, 9–26 Hz, and 3–14 Hz, respectively. …”
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Bearing Fault Diagnosis Grounded in the Multi-Modal Fusion and Attention Mechanism by Jianjian Yang, Haifeng Han, Xuan Dong, Guoyong Wang, Shaocong Zhang

“…Furthermore, it innovatively introduces the Channel-Based Multi-Head Attention (CBMA) mechanism for the efficient fusion of features from different modalities, maximizing the complementarity between signals. …”
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Diagnosis of array antennas based on near-field data using Faster R-CNN by Boguang Yang, Yulun Wei, Jixiang Shi, Tao Hong, Liangyu Li, Kai-Da Xu

“…In this paper, a source reconstruction method for detecting failures in array antenna elements using near-field data based on Faster region-convolutional neural network (Faster R-CNN) is introduced. …”
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Multi-Functional Optical Spectrum Analysis Using Multi-Task Cascaded Neural Networks by Haoyu Wang, Sheng Cui, Changjian Ke, Chenglong Yu, Zi Liang, Deming Liu

“…We demonstrate that, compared with the multi-task artificial neural network (MT-ANN) and convolutional neural network (MT-CNN), the proposed multi-task cascaded ANNs (CANN) and cascaded CNNs (CCNN) can greatly improve the OSA performance and accelerate the training process by exploiting specific features and loss functions for different tasks. …”
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