Wavelet kernel and convolution neural network based accurate detection of incipient stator and rotor faults of induction motor by Sudeep Samanta, Jitendra Nath Bera, Amitava Biswas

“…This technique leverages the deep structures of CNNs to autonomously learn features from current signals, achieving a notable accuracy of above 97% in tests with both simulated model and two different hardware motor setup. The experimental result shows that it is capable of detecting as low as 1–2% of stator interturn fault with varying impedance in short circuit path as well as one broken rotor bar fault. …”
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LKDA-Net: Hierarchical transformer with large Kernel depthwise convolution attention for 3D medical image segmentation. by Ming Li, Jingang Ma, Jing Zhao

“…Firstly, inspired by the Swin Transformer module, we investigate different-sized large-kernel convolution attention mechanisms to obtain larger global receptive fields, and replace the MLP in the Swin Transformer with the Inverted Bottleneck with Depthwise Convolutional Augmentation to reduce channel redundancy and enhance feature expression and segmentation performance. …”
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Radio Frequency Signal-Based Drone Classification with Frequency Domain Gramian Angular Field and Convolutional Neural Network by Yuanhua Fu, Zhiming He

“…Moreover, to further improve the recognition performance, the images obtained from different channels are fused to serve as the input of a CNN classifier. …”
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Investigating the Impact of the Stationarity Hypothesis on Heart Failure Detection using Deep Convolutional Scattering Networks and Machine Learning by Mohamed Elmehdi Ait Bourkha, Dounia Nasir

“…In this paper, many contributions have been developed with the aim of enhancing automated detection of CVDs under the inter-patient paradigm, including using WSN in conjunction with different Machine Learning (ML) models and the stationarity hypothesis of ECG signals. …”
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Magnetic resonance imaging reconstruction algorithm under complex convolutional neural network in diagnosis and prognosis of cerebral infarction. by Jie Dong, Shujun Zhao, Yun Meng, Yong Zhang, Suxiao Li

“…The MRI images of patients with cerebral infarction in the dataset were undertaken as the data source, the average diffusion coefficient (DCavg) and apparent diffusion coefficient (ADC) values of different parts of the MRI image were measured, respectively. …”
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A Lightweight and Efficient Plant Disease Detection Method Integrating Knowledge Distillation and Dual-Scale Weighted Convolutions by Xiong Yang, Hao Wang, Qi Zhou, Lei Lu, Lijuan Zhang, Changming Sun, Guilu Wu

“…Subsequently, we developed the DSConv module—a novel convolutional structure employing double-scale weighted convolutions that dynamically adjust to different scale perceptions and optimize attention allocation. …”
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Classification of Chicken Carcass Breast Blood-Related Defects Using Hyperspectral Imaging Combined with Convolutional Neural Networks by Liukui Duan, Juanfang Bao, Hao Yang, Liuqian Gao, Xu Zhang, Shengjie Li, Huihui Wang

“…The combination of hyperspectral data and CNN can effectively accomplish the classification of CBDs, although different model architectures emphasize classification speed and accuracy differently. …”
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Analysis and Classification of Distress on Flexible Pavements Using Convolutional Neural Networks: A Case Study in Benin Republic by Crespin Prudence Yabi, Godfree F. Gbehoun, Bio Chéissou Koto Tamou, Eric Alamou, Mohamed Gibigaye, Ehsan Noroozinejad Farsangi

“…The accuracy achieved by the different models varies from 94.6% to 97.3%. This accuracy is promising compared to the literature models. …”
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LSTA-CNN: A Lightweight Spatiotemporal Attention-Based Convolutional Neural Network for ASD Diagnosis Using EEG by Jing Li, Xiangwei Jia, Xinghan Chen, Gongfa Li, Gaoxiang Ouyang

“…However, as a kind of neural electrophysiological signal, EEG contains different types of temporal and spatial information. …”
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AlexNet Convolutional Neural Network Architecture with Cosine and Hamming Similarity/Distance Measures for Fingerprint Biometric Matching by Ahmed Sabah Ahmed AL-Jumaili, Huda Kadhim Tayyeh, Abeer Alsadoon

“…Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. …”
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A Real-Time Polygonal Wheel-Rail Force Identification Method Based on Convolutional Neural Networks (CNN) by Zeteng Zhang, Jinhai Wang, Jianwei Yang, Dechen Yao

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Herbify: an ensemble deep learning framework integrating convolutional neural networks and vision transformers for precise herb identification by Farhan Sheth, Ishika Chatter, Manvendra Jasra, Gireesh Kumar, Richa Sharma

“…A novel dataset, Herbify, was compiled from two different herb datasets and refined through rigorous cleaning, preprocessing, and quality control procedures. …”
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ACU-Net: Attention-based convolutional U-Net model for segmenting brain tumors in fMRI images by Md Alamin Talukder, Md Abu Layek, Md Aslam Hossain, Md Aminul Islam, Mohammad Nur-e-Alam, Mohsin Kazi

“…Performance metrics, particularly Dice scores, were used to assess segmentation accuracy across different tumor regions, including whole tumor (WT), tumor core (TC), and enhancing tumor (ET) classes. …”
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Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images by Anand Kumar Gupta, Asadi Srinivasulu, Kamal Kant Hiran, Goddindla Sreenivasulu, Sivaram Rajeyyagari, Madhusudhana Subramanyam

“…To the last, the study outlines the advantages being offered by the proposed model with respect to other existing models by comparing different parameters of validation such as accuracy, error rate, data size, time complexity, and execution time.…”
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Face Anti-spoofing Detection Based on Novel Encoder Convolutional Neural Network and Texture’s Grayscale Structural Information by Marwa Radad, Amira E. Enab, Salah S. Elagooz, Nawal A. El-Fishawy, Mohamed A. El-Rashidy

“…The texture descriptors of LBP and LTP are utilized to extract the texture's grayscale structural information to overcome different environmental situations. This work combined the human face's intensity RGB color and grey texture categorization parameters to increase the detection accuracy of face spoofing and produce positive experimental outcomes. …”
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Grid Partition-Based Dynamic Spatial–Temporal Graph Convolutional Network for Large-Scale Traffic Flow Forecasting by Lifeng Gao, Liujia Chen, Agen Qiu, Qinglian Wang, Jianlong Wang, Cai Chen, Fuhao Zhang, Geli Ou’er

“…The performance of the proposed model was compared with that of different baseline models using two large-scale real-world datasets; the proposed model significantly outperformed the baseline models, demonstrating its potential effectiveness in managing large-scale traffic networks.…”
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Low illumination image enhancement algorithm of CycleGAN coal mine based on attention mechanism and Dilated convolution by Yuanbin WANG, Yaru GUO, Jia LIU, Xu WANG, Bingchao WU, Meng LIU

“…Patch-GAN is apply for the discriminator of the network, and the input is mapped into a matrix to pay more comprehensive attention to the details of different regions of the image, and improve the discriminator's resolution of image details. …”
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Federated learning-based hybrid convolutional recurrent neural network for multi-class intrusion detection in IoT networks by Prabu Selvam, P. Karthikeyan, S. Manochitra, A. V. L. N. Sujith, T. Ganesan, Rajaram Ayyasamy, Mohammed Shuaib, Shadab Alam, A. Rajendran

“…For multi-class intrusion detection in IoT networks, this study proposed an novel hybrid convolutional recurrent neural network (CRNN) model based on federated learning. …”
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Atrial Fibrillation Type Classification by a Convolutional Neural Network Using Contrast-Enhanced Computed Tomography Images by Hina Kotani, Atsushi Teramoto, Tomoyuki Ohno, Yoshihiro Sobue, Eiichi Watanabe, Hiroshi Fujita

“…In this study, we used contrast-enhanced computed tomography (CT) to classify atrial fibrillation (AF) into paroxysmal atrial fibrillation (PAF) and long-term persistent atrial fibrillation (LSAF), which have particularly different recurrence rates after catheter ablation. …”
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Transfer Learning to Detect COVID-19 Automatically from X-Ray Images Using Convolutional Neural Networks by Mundher Mohammed Taresh, Ningbo Zhu, Talal Ahmed Ali Ali, Asaad Shakir Hameed, Modhi Lafta Mutar

“…In this paper, the effectiveness of artificial intelligence (AI) in the rapid and precise identification of COVID-19 from CXR images has been explored based on different pretrained deep learning algorithms and fine-tuned to maximise detection accuracy to identify the best algorithms. …”
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