Calculus accumulation-based method for extracting values of instrumentation image by Dianming Wang, Li Zhou, Xiangxin Chen, Mingxin Yi, Xiaoju Yin

“…A double-pointer meter reading recognition scheme based on two-dimensional convolution and calculus accumulation is proposed. The weighted multifeature matching algorithm is used to accurately obtain the center coordinates of the rotating shaft to improve the success rate of meter image recognition. …”
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Prediction and classification of wheat plant disease in Pakistan using deep learning techniques by Muhammad Usman Malik, Muhammad Faisal Hayat

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RS-SCBiGRU: a noise-robust neural network for high-speed motor fault diagnosis with limited samples by Sun Fenghao, Li Guofa, He Jialong, Liu Shaoyang

“…Furthermore, to enhance the richness of feature representations and strengthen information exchange between different feature channels, this paper proposes a frequency-adaptive convolutional layer (SCNET), which significantly optimizes the performance of Bidirectional Gated Recurrent Units (BiGRU) in fault feature extraction. …”
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Optimizing AlexNet for accurate tree species classification via multi-branch architecture and mixed-domain attention by Jianjianxian Liu, Tao Xing, Xiangyu Wang

“…The multi-branch convolutional module extracts diverse features by processing input with branches of different kernel sizes, capturing both fine and global details. …”
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Deep CNN ResNet-18 based model with attention and transfer learning for Alzheimer's disease detection by Sofia Biju Francis, Sofia Biju Francis, Jai Prakash Verma

“…Alzheimer's disease leads to a rapid and irreversible decline in cognitive abilities by damaging memory cells.MethodsA ResNet-18-based system is proposed, integrating Depth Convolution with a Squeeze and Excitation (SE) block to minimize tuning parameters. …”
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Building Surface Defect Detection Based on Improved YOLOv8 by Xiaoxia Lin, Yingzhou Meng, Lin Sun, Xiaodong Yang, Chunwei Leng, Yan Li, Zhenyu Niu, Weihao Gong, Xinyue Xiao

“…The dataset used in this study contains six common building surface defects, and the images are captured in diverse scenarios with different lighting conditions, building structures, and ages of material. …”
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Aerial Biological Target Classification Based on Time–Frequency Multi-Scale Feature Fusion Network by Lianjun Wang, Rui Wang, Weidong Li, Jiangtao Wang, Yujia Yan, Cheng Hu

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BCSnet: A U-Net-Based Model for Segmentation of Brain Cells in Trypan Blue Images by Aleksei A. Kudryavtsev, Ivan V. Simkin, Maksim A. Dragun, Olga P. Alexandrova, Ivan P. Malashin, Denis A. Sukhanov, Vladimir A. Nelyub, Aleksei S. Borodulin, Stanislav O. Yurchenko, Vadim S. Tynchenko

“…This method requires a lot of time and effort associated with the need for manual cell counting and qualification in histology to detect visual differences between alive neuron cells and non-alive ones. …”
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Neural network pruning based on channel attention mechanism by Jianqiang Hu, Yang Liu, Keshou Wu

“…Network pruning facilitates the deployment of convolutional neural networks in resource-limited environments by reducing redundant parameters. …”
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Capsule network based analysis of histopathological images of oral squamous cell carcinoma by Santisudha Panigrahi, Jayshankar Das, Tripti Swarnkar

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Faster R-CNN model for target recognition and diagnosis of scapular fractures by Qiong Fang, Anhong Jiang, Meimei Liu, Sen Zhao

“…Objective: This study aims to establish a diagnostic model for scapular fractures using a convolutional neural network (CNN) and to discuss the clinical advantages of this model in diagnosing such complex conditions. …”
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Rapid diagnosis of rheumatoid arthritis and ankylosing spondylitis based on Fourier transform infrared spectroscopy and deep learning by Wei Shuai, Xue Wu, Chen Chen, Enguang Zuo, Xiaomei Chen, Zhengfang Li, Xiaoyi Lv, Lijun Wu, Cheng Chen

“…Three multi-scale convolutional modules with different specifications were designed based on the multi-scale convolutional neural network (MSCNN) to effectively fuse the local features to enhance the generalization ability of the model. …”
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Unraveling Parkinson's disease motor subtypes: A deep learning approach based on spatiotemporal dynamics of EEG microstates by Lin Meng, Deyu Wang, Jun Ma, Yu Shi, Hongbo Zhao, Yanlin Wang, Qingqing Shi, Xiaodong Zhu, Dong Ming

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A depth-wise separable VGG19-capsule network for enhanced bell pepper and grape leaf disease classification with ensemble activation by Midhun P Mathew, Sudheep Elayidom, Jagathy Raj V P, Abubeker K M

“…The novel contribution lies in the enhanced VGG19 architecture, incorporating depth-wise separable convolution, batch normalization, and a 40% dropout by introducing convolutional layers before the primary capsule layer. …”
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A Lightweight Network for Water Body Segmentation in Agricultural Remote Sensing Using Learnable Kalman Filters and Attention Mechanisms by Dingyi Liao, Jun Sun, Zhiyong Deng, Yudong Zhao, Jiani Zhang, Dinghua Ou

“…This paper proposed a lightweight and efficient learnable Kalman filter and Deformable Convolutional Attention Network (LKF-DCANet). The encoder is built using a shallow Channel Attention-Enhanced Deformable Convolution module (CADCN), while the decoder combines a Convolutional Additive Token Mixer (CATM) and a learnable Kalman filter (LKF) to achieve adaptive noise suppression and enhance global context modeling. …”
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Performance of deep learning models for the classification and object detection of different oral white lesions using photographic images by Siribang-on Piboonniyom Khovidhunkit, Kunchidsong Phosri, Bhornsawan Thanathornwong, Dulyapong Rungraungrayabkul, Suvit Poomrittigul, Treesukon Treebupachatsakul

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Comparative Analysis of Single-Channel and Multi-Channel Classification of Sleep Stages Across Four Different Data Sets by Xingjian Zhang, Gewen He, Tingyu Shang, Fangfang Fan

“…While both single- and multi-channel data are commonly used in automatic sleep staging, limited research has adequately investigated the differences in their effectiveness. <b>Methods:</b> In this study, four public data sets—Sleep-SC, APPLES, SHHS1, and MrOS1—are utilized, and an advanced hybrid attention neural network composed of a multi-branch convolutional neural network and the multi-head attention mechanism is employed for automatic sleep staging. …”
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CCD-Net: Color-Correction Network Based on Dual-Branch Fusion of Different Color Spaces for Image Dehazing by Dongyu Chen, Haitao Zhao

“…Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown significant improvements by directly learning features from hazy images to produce clear outputs. …”
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Muscle Fat and Volume Differences in People With Hip‐Related Pain Compared With Controls: A Machine Learning Approach by Chris Stewart, Evert O. Wesselink, Zuzana Perraton, Kenneth A. Weber II, Matthew G. King, Joanne L. Kemp, Benjamin F. Mentiplay, Kay M. Crossley, James M. Elliott, Joshua J. Heerey, Mark J. Scholes, Peter R. Lawrenson, Chris Calabrese, Adam I. Semciw

“…Results When considering adjusted estimates of muscle volume, there were significant differences observed between groups for gluteus medius (adjusted mean difference 23 858 mm3 [95% confidence interval 7563, 40 137]; p = 0.004) and tensor fascia latae (6660 mm3 [2440, 13 075]; p = 0.042). …”
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Enhancing the trustworthiness of chaos and synchronization of chaotic satellite model: a practice of discrete fractional-order approaches by Saima Rashid, Sher Zaman Hamidi, Saima Akram, Moataz Alosaimi, Yu-Ming Chu

“…For achieving the intended formation, a framework of a discrete fractional difference satellite model is constructed by the use of commensurate and non-commensurate orders for the control and synchronization of fractional-order chaotic satellite system. …”
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