VGG-MFO-orange for sweetness prediction of Linhai mandarin oranges by Chun Fang, Runhong Shen, Meiling Yuan, ZhengXu, Wangyi Ye, Sheng Dai, Di Wang

“…In this paper, a new Attention for Orange (AO) attention mechanism and Multiscale Feature Optimization (MFO) feature extraction module are designed and combined with VGG13 convolutional neural network (CNN), innovatively proposed VGG-MFO-Orange CNN model for accurately classifying mandarin oranges with different sweetness. …”
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AngleCam: Predicting the temporal variation of leaf angle distributions from image series with deep learning by Teja Kattenborn, Ronny Richter, Claudia Guimarães‐Steinicke, Hannes Feilhauer, Christian Wirth

“…AngleCam is based on pattern recognition with convolutional neural networks and trained with leaf angle distributions obtained from visual interpretation of more than 2500 plant photographs across different species and scene conditions. …”
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Development of a Deep Learning‐Assisted Mobile Application for the Identification of Nematodes Through Microscopic Images by Naseeb Singh, Ashish Kumar Singh, L. K. Dhruw, Simardeep Kaur, S. Hazarika, K. K. Mishra, V. K. Mishra, Laxmi Kant

“…A novel lightweight convolutional neural network (CNN) was developed to identify the nematodes belonging to different trophic groups (Heterorhabditis indica, Meloidogyne incognita, Helicotylenchus, Anguina tritici, and Xiphinema). …”
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Self-Supervised Social Recommendation Algorithm Fusing Residual Networks by WANG Yujie, YANG Zhe

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A noninvasive hyperkalemia monitoring system for dialysis patients based on a 1D-CNN model and single-lead ECG from wearable devices by Haijie Shang, Shaobin Yu, Yihan Wu, Xu Liu, Jiayuan He, Min Ma, Xiaoxi Zeng, Ning Jiang

“…The model automatically extracts features from ECG signals at different frequencies through multiple convolutional channels, eliminating the need for manual feature extraction before data input. …”
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The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning by Fatemeh Bahrambanan, Meysam Alizamir, Kayhan Moradveisi, Salim Heddam, Sungwon Kim, Seunghyun Kim, Meysam Soleimani, Saeid Afshar, Amir Taherkhani

“…Based on feature selection models, four different scenarios were developed and five, ten, twenty and thirty features selected for designing a more accurate classification paradigm. …”
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Semantic Tokenization-Based Mamba for Hyperspectral Image Classification by Ri Ming, Na Chen, Jiangtao Peng, Weiwei Sun, Zhijing Ye

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Blockchain enabled IoMT and transfer learning for ocular disease classification by Muhammad Adnan Khan, Muhammad Zahid Hussain, Muhammad Farhan Khan, Munir Ahmad, Sagheer Abbas, Tehseen Mazhar, Tariq Shahzad, Mamoon M. Saeed

“…In the proposed work, six different automated convolutional neural network architectures based on the Internet of Medical Things (IoMT) using transfer learning techniques were implemented for the classification of fundus images that can detect ocular diseases. …”
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SlowFast-TCN: A Deep Learning Approach for Visual Speech Recognition by Nicole Yah Yie Ha, Lee-Yeng Ong, Meng-Chew Leow

“…Consequently, there is less temporal information for distinguishing between different viseme classes, leading to increased visual ambiguity during classification. …”
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Using deep learning for thyroid nodule risk stratification from ultrasound images by Yasaman Sharifi, Morteza Danay Ashgzari, Susan Shafiei, Seyed Rasoul Zakavi, Saeid Eslami

“…Our proposed automated method has four main steps: preprocessing and image augmentation, nodule detection, nodule classification on the basis of ACR-TIRADS, and risk-level stratification and treatment management. We trained different state-of-the-art pretrained convolutional neural networks (CNNs) to choose the best architecture in the detection and classification stage. …”
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3D-CNN with Multi-Scale Fusion for Tree Crown Segmentation and Species Classification by Jiayao Wang, Zhen Zhen, Yuting Zhao, Ye Ma, Yinghui Zhao

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Cimiciato defect detection in hazelnuts: CNN models applied on X-ray images by Andrea Vitale, Matteo Giaccone, Antonio Gaetano Napolitano, Flavia de Benedetta, Laura Gargiulo, Giacomo Mele

“…Currently used methods for identifying insect damages (cimiciato) often rely on visual inspection, external imaging or require destructive testing.This study compared twelve different pretrained Convolutional Neural Network (CNN) architectures applied on hazelnut kernels X-ray radiographs for the automated detection of cimiciato defects.Through an extensive training and validation process, followed by testing on a separate dataset, InceptionV3 architecture showed the best overall balance across all performance metrics, including accuracy, sensitivity, and precision, while Xception demonstrated superior specificity and the lowest false positive rate. …”
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STIED: a deep learning model for the spatiotemporal detection of focal interictal epileptiform discharges with MEG by Raquel Fernández-Martín, Alfonso Gijón, Odile Feys, Elodie Juvené, Alec Aeby, Charline Urbain, Xavier De Tiège, Vincent Wens

“…The model trained on the FE group also showed promising results when applied to a separate group of presurgical patients with different types of refractory focal epilepsy, though further work is needed to distinguish IEDs from physiological transients. …”
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Deep Models for Stroke Segmentation: Do Complex Architectures Always Perform Better? by Ahmed Soliman, Yalda Zafari-Ghadim, Yousif Yousif, Ahmed Ibrahim, Amr Mohamed, Essam A. Rashed, Mohamed A. Mabrok

“…These findings suggest that proposed complex architectures may be task-specific and simpler models with appropriate pre-/post-processing pipeline can be equally or more effective in generalization across different tasks in medical image segmentation.…”
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Insights into gait performance in Parkinson's disease via latent features of deep graph neural networks by Jiecheng Wu, Jiecheng Wu, Ning Su, Xinjin Li, Xinjin Li, Chao Yao, Jipeng Zhang, Xucheng Zhang, Wei Sun

“…This allowed us to explore how the model's parameters (different ST-GCN Layers) could assist clinicians in understanding.ResultsThe dataset used to evaluate the model in this paper includes motion data from 65 PD participants and 77 healthy control participants. …”
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Fracture identification and 3D reconstruction of coal-rock combinations based on VRA-UNet network by Dengke WANG, Longhang WANG, Yaguang QIN, Le WEI, Tanggen CAO, Wenrui LI, Lu LI, Xu CHEN, Yuling XIA

“…Finally, an asymmetric atrous pyramid module (AC-ASPP) utilizing convolution kernels of different scales is added at the end of the downsampling, which reduced the computational complexity and improved the computational efficiency of the model while keeping the receptive field unchanged. …”
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Comparative Analysis of AI Models for Atypical Pigmented Facial Lesion Diagnosis by Alessandra Cartocci, Alessio Luschi, Linda Tognetti, Elisa Cinotti, Francesca Farnetani, Aimilios Lallas, John Paoli, Caterina Longo, Elvira Moscarella, Danica Tiodorovic, Ignazio Stanganelli, Mariano Suppa, Emi Dika, Iris Zalaudek, Maria Antonietta Pizzichetta, Jean Luc Perrot, Gabriele Cevenini, Ernesto Iadanza, Giovanni Rubegni, Harald Kittler, Philipp Tschandl, Pietro Rubegni

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Fault diagnosis and inference of hoist main bearing based on transfer learning and ontology by Fei DONG, Di ZHANG, Kunpeng GE, Junjie CHEN, Xinyue XU

“…To overcome the challenges still faced by data-driven hoist main bearing fault diagnosis methods, including data imbalance due to a lack of fault samples under real operating conditions, diagnostic performance degradation of fault diagnosis models caused by significant differences in data sample distribution under varying conditions, single fault diagnosis function, and a lack of reasoning analysis and localization for the causes of hoist main bearing system failures, a new fault diagnosis and reasoning method for hoist main bearing systems is studied, which includes two aspects: ① Bearing fault diagnosis based on convolutional neural network transfer learning and domain adaptation. …”
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A fault diagnosis method for rolling bearings in open-set domain adaptation with adversarial learning by Tongfei Lei, Feng Pan, Jiabei Hu, Xu He, Bing Li

“…Abstract The closed-set assumption often fails in practical industrial applications, especially considering diverse working conditions where the data distribution may differ significantly. In light of this, a domain adaptation method with adversarial learning is designed for open-set fault diagnosis. …”
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Double-path multiscale adaptive compressed sensing network for electronic data by Lubin Yu, Yongsheng Huang, Yiqiang Cheng, Qiliang Du, Zhenwei Zhou, Lianfang Tian, Shilie He, Honghui Liu

“…Then, the secondary reconstruction module uses the adaptive dilated convolution residual module to adaptively adjust the size of the convolution kernel to ensure the high-quality reconstruction of different signals and combines it with the tree-like structure residual block for enhanced reconstruction. …”
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