Remaining Useful Life Prediction of Aero-Engine Based on Improved GWO and 1DCNN by Lihua Shen, Yucheng Wang, Baorui Du, Hailong Yang, He Fan

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Image Experience Prediction for Historic Districts Using a CNN-Transformer Fusion Model by Weijia Wang, Youping Teng, Lu Yan, Longwei Wu, Yinying Yang, Zijian Luo

“…The system employs a multi-view feature extraction framework, integrating VGG, ResNet CNNs, and the Swin Transformer model, resulting in a novel feature matrix. …”
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Impact of Input Image Resolution on Deep Learning Performance for Side-Scan Sonar Classification: An Accuracy–Efficiency Analysis by Xing Du, Yongfu Sun, Yupeng Song, Wanqing Chi, Lifeng Dong, Xiaolong Zhao

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Machine learning-based pattern recognition of Bender element signals for predicting sand particle-size by Yong-Hoon Byun, Juik Son, Jungmin Yun, Hyunwook Choo, Jongmuk Won

“…Abstract This study explores the potential of integrating bender element signals with a convolutional neural network (CNN) to predict the particle size distribution of relatively uniform sand. …”
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MFH-LPI: based on multi-view similarity networks fusion and hypergraph learning for long non-coding RNA-protein interactions prediction by Zengwei Xing, Shaoyou Yu, Shuzu Liao, Peng Wang, Bo Liao

“…Finally, we predict LPIs using a multilayer graph convolutional network (GCN) combined with a fully connected (FC) layer. …”
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Pears Internal Quality Inspection Based on X-Ray Imaging and Multi-Criteria Decision Fusion Model by Zeqing Yang, Jiahui Zhang, Zhimeng Li, Ning Hu, Zhengpan Qi

“…The proposed method combines manual feature-based classifiers, including Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), with a deep convolutional neural network (DCNN) model within an MCD-based fusion framework. …”
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Hybrid CNN-LSTM With Attention Mechanism for Robust Credit Card Fraud Detection by Iman Akour, Nour Mohamed, Said Salloum

“…This work not only contributes to the advancement of fraud detection techniques but also provides a framework for future research.…”
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Simulating clinical features on chest radiographs for medical image exploration and CNN explainability using a style-based generative adversarial autoencoder by Kyle A. Hasenstab, Lewis Hahn, Nick Chao, Albert Hsiao

“…Abstract Explainability of convolutional neural networks (CNNs) is integral for their adoption into radiological practice. …”
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Infrared Monocular Depth Estimation Based on Radiation Field Gradient Guidance and Semantic Priors in HSV Space by Rihua Hao, Chao Xu, Chonghao Zhong

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CMTNet: a hybrid CNN-transformer network for UAV-based hyperspectral crop classification in precision agriculture by Xihong Guo, Quan Feng, Faxu Guo

“…To address these challenges, we propose CMTNet, an innovative deep learning framework that integrates convolutional neural networks (CNNs) and Transformers for hyperspectral crop classification. …”
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Research on Pork Cut and Freshness Determination Method Based on Computer Vision by Shihao Song, Qiqi Guo, Xiaosa Duan, Xiaojing Shi, Zhenyu Liu

“…Finally, based on the PYQT5 framework, the MobileNetV3_Small model was deployed on a local client, realizing an efficient and accurate end-to-end automatic recognition system. …”
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CUNet-CLSTM: A Novel Fusion of CUNet and CLSTM for Superior Liver Cancer Detection in CT Scans by K. Vijayaprabakaran, Padmanaban Ramalingam, Rajakumar Ramalingam, A. Ilavendhan, R. Vedhapriyavadhana

“…This study proposes a novel architecture, the cascaded UNet convolutional long short-term model (CUNet-CLSTM), which leverages the strengths of UNet and convolutional long short-term memory to improve liver segmentation and tumor detection in CT images. …”
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Spectral-spatial wave and frequency interactive transformer for hyperspectral image classification by Tahir Arshad, Bo peng, Ali Rahman, Rahim khan, Sajid Ullah khan, Sultan Alnazi, Nazik Alturki

“…To address this limitation, we propose a Spectral-Spatial Wave and Frequency Interactive Transformer for HSI classification, which integrates frequency-aware and phase-aware token representations into a unified Transformer framework. Specifically, our model first employs a CNN backbone to extract shallow spectral-spatial features. …”
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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

“…Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of Ocular disease. …”
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Optimized Multi-Scale Detection and Numbering of Teeth in Panoramic Radiographs Using DentifyNet by Salih Taha Alperen Ozcelik, Huseyin Uzen, Abdulkadir Sengur, Muammer Turkoglu, Adalet Celebi, Nebras M. Sobahi

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COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings by Jordi Laguarta, Ferran Hueto, Brian Subirana

“…<italic>Methods:</italic> We developed an AI speech processing framework that leverages acoustic biomarker feature extractors to pre-screen for COVID-19 from cough recordings, and provide a personalized patient saliency map to longitudinally monitor patients in real-time, non-invasively, and at essentially zero variable cost. …”
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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

“…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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Hyperledger Fabric Graph Isomorphism Network for Conflict Transactions Detection in Multi-Version Concurrency Control by Fawaz Alzahrani, Mohd Yazid Idris, Mohd Fo'Ad Rohani, Rahmat Budiarto

“…Employing advanced graph neural networks, HFGIN utilizes node and edge data representations within a graph-based framework, which significantly increases the efficiency of detecting MVCC conflicts. …”
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Deep learning and hyperspectral features for seedling stage identification of barnyard grass in paddy field by Siqiao Tan, Qiang Xie, Wenshuai Zhu, Yangjun Deng, Lei Zhu, Xiaoqiao Yu, Zheming Yuan, Zheming Yuan, Yuan Chen, Yuan Chen

“…To explore the feasibility of hyperspectral identification of barnyard grass and rice in the seedling stage, we have pioneered the DeepBGS hyperspectral feature parsing framework. This approach harnesses the power of deep convolutional networks to automate the extraction of pertinent information. …”
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