A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning by Amin Pourmahboubi, Nazanin Arsalani Saeed, Hamed Tabrizchi

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Fault diagnosis of ZDJ7 railway point machine based on improved DCNN and SVDD classification by Zengshu Shi, Yiman Du, Xinwen Yao

“…It has a good anti‐noise performance under different convolution kernels and SNRs. When the sample distribution is unbalanced, the performance indexes obtained by the proposed model are the best.…”
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Calculus accumulation-based method for extracting values of instrumentation image by Dianming Wang, Li Zhou, Xiangxin Chen, Mingxin Yi, Xiaoju Yin

“…Through the experimental comparison and analysis of outdoor dust occlusion, droplet occlusion, leaf occlusion, and different lighting conditions, the results show that the algorithm has good robustness, is almost not disturbed by the external environment, and has high recognition accuracy. …”
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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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Enhancing Landmark Point Detection in <i>Eriocheir Sinensis</i> Carapace with Differentiable End-to-End Networks by Chong Wu, Shuxian Wang, Shengmao Zhang, Hanfeng Zheng, Wei Wang, Shenglong Yang

“…A 37-point localization framework was developed for the carapace, with the dataset augmented through random distortions, rotations, and occlusions to enhance generalization capability. Three types of convolutional neural network models were used to compare detection accuracy, generalization ability, and model power consumption, with different loss functions compared. …”
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Detecting performance difficulty of learners in colonoscopy: Evidence from eye-tracking by Xin Liu, Bin Zheng, Xiaoqin Duan, Wenjing He, Yuandong Li, Jinyu Zhao, Chen Zhao, Lin Wang

“…Basic human eye gaze and pupil characteristics were learned and verified by the deep convolutional generative adversarial networks (DCGANs); the generated data were fed to the Long Short-Term Memory (LSTM) networks with three different data feeding strategies to classify MNLs from the entire colonoscopic procedure. …”
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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

“…Additionally, pre-trained ResNet-18 models were created with and without the SE block to compare ROC and accuracy values across different hyperparameters.ResultsThe proposed model achieved ROC values of 95% for Alzheimer's Disease (AD), 95% for Cognitively Normal (CN), and 93% for Mild Cognitive Impairment (MCI), with a maximum test accuracy of 88.51%. …”
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Influence of land cover on noise simulation output – A case study in Malmö, Sweden by Pantazatou Karolina, Mattisson Kristoffer, Olsson Per-Ola, Telldén Erik, Kettisen Anders, Hosseinvash Azari Soraya, Liu Wenjing, Harrie Lars

“…This study examines how different LC datasets influence simulated environmental noise levels of road traffic using Nord2000 in an urban area of 1 km2 in southern Sweden. …”
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An islanding detection method for grid-connect inverter based on parameter optimized variational mode decomposition and deep learning by Yan Xia, Yan Xia, Yuli Lv, Feihong Yu, Yiqiang Yang, Yili Yang, Wei Li, Ke Li

“…Simulation results indicate that the proposed method can effectively differentiate the islanding state under different working conditions with a testing accuracy level of 100% within a maximal detection time of 46.402 ms. …”
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Multi-Modal AI for Multi-Label Retinal Disease Prediction Using OCT and Fundus Images: A Hybrid Approach by Amina Zedadra, Mahmoud Yassine Salah-Salah, Ouarda Zedadra, Antonio Guerrieri

“…These results confirm the robustness, reliability, and generalization capability of the proposed approach across different imaging modalities.…”
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Text Analysis of Digital Commentary on Ice and Snow Tourism Based on Artificial Intelligence and Long Short-Term Memory Neural Network by Qi Zhuang, Zhengjie Chu, Jun Li

“…In particular, the model shows superior performance in the sentiment classification of long review texts, effectively enhancing the accuracy and granularity of sentiment recognition through dynamic convolution and the self-attention mechanism. Moreover, the model distinguishes sentiment tendencies across different user groups regarding their experiences in ice and snow tourism. …”
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Advancements in Efficient Underwater Image Restoration Using ETransMapNet for Enhanced Dehazing by C. P. Indumathi, Haya Mesfer Alshahrani, N. A. Natraj, C. H. Sarada Devi

“…ETransMapNet extracts features using three convolution kernels of different sizes (3 × 3, 5 × 5, and 7 × 7). …”
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AI-Driven Innovation Using Multimodal and Personalized Adaptive Education for Students With Special Needs by Nesren Farhah, Muhammad Adnan, Ahmed Abdullah Alqarni, M. Irfan Uddin, Theyazn H. H. Aldhyani

“…These findings suggest that more advanced and intelligent neural networks can improve how students with different learning abilities consume content and, consequently, provide better educational answers and solutions to the market. …”
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SGCL-LncLoc: An Interpretable Deep Learning Model for Improving lncRNA Subcellular Localization Prediction with Supervised Graph Contrastive Learning by Min Li, Baoying Zhao, Yiming Li, Pingjian Ding, Rui Yin, Shichao Kan, Min Zeng

“…Then, SGCL-LncLoc applies graph convolutional networks to learn the comprehensive graph representation. …”
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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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Short-term train arrival delay prediction: a data-driven approach by Qingyun Fu, Shuxin Ding, Tao Zhang, Rongsheng Wang, Ping Hu, Cunlai Pu

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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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