A hybrid deep learning-based approach for optimal genotype by environment selection by Zahra Khalilzadeh, Motahareh Kashanian, Saeed Khaki, Lizhi Wang

“…The ability to accurately predict the yields of different crop genotypes in response to weather variability is crucial for developing climate resilient crop cultivars. …”
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Research on Hybrid Architecture Neural Networks for Time Series Prediction by Fujin Zhuang, Xiao Chen, Punyaphol Horata, Khamron Sunat

“…In the model architecture design, the TCN module utilizes causal convolution structure and dilated convolution to handle long-term temporal dependencies; the MLP module performs feature transformation and nonlinear mapping through a two-layer network; and the attention mechanism identifies important features at key time steps. …”
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TCN-GRU Based on Attention Mechanism for Solar Irradiance Prediction by Zhi Rao, Zaimin Yang, Xiongping Yang, Jiaming Li, Wenchuan Meng, Zhichu Wei

“…Considering the time series nature of the GHI and monitoring sites dispersed over different latitudes, longitudes, and altitudes, this study proposes a model combining deep neural networks and deep convolutional neural networks for the multi-step prediction of GHI. …”
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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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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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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

“…Finally, the AVMD-TEO-MPE -based features are used to train the one-dimensional convolutional neural network (1D-CNN) as a deep learning classifier. …”
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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

“…This study provides an in-depth exploration of the use of multimodal techniques in developing adaptive learning systems designed for students with special needs using various neural network models: a Baseline Neural Network, Convolutional Neural Network, Attention Model, LSTM, GRU, and Transformer models. …”
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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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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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MCFTNet: Multimodal Cross-Layer Fusion Transformer Network for Hyperspectral and LiDAR Data Classification by Wei Huang, Tianren Wu, Xueyu Zhang, Liangliang Li, Ming Lv, Zhenhong Jia, Xiaobin Zhao, Hongbing Ma, Gemine Vivone

“…Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral imaging (HSI) classification tasks. …”
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Fine-Grained Recognition of Fidgety-Type Emotions Using Multi-Scale One-Dimensional Residual Siamese Network by Jiu SUN, Junxin ZHU, Jun SHAO

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A Deep Learning Approach to Evaluating SISO-OFDM Channel Equalization by Saja S Hanoon, Hasan F Khazal, Thamer M Jamel

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A Cluster-based Optimal Scheduling Strategy for Electric Vehicles Considering User Participation by HAN Yan, DING Xiying, CHENG Kun, LI Xiaodong

“…Multilayer perceptron neural network is adopted to predict power load and obtain peak difference. The electric vehicle cluster classification network based on convolutional neural network is trained by using a large number of vehicle information and considering vehicle owner intention to classify the vehicle peak shaving participation, and quickly determine the total electricity involved in peak shaving. …”
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Time Series Forecasting for Air Quality with Structured and Unstructured Data Using Artificial Neural Networks by Kenneth Chan, Paul Matthews, Kamran Munir

“…This paper presents a study of developing a new model, the “Convolutional Neural Network, Recurrent Neural Network Dual Input Model” (CORD). …”
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