Prediction and Parameter Optimization of Surface Settlement Induced by Shield Tunneling Using Improved Informer Algorithm by Shisen Zhao, Xianda Feng, Kefeng Peng

“…The improved Informer algorithm exhibited significant enhancements in prediction accuracy, perception range across different timescales, and computational efficiency. …”
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Remote Sensing Image-Based Building Change Detection: A Case Study of the Qinling Mountains in China by Lei Fu, Yunfeng Zhang, Keyun Zhao, Lulu Zhang, Ying Li, Changjing Shang, Qiang Shen

“…With the widespread application of deep learning in Earth observation, remote sensing image-based building change detection has achieved numerous groundbreaking advancements. However, differences across time periods caused by temporal variations in land cover, as well as the complex spatial structures in remote sensing scenes, significantly constrain the performance of change detection. …”
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A Comprehensive Review on Unsupervised Domain Adaptation for 3D Segmentation and Reconstruction in CT Urography Imaging by Shreya, Sushanth, Dasharathraj K. Shetty, Shreepathy Ranga Bhatta, Nikita Panwar

“…The most important part of this review is the discussion on 3D kidney segmentation and reconstruction from urographic images, which has helped doctors a lot with the accurate diagnosis and planning of treatment for kidney diseases. Even though 3D convolution networks have been used a lot in medical picture segmentation, it can be hard to adapt them to clinical data from different modalities that have not been seen before. …”
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Analysis of the criteria selection problem in diversification models by Анна Бакурова, Алла Савранська, Еліна Терещенко, Дмитро Широкорад, Марк Шевчук

“…To formalize the problem, five models are proposed that differ in vector objective functions, both in the quantity and quality of the selected criteria. …”
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Analysis of the criteria selection problem in diversification models by Анна Бакурова, Алла Савранська, Еліна Терещенко, Дмитро Широкорад, Марк Шевчук

“…To formalize the problem, five models are proposed that differ in vector objective functions, both in the quantity and quality of the selected criteria. …”
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Analysis of the criteria selection problem in diversification models by Анна Бакурова, Алла Савранська, Еліна Терещенко, Дмитро Широкорад, Марк Шевчук

“…To formalize the problem, five models are proposed that differ in vector objective functions, both in the quantity and quality of the selected criteria. …”
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Analysis of the criteria selection problem in diversification models by Анна Бакурова, Алла Савранська, Еліна Терещенко, Дмитро Широкорад, Марк Шевчук

“…To formalize the problem, five models are proposed that differ in vector objective functions, both in the quantity and quality of the selected criteria. …”
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Enhanced climate change resilience on wheat anther morphology using optimized deep learning techniques by Arifa Zahir, Zulfiqar Ali, Ahmad Sami Al-Shamayleh, Syed Raza Ab bas, Basharat Mahmood, Abdullah Hussein Al-Ghushami, Rubina Adnan, Adnan Akhunzada

“…By using object identification techniques, the research accurately measures the length and width of each anther in images, offering valuable insights into the differences between various wheat varieties. Furthermore, Deep Learning (DL) methodologies are utilized to enhance agriculture, specifically employing record categorization to advance plant breeding management. …”
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HDTFF-Net: Hierarchical Deep Texture Features Fusion Network for High-Resolution Remote Sensing Scene Classification by Wanying Song, Yifan Cong, Shiru Zhang, Yan Wu, Peng Zhang

“…Fusing features from different feature descriptors or different convolutional layers can improve the understanding of scene and enhance the classification accuracy. …”
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Improved YOLOv8-Based Algorithm for Citrus Leaf Disease Detection by Zhengbing Zheng, Yibang Zhang, Luchao Sun

“…The proposed approach uses YOLOv8n as the base model and introduces adaptive convolution into the Backbone, allowing the model to dynamically prioritize different disease features. …”
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Boosting Arabic text classification using hybrid deep learning approach by Eman Alnagi, Rawan Ghnemat, Qasem Abu Al-Haija

“…Lastly, comparing with the state-of-the-art models revealed the superiority of our hybrid model, which outperformed the other architectures in the same area of study, the accuracies have been improved by 1% to 30% for the different datasets.…”
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From Binary to Multi-Class: Neural Networks for Structural Damage Classification in Bridge Monitoring Under Static and Dynamic Loading by Andreas Kardoulias, Alexandros Arailopoulos, Panagiotis Seventekidis

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Ultra Short-Term Charging Load Forecasting Based on Improved Data Decomposition and Hybrid Neural Network by Shaoyang Yin, Zhaohui Chen, Wanyuan Liu, Zhiwen Su

“…The experimental results show that compared with single models, the proposed model performs better in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared in three different scenarios, proving that the model has high prediction accuracy and good robustness in ultra-short-term charging load prediction.…”
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Studying the influence of correction codes on coherent reception of M-PSK signals in the presence of noise and harmonic interference by V. D. Nguyen

“…One of the important requirements for the quality of data transmission is the system error tolerance. There are different ways of improving the quality of information transmission. …”
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StaEn-IDS: An Explainable Stacking Ensemble Deep Neural Network-Based Intrusion Detection System for IoT by Monika Vishwakarma, Nishtha Kesswani

“…With millions of connected devices from different manufacturers using various protocols and IoT applications, ensuring IoT security is challenging. …”
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Combustion Field Prediction and Diagnosis via Spatiotemporal Discrete U-ConvLSTM Model by Xiaodong Huang, Xiaojian Hao, Baowu Pan, Shaogang Chen, Shenxiang Feng, Pan Pei

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Ultra-short-term Multi-region Power Load Forecasting Based on Spearman-GCN-GRU Model by Junying WU, Xin LU, Hong LIU, Bin ZHANG, Shouliang CHAI, Yunchun LIU, Jianan WANG

“…Firstly, the Spearman correlation coefficient is used to analyze the spatial-temporal correlation of power load in different regions and construct the Spearman adjacency matrix. …”
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3D densely connected CNN with multi-scale receptive fields and hybrid loss for brain tumor segmentation by Fatemehzahra Adib, Maryam Amirmazlaghani, Mohammad Rahmati

“…This paper presents a 3D convolutional neural network (CNN) model to automatically segment brain tumors from MRI scans. …”
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A multi-dimensional data-driven ship roll prediction model based on VMD-PCA and IDBO-TCN-BiGRU-Attention by Huifeng Wang, Jianchuan Yin, Jianchuan Yin, Nini Wang, Lijun Wang, Lijun Wang

“…This model integrates data decomposition, dimensionality reduction, deep learning, and optimization techniques.MethodsThe model uses the variational mode decomposition (VMD) method to break down the ship’s roll motion data into components at different scales. This improves the smoothness of the data. …”
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Short-Term Prediction of Ship Heave Motion Using a PSO-Optimized CNN-LSTM Model by Guowei Li, Gang Tang, Jingyu Zhang, Qun Sun, Xiangjun Liu

“…The paper begins by establishing the ship heave motion model based on the P–M spectrum and slice theory, simulating the ship heave motion curve under different sea conditions on MATLAB. This simulation provides crucial data for the subsequent prediction model. …”
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