A text classification method based on a convolutional and bidirectional long short-term memory model by Hai Huan, Zelin Guo, Tingting Cai, Zichen He

“…In response to this problem, a text classification method based on the CBM (Convolutional and Bi-LSTM Model) model, which can extract shallow local semantic features and deep global semantic features, is proposed. …”
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A spatially aware global and local perspective approach for few-shot incremental learning by Heng Wu, Zijun Zheng, Laishui Lv, Yifeng Xu, Dalal Bardou, Shanzhou Niu, Gaohang Yu, Yinyin Wang

“…In light of this, in this paper, we propose a Spatially Aware Global and Local Perspectives (SGLP) approach to tackle the few-shot incremental learning problem. To enhance semantic representations of features, we build the relationship information of the spatial feature in the global scope and encourage the model to pay attention to the dominant region in features. …”
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The differentiation of regions by means of the hierarchical method of cluster analysis and environmental data by N. A. Boyko, E. Sh. Sibukaev

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A Novel Tree-Based Combined Test for Seasonality by Karsten Webel, Daniel Ollech

“…Treating the detection of seasonality as a classification problem and the tests’ p-values as correlated predictors, the first step is to identify the most important tests in the ensemble via recursive feature elimination in multiple random forests of such trees; the second step is to grow and prune a single tree based upon information from only these identified tests. …”
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A Multi-Layer Attention Knowledge Tracking Method with Self-Supervised Noise Tolerance by Haifeng Wang, Hao Liu, Yanling Ge, Zhihao Yu

“…In the pre-training stage, MASKT uses a random forest method to filter out positive and negative correlation feature embeddings; then, it reuses noise-processed restoration tasks to extract more learnable features and enhance the learning ability of the model. …”
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Effective Algorithm for Biomedical Image Segmentation by Zhao Di, Tang Yi, A. B. Gourinovitch

“…Objects in medical images have different scales, types, complex backgrounds, and similar tissue appearances, making information extraction challenging. To solve this problem, a module is proposed that takes into account the features of images, which will improve the biomedical image segmentation network FE-Net. …”
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Rapid screening and optimization of CO2 enhanced oil recovery operations in unconventional reservoirs: A case study by Shuqin Wen, Bing Wei, Junyu You, Yujiao He, Qihang Ye, Jun Lu

“…To enhance the interpretability of the established models, the multiway feature importance analysis and Shapley Additive Explanations (SHAP) were proposed to quantify the contribution of individual features to the model output. …”
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Deep Learning Architectures for Single-Label and Multi-Label Surgical Tool Classification in Minimally Invasive Surgeries by Hisham ElMoaqet, Hamzeh Qaddoura, Mutaz Ryalat, Natheer Almtireen, Tamer Abdulbaki Alshirbaji, Nour Aldeen Jalal, Thomas Neumuth, Knut Moeller

“…This study proposes a novel deep learning approach for surgical tool classification based on combining convolutional neural networks (CNNs), Feature Fusion Modules (FFMs), Squeeze-and-Excitation (SE) networks, and Bidirectional long-short term memory (BiLSTM) networks to capture both spatial and temporal features in laparoscopic surgical videos. …”
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GAT-Enhanced YOLOv8_L with Dilated Encoder for Multi-Scale Space Object Detection by Haifeng Zhang, Han Ai, Donglin Xue, Zeyu He, Haoran Zhu, Delian Liu, Jianzhong Cao, Chao Mei

“…The problem of inadequate object detection accuracy in complex remote sensing scenarios has been identified as a primary concern. …”
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D’un problème d’action publique à la structuration d’un champ de recherche … et vice-versa : l’exemple de l’introduction de la question logistique dans l’aménagement urbain... by Jean Debrie

“…The interaction between knowledge and professional practices is a characteristic feature of academic approaches to urban planning and development. …”
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The Alliance between Mengu-Timur and Abaga: The Problem of Sources Interpretation and Translation of Persian and Turkic Terms in Arabic Texts (on the example of “Collection of Mate... by Ihsan Fazlioğlu

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Radar HRRP recognition based on supervised exponential sparsity preserving projection with small training data size by X. Yang, G. Zhang, H. Song

“…Second, matrix exponential is utilized to ensure the positive definiteness of the coefficient matrices, thereby addressing the small-sample-size (SSS) problem. Finally, an efficient numerical method is presented for solving the corresponding large-scale matrix exponential eigenvalue problem. …”
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Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network by Vijaya Gunturu, J. Kavitha, Swapna Thouti, N. K. Senthil Kumar, Kamal Poon, Ayman A. Alharbi, Amar Y. Jaffar, V. Saravanan

“…Abstract The correct sitting posture in a wheelchair is crucial for paralyzed people. This helps prevent problems such as pressure ulcers, muscle contractures, and respiratory problems. …”
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Survey of research on application of heuristic algorithm in machine learning by Yanping SHEN, Kangfeng ZHENG, Chunhua WU, Yixian YANG

“…Aiming at the problems existing in the application of machine learning algorithm,an optimization system of the machine learning model based on the heuristic algorithm was constructed.Firstly,the existing types of heuristic algorithms and the modeling process of heuristic algorithms were introduced.Then,the advantages of the heuristic algorithm were illustrated from its applications in machine learning,including the parameter and structure optimization of neural network and other machine learning algorithms,feature optimization,ensemble pruning,prototype optimization,weighted voting ensemble and kernel function learning.Finally,the heuristic algorithms and their development directions in the field of machine learning were given according to the actual needs.…”
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Four-path unsupervised learning-based image defogging network by Wei LIU, Cheng CHEN, Rui JIANG, Tao LU

“…To solve the problems of supervised network and unsupervised network in the field of single image defogging, a four-path unsupervised learning-based image defogging network based on cycle generative adversarial network (CycleGAN) was proposed, which mainly included three sub-networks: defogging network, synthetic fog network and attention feature fusion network.The three sub-networks were sequentially combined to construct four learning paths, which were the defogging path, the color-texture recovery path for defogged result, the synthetic fog path, and the color-texture recovery path for synthetic fog result.Specifically, in the synthetic fog network, to better constrain the defogging network to generate higher quality fogfree images, the atmospheric scattering model (ASM)was introduced to enhance the mapping transformation of the network from the foggy image domain to the fogfree image domain.Furthermore, to further improve the image generation quality of the defogging network and the synthetic fog network, an attention feature fusion network was proposed.The proposed network was based on several fog-derived images, which adopts a multi-channel mapping structure and an attention mechanism to enhance the recovery of color and texture details.Extensive experiments on both synthetic and real-world datasets show that the proposed method can better restore the color and texture details information of foggy images in various scenes.…”
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Mapping Grayscale Images to Colour Space Using Deep Learning by Anu Saini, Jyoti Tripathi

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Digital models of the consult-organization of management in the company by T. Rostovskaya, I. Groshev, Yu. Krasovskii

“…All four digital models are interrelated and help deeply understand the problems of the transition of the company from one state to another. …”
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Detection and identification of non-technical loss based on electricity consumption curve and deep learning by WANG Yunjing, XIAO Keyu, QU Zhengwei, HAN Xiaoming, DONG Haiyan, Popov Maxim Georgievitch

“…Finally, a multi-level neural network architecture is designed and deep learning is utilized to solve the multiclass classification problem of the feature sequences. Simulation based on actual power consumption dataset of a certain area shows that the research content can realize an effective detection of non-technical loss as well as identification of specific tampering strategies.…”
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YOLOv11-HRS: An Improved Model for Strawberry Ripeness Detection by Jianhua Liu, Jing Guo, Suxin Zhang

“…Furthermore, the RepNCSPELAN4_L module is devised to enhance multi-scale target representation through contextual feature aggregation. Simultaneously, a 160 × 160 small-target detection head is embedded in the feature pyramid to enhance the detection capability of small targets. …”
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A multi-scale multi-channel CNN introducing a channel-spatial attention mechanism hyperspectral remote sensing image classification method by Ru Zhao, Chaozhu Zhang, Dan Xue

“…Aiming the problems that the classification performance of hyperspectral images in existing classification algorithms is highly dependent on spatial-spectral information and that detailed features are ignored in single convolutional channel feature extraction, resulting in poor generalization performance of the feature extraction model, a multi-scale multi-channel convolutional neural network (MMC-CNN) model is proposed in this paper. …”
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