Extra-Nodal Follicular Dendritic Cell Sarcoma with Unusual Morphological Features and PDL1 Expression by Manali Ranade, Aekta Shah, Subhash Yadav, Santosh Menon, Sudeep Gupta

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LRU-Net: lightweight and multiscale feature extraction for localization of ACL tears region in MRI images by Xiaojun Si, Liang Yan, Cui Shi, Yang Xu

“…Furthermore, it employs a dynamic feature extraction module for adaptive multiscale feature extraction. …”
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Fusion ConvLSTM-Net: Using Spatiotemporal Features to Increase Residential Load Forecast Horizon by Abhishu Oza, Dhaval K. Patel, Bryan J. Ranger

“…In this paper, we propose Fusion ConvLSTM-Net, a novel fusion encoder-decoder architecture that combines both spatial and temporal features to extend the load forecast to a full 24 hour period. …”
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Features of reproduction of the shrub vole <i>Microtus majori</i> Thomas, 1906 in natural conditions by R. I. Dzuev, A. A. Cheprakova, F. H. Zhilyaeva, K. A. Mashukova

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Improving Age Estimation in Occluded Facial Images with Knowledge Distillation and Layer-Wise Feature Reconstruction by Shuangfei Yu, Qilu Zhao

“…Although prior research has explored de-occlusion methods for occluded facial images, there remains a lack of studies focusing on the implicit facial feature information present in fixed occlusion patterns. …”
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Features of Imaging of Different Types of Hodgkin’s Lymphoma Using Combined Positron Emission and Computed Tomography by AS Subbotin, NG Afanas’eva

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MemoCMT: multimodal emotion recognition using cross-modal transformer-based feature fusion by Mustaqeem Khan, Phuong-Nam Tran, Nhat Truong Pham, Abdulmotaleb El Saddik, Alice Othmani

“…This CMT can effectively analyze local and global speech features and their corresponding text. To boost efficiency, MemoCMT leverages recent advancements in pre-trained models: HuBERT extracts meaningful features from the audio, while BERT analyzes the text. …”
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Detection of Student Engagement via Transformer-Enhanced Feature Pyramid Networks on Channel-Spatial Attention by A. Naveen, I. Jeena Jacob, Ajay Kumar Mandava

“…This study proposes a novel real-time detection framework that leverages Transformer-enhanced Feature Pyramid Networks (FPN) with Channel-Spatial Attention (CSA), referred to as BiusFPN_CSA. …”
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Deep learning based local feature classification to automatically identify single molecule fluorescence events by Shuqi Zhou, Yu Miao, Haoren Qiu, Yuan Yao, Wenjuan Wang, Chunlai Chen

“…In this study, we introduce DEBRIS (Deep lEarning Based fRagmentatIon approach for Single-molecule fluorescence event identification), a deep-learning model focusing on classifying local features and capable of automatically identifying steady fluorescence signals and dynamically emerging signals of different patterns. …”
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Multi-Step Natural Gas Load Forecasting Incorporating Data Complexity Analysis with Finite Features by Ning Tian, Bilin Shao, Huibin Zeng, Meng Ren, Wei Zhao, Xue Zhao, Shuqiang Wu

“…This synergy enables effective learning of local features and long-term temporal patterns, resulting in precise predictions. …”
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Early Detection of ITSC Faults in PMSMs Using Transformer Model and Transient Time-Frequency Features by Ádám Zsuga, Adrienn Dineva

“…The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. …”
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Features of semiotic encoding of a precedent name or “an ordinary Soviet man” in a polycode text by T. V. Marchenko

“…The article studies features of encoding precedent names (PNs) and precedent images in modern polycode texts with account of their semiotically complicated nature and the characteristics of visual / verbal / auditory forms of precedence involved in the process. …”
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Temporal Variations of the Oldest Emperor‐Hawaiian Plume Signature Influenced by Interaction With Shallow Mantle Features by P. D. Kempton, C. Adam, A. D. Saunders, T. L. Barry

“…This suggests that the ancestral Emperor‐Hawaiian plume was either (a) not initially associated with the Pacific LLSVP, (b) was deflected northward by shallow mantle features such that plume‐ridge interactions dominated in the upper mantle or convective flow patterns modified the plume structure in the mid mantle, or (c) the edge of the Pacific LLSVP receded southward by more than 15° over the past ∼100 m.y.…”
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Integration of Deep Learning Neural Networks and Feature-Extracted Approach for Estimating Future Regional Precipitation by Shiu-Shin Lin, Kai-Yang Zhu, He-Yang Huang

“…DNN is used to learn the nonlinear and complex relationships among the features extracted by KPCA to predict future regional rainfall patterns and trends in complex island terrain in Taiwan. …”
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Enhancing parkinson disease detection through feature based deep learning with autoencoders and neural networks by P. Valarmathi, Y. Suganya, K. R. Saranya, S. Shanmuga Priya

“…Autoencoder, a specific form of Artificial Neural Network (ANN) that is designed to excel in the task of feature extraction, is utilized in our study to effectively capture complex patterns present in audio data. …”
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An interpretable XAI deep EEG model for schizophrenia diagnosis using feature selection and attention mechanisms by Ahmad Almadhor, Stephen Ojo, Thomas I. Nathaniel, Shtwai Alsubai, Abdullah Alharthi, Abdullah Al Hejaili, Gabriel Avelino Sampedro

“…In addition to fine-tuning input dimensionality, F-test feature selection increases learning efficiency.ResultsThrough the integration of feature importance analysis and conventional performance measures, this study presents valuable insights into the discriminative neurophysiological patterns associated with Schizophrenia, advancing both diagnostic and neuroscientific expertise. …”
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Data Reconstruction Methods in Multi-Feature Fusion CNN Model for Enhanced Human Activity Recognition by Jae Eun Ko, SeungHui Kim, Jae Ho Sul, Sung Min Kim

“…We tested across various levels of noise, and the proposed model consistently demonstrated greater robustness than the time-series-based approach. Fusing features from three inputs effectively captured latent patterns and variations in accelerometer data. …”
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A Bridge Crack Segmentation Algorithm Based on Fuzzy C-Means Clustering and Feature Fusion by Yadong Yao, Yurui Zhang, Zai Liu, Heming Yuan

“…In response to the limitations of traditional image processing algorithms, such as high noise sensitivity and threshold dependency in bridge crack detection, and the extensive labeled data requirements of deep learning methods, this study proposes a novel crack segmentation algorithm based on fuzzy C-means (FCM) clustering and multi-feature fusion. A three-dimensional feature space is constructed using B-channel pixels and fuzzy clustering with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>c</mi></semantics></math></inline-formula> = 3, justified by the distinct distribution patterns of these three regions in the image, enabling effective preliminary segmentation. …”
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Enhancing agricultural data interpretability and visualization with TabNet-driven feature extraction and Local Biplots by J. Triana-Martinez, A. Álvarez-Meza, G. Castellanos-Dominguez

“…The method provides an intuitive representation of non-stationary and non-linear data patterns, enhancing both global and cluster-level explainability. …”
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Enhancing feature learning of hyperspectral imaging using shallow autoencoder by adding parallel paths encoding by Bibi Noor Asmat, Hafiz Syed Muhammad Bilal, M. Irfan Uddin, Faten Khalid Karim, Samih M. Mostafa, José Varela-Aldás

“…While PCA and ICA, being linear methods, may overlook complex patterns, Autoencoders (AE) can capture and represent non-linear features. …”
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