DFANet: A Deep Feature Attention Network for Building Change Detection in Remote Sensing Imagery by Peigeng Lu, Haiyong Ding, Xiang Tian

“…Finally, Transformer is introduced to capture long-range dependencies across bitemporal images, enabling the network to better understand feature change patterns and the relationships between different regions and land cover categories. …”
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The pattern of state organization in the new constitution of Lithuania: A third way between the presidential and parliamentary systems? by Andreas Hollstein

“…In the theoretical section, he writes that the classification of the patterns of state organization is relatively simple in the cases when the characteristics of the classified system correspond to the essential features of either British or American systems. …”
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Time-Series Representation Feature Refinement with a Learnable Masking Augmentation Framework in Contrastive Learning by Junyeop Lee, Insung Ham, Yongmin Kim, Hanseok Ko

“…Time-series data pose challenges due to their temporal dependencies and feature-extraction complexities. To address these challenges, we introduce a masking-based reconstruction approach within a contrastive learning context, aiming to enhance the model’s ability to learn discriminative temporal features. …”
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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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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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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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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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Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis by Lanting Guo, Xiaoyu Hu, Wenhe Liu, Yang Liu

“…Using this graph as the prior knowledge, our system synthesizes discriminative visual features for unseen hazard classes through a multi-source graph fusion module and region feature diffusion model. …”
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An IoT intrusion detection framework based on feature selection and large language models fine-tuning by Huan Ma, Wan Zhang, Dalong Zhang, Baozhan Chen

“…But existing methods face two significant challenges: (1) Feature redundancy: Current approaches extract numerous flow-level features to learn attack behavior, resulting in high computational complexity and substantial redundant information. (2) Class imbalance: Limited attack traffic samples hinder models from effectively learning attack patterns. …”
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A Dual-Feature Framework for Enhanced Diagnosis of Myeloproliferative Neoplasm Subtypes Using Artificial Intelligence by Amna Bamaqa, N. S. Labeeb, Eman M. El-Gendy, Hani M. Ibrahim, Mohamed Farsi, Hossam Magdy Balaha, Mahmoud Badawy, Mostafa A. Elhosseini

“…In contrast, automatic features utilize deep learning models to identify complex patterns in histopathological images. …”
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Intelligent islanding detection framework for smart grids using wavelet scalograms and HOG feature fusion by Kumaresh Pal, Kumari Namrata, Ashok Kumar Akella, Akshit Samadhiya, Ahmad Taher Azar, Mohamed Tounsi, Naglaa F. Soliman, Walid El-Shafai

“…The HOG descriptors effectively capture the intricate patterns and subtle signal changes associated with islanding conditions. …”
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EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection by Alanoud Al Mazroa, Majdy M. Eltahir, Shouki A. Ebad, Faiz Abdullah Alotaibi, Venkatachalam K, Jaehyuk Cho

“…This is because schizophrenia involves intricate and subtle brain wave patterns that make it difficult to detect the disorder from EEG signals. …”
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