Facial Feature Recognition with Multi-task Learning and Attention-based Enhancements by M. Rohani, H. Farsi, S. Mohamadzadeh

“…This innovation is trained using the UTKFace dataset and is specifically optimized to improve accuracy in age estimation across different age groups. Second, we present an enhanced attention mechanism that guides the model to prioritize features that contribute to more robust FFR. …”
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Multilevel Feature Interaction Network for Remote Sensing Images Semantic Segmentation by Hongkun Chen, Huilan Luo

“…In addition, a lightweight global feature module is designed to adaptively extract global contextual information from different scales features, thereby enhancing the semantic accuracy of the features. …”
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Quantum annealing feature selection on light-weight medical image datasets by Merlin A. Nau, Luca A. Nutricati, Bruno Camino, Paul A. Warburton, Andreas K. Maier

“…Quantum computers, particularly quantum annealers, are well-suited for such problems, which may offer advantages under certain problem formulations. We present a method to solve larger feature selection instances than previously demonstrated on commercial quantum annealers. …”
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Detection of Road Rage in Vehicle Drivers Based on Speech Feature Fusion by Xiaofeng Feng, Chenhui Liu, Ying Chen

“…In order to detect emotional changes of drivers in a straightforward and accurate way, and enable measures to be taken to avoid accidents, this paper presents a road rage emotion detection method called DMG-FCC for drivers of motor vehicles based on the fusion of MFCC and GFCC speech features in the frequency domain and their first-order differential features (<inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>MFCC, <inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>GFCC). …”
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Hyperbolic geometry enhanced feature filtering network for industrial anomaly detection by Yanjun Feng, Jun Liu, Yonggang Gai

“…This paper presents HADNet, a hyperbolic space-based anomaly detection method. …”
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Efficient feature selection based on Gower distance for breast cancer diagnosis by Salwa Shakir Baawi, Mustafa Noaman Kadhim, Dhiah Al-Shammary

“…This study presents an efficient feature selection method based on the Gower distance to enhance the accuracy and efficiency of standard classifiers on high-dimensional medical datasets. …”
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Sleep stages classification based on feature extraction from music of brain by Hamidreza Jalali, Majid Pouladian, Ali Motie Nasrabadi, Azin Movahed

“…In this process, first, the tempo and scale parameters are extracted from the signal according to the rules of music, and next by applying them and changing the dominant frequency of the pre-processed single-channel EEG signal, a sequence of musical notes is produced. A total of 19 features are extracted from the sequence of notes and fed into feature reduction algorithms; the selected features are applied to a two-stage classification structure: 1) the classification of 5 classes (merging S1 and REM-S2-S3-S4-W) is made with an accuracy of 89.5 % (Cap sleep database), 85.9 % (Sleep-EDF database), 86.5 % (Sleep-EDF expanded database), and 2) the classification of 2 classes (S1 vs. …”
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Adaptive deep feature representation learning for cross-subject EEG decoding by Shuang Liang, Linzhe Li, Wei Zu, Wei Feng, Wenlong Hang

“…Methods: We propose a novel adaptive deep feature representation (ADFR) framework to improve the cross-subject EEG classification performance through learning transferable EEG feature representations. …”
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Weighted Content Similarity Feature for Software Architecture Anti-Patterns Prediction by Somayeh Kalhor, Mohammad Reza Keyvanpour

“…This study presents an improved component-level feature, called weighted content similarity, to more accurately detect component dependencies by minimizing the influence of common words that are often used in comments but are worthless in identifying the relationship between components. …”
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FEATURE-BASED IMPLEMENTATION OF MACHINE LEARNING ALGORITHMS FOR CARDIOVASCULAR DISEASE PREDICTION by H. Singh, R. Tripathy, P. Kumar Sarangi, U. Giri, S. Kumar Mohapatra, N. Rameshbhai Amin

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Multi-Scale Hierarchical Feature Fusion for Infrared Small-Target Detection by Yue Wang, Xinhong Wang, Shi Qiu, Xianghui Chen, Zhaoyan Liu, Chuncheng Zhou, Weiyuan Yao, Hongjia Cheng, Yu Zhang, Feihong Wang, Zhan Shu

“…Detecting small targets in infrared images presents significant challenges due to their tiny size and complex backgrounds, making this task a hotspot for research. …”
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An optimized ensemble model with advanced feature selection for network intrusion detection by Afaq Ahmed, Muhammad Asim, Irshad Ullah, Zainulabidin, Abdelhamied A. Ateya

“…To address this challenge, our study presents the “Optimized Random Forest (Opt-Forest),” an innovative ensemble model that combines decision forest approaches with genetic algorithms (GAs) for enhanced intrusion detection. …”
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Recognizing and localizing chicken behaviors in videos based on spatiotemporal feature learning by Yilei Hu, Jinyang Xu, Zhichao Gou, Di Cui

“…This limitation highlights the insufficient temporal resolution of video-based behavior recognition models. This study presents a chicken behavior recognition and localization model, CBLFormer, which is based on spatiotemporal feature learning. …”
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Multiscale Feature Reconstruction and Interclass Attention Weighting for Land Cover Classification by Zongqian Zhan, Zirou Xiong, Xin Huang, Chun Yang, Yi Liu, Xin Wang

“…However, high-resolution remote sensing images typically have abundant textual details, variable scales in objects, large intraclass variance, and similar interclass correlation, which bring challenges to land cover classification. In this work, we present two pluggable modules to further boost serial learning architecture: first, to cope with ambiguous boundaries caused by lost details and fragmented segmentation stemmed from scale variances, a combination of spatial attention and channel attention is proposed for multiscale feature reconstruction (MSFR); second, to mitigate the classification error caused by intraclass variance and interclass correlation, we explore an interclass attention weighting (ICAW) module, which builds feature vectors for each category, and applies a multihead attention model to capture self-attention dependence among different categories. …”
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Decreased Expression of Aquaporins as a Feature of Tubular Damage in Lupus Nephritis by Maxime Melchior, Marie Van Eycken, Charles Nicaise, Thomas Duquesne, Léa Longueville, Amandine Collin, Christine Decaestecker, Isabelle Salmon, Christine Delporte, Muhammad Soyfoo

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Wireless Authentication Method Based on Near-Field Feature Fusion Network by QIU Jiefan, ZHOU Kezhong, ZHU Dongfu, ZHANG Jinhong, CHI Kaikai

“…The experimental results validated the superiority of the proposed method, which combined both dynamic and LOS features. Additionally, the inference time was 3.26 milliseconds, demonstrating the efficiency of the approach.ConclusionsThis paper presents a WiFi-based gait authentication method that integrates near-field LOS features for identity recognition. …”
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THE FEATURES OF LASER EMISSION ENERGY DISTRIBUTION AT MATHEMATIC MODELING OF WORKING PROCESS by A. M. Avsiyevich, O. G. Devoino, A. D. Kocherov, A. P. Pilipchuk

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Impact of face outline, parafoveal feature number and feature type on early face perception in a gaze-contingent paradigm: A mass-univariate re-analysis of ERP data by Seth B. Winward, James Siklos-Whillans, Roxane J. Itier

“…However, we found that this effect began around 114 ms, and was maximal during the P1-N170 and N170-P2 intervals. The number of features present in parafovea also impacted the entire waveform, with systematic reductions in amplitude and latency as the number of features increased. …”
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Allgrove syndrome in endocrinology: Difficulties of diagnosis and treatment features. Case report by Uliana V. Buyvalenko, Anna R. Levshina, Anna K. Eremkina, Kseniya A. Komshilova, Nadezhda M. Platonova

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CHARACTERISTICS OF CLINICAL PICTURE AND BIOLOGIC FEATURES OF RARE BREAST CANCER FORMS by G. V. Martinova, I. V. Visotskaya, V. P. Letyagin, V. D. Yermilova, Ye. A. Kim

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