Named Entity Recognition Method Based on Multi-Feature Fusion by Weidong Huang, Xinhang Yu

“…The model also leverages multi-head attention for feature fusion, and the final results are decoded using a Conditional Random Field (CRF) layer. …”
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Detecting emotional disorder with eye movement features in sports watching by Wei Qiang, Wei Qiang, Lin Yang, Xucheng Zhang, Na Liu, Yanyong Wang, Jipeng Zhang, Yixin Long, Weiwei Xu, Wei Sun

“…A decision tree model trained on all significant features achieved 0.92 accuracy, 0.80 precision, and an AUC of 0.94. …”
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Unsupervised feature selection and class labeling for credit card fraud by Robert K. L. Kennedy, Flavio Villanustre, Taghi M. Khoshgoftaar

“…In the context of fraud detection, such as credit card fraud, these challenges are compounded by privacy concerns and high class imbalances, which severely degrades classification performance of machine learning models. In this paper, we present a fully unsupervised approach that combines SHapley Additive exPlanations (SHAP) for feature selection with an autoencoder based method for generating class labels for a widely used credit card fraud detection dataset. …”
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Exploring Discursive Features and Techniques of Manipulating Consumers in Printed Advertisements by Khadijeh Mohammadi, Hiwa Weisi, Shahab Moradkhani

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The influence of correlated features on neural network attribution methods in geoscience by Evan Krell, Antonios Mamalakis, Scott A. King, Philippe Tissot, Imme Ebert-Uphoff

“…EXplainable Artificial Intelligence (XAI) techniques expose how models make predictions, but XAI results may be influenced by correlated features. …”
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Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Met... by Amir Moslemi, Laurentius Oscar Osapoetra, Archya Dasgupta, Schontal Halstead, David Alberico, Maureen Trudeau, Sonal Gandhi, Andrea Eisen, Frances Wright, Nicole Look-Hong, Belinda Curpen, Michael Kolios, Gregory J. Czarnota

“…For training predictive models, three models including all features (wavelet and original image features), only wavelet and only original-image features were considered. …”
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Educational Data Mining by Optimally Fusing Shallow and Deep Features by Bo-An Xiao

“…Then, the SVD algorithm program is used to reduce the range of feature vectors. And thus, the model is completely functional and we identify unified channels and users. …”
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YLOMF: You Look Once for Multi-Feature—A Multi-Feature Multi-Task Network for Intelligent Real-Time Perception in Autonomous Driving by B. N. Anoop, S. K. Abhilash, Venu Madhav Nookala, S. Prita, S. Raghavendra

“…This study presents You Look Once for Multi-Feature(YLOMF), a novel single-stage, vision-based hierarchical model for autonomous driving. …”
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Assessing water quality environmental grades using hyperspectral images and a deep learning model: A case study in Jiangsu, China by Hongran Li, Hui Zhao, Chao Wei, Min Cao, Jian Zhang, Heng Zhang, Dongqing Yuan

“…The model is specifically designed to integrate both channel and spatial information, enabling precise water quality grade assessment by detecting subtle features within HSIs data. …”
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Specific features of cognitive skill development in athletes of situational sports by Vyacheslav Romanenko, Yrui Tropin, Leonid Podrigalo, Natalya Boychenko, Anatoly Abdula, Nataliia Sereda, Yaroslav Yatsiv

“…Wilks’ Lambda was used to evaluate the quality of the discriminant model. Analysis of variance (ANOVA) was performed to identify variables with significant intergroup differences. …”
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Unsupervised feature selection in binarization of real attributes for conceptual clustering by Shkaberina Guzel, Masich Igor, Markushin Egor, Kraeva Ekaterina

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Relative importance of temporal and location features in predicting smoking events by Han Yang, Hang Yu, Michael Kotlyar, Sheena R. Dufresne, Serguei V. S. Pakhomov

“…Across all modeling approaches and settings, excluding temporal features led to a substantial decrease in performance, while removing spatial features had a minimal effect. …”
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A Multi-Scale Feature Attention Image Recognition Algorithm by Xin MingYuan, Ang Ling Weay, Sellappan Palaniappan

“…Due to its ability to efficiently suppress irrelevant characteristics and accentuate pertinent ones, this technique may extract more robust multiscale features and enhance classification performance through meta-learning.In this paper, the effectiveness of the multi-scale attention network is verified on two datasets, namely, Mini-ImageNet and Tiered-ImageNet, and the accuracy of the method is 58.54% for 5-way 1shot and 74.76% for 5-way 5shot on the Mini-ImageNet dataset. …”
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Spectrogram Features-Based Automatic Speaker Identification For Smart Services by Rashid Jahangir, Mohammed Alreshoodi, Fawaz Khaled Alarfaj

“…The extracted spectrogram-based features and models are evaluated on the ELSDSR, TSP, and LibriSpeech datasets and achieved the weighted accuracy of 96.0%, 99.2%, and 97.6%, respectively. 3. …”
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Integrated Feature-Temporal GAN for Imbalanced Transaction Fraud Detection by Yicen Zheng, Yu Xie, Jiamin Yao

“…In this work, we introduce the Integrated Feature-Temporal-aware Generative Adversarial Network (IFT-GAN), a novel framework that simultaneously refines feature space and models temporal dynamics during the generation of synthetic samples. …”
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Classification of Lung Nodule Using Hybridized Deep Feature Technique by Malin Bruntha, Immanuel Alex Pandian, Siril Sam Abraham

“…The dimensionality reduction technique, Principle Component Analysis (PCA) has been introduced to improve the performance of hybridized features with SVM. The experiments have been carried out with 14 different methods. …”
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Approximate Calculation and Feature Analysis of Electric Field in Space by Thunderclouds by Lin Wang, Haojiang Wan, Yazhou Chen

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Snow Depth Retrieval Using Sentinel-1 Radar Data: A Comparative Analysis of Random Forest and Support Vector Machine Models with Simulated Annealing Optimization by Yurong Cui, Sixuan Chen, Guiquan Mo, Dabin Ji, Lansong Lv, Juan Fu

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High-speed encrypted traffic classification by using payload features by Xinge Yan, Liukun He, Yifan Xu, Jiuxin Cao, Liangmin Wang, Guyang Xie

“…Second, HETC introduces binary features upon the previous features and performs fine-grained traffic classification by combining these payload features with a Random Forest model. …”
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Diagnosis of Alzheimer’s disease using brain $$^{18}\textrm{F}$$ -FDG PET imaging based on a state space model by Yufang Dong, Yonglin Chen, Zhe Jin, Xingbo Dong

“…This allows the model to maintain high performance while achieving more lightweight and efficient operations. …”
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