Graph Convolutional Enhanced Discriminative Broad Learning System for Hyperspectral Image Classification by Tuya

“…In addition, the features learned by broad learning system lack more effective discriminative ability, which leads to the limited expressive ability of features. …”
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Deep knowledge tracing with learning curves by Hang Su, Xin Liu, Shanghui Yang, Xuesong Lu

“…Moreover, the two models employ LSTM networks to learn the overall knowledge state, which is fused with the feature learned by the convolutional/capsule module. As such, the two models can learn the student's overall knowledge state as well as the knowledge state of the concept in the next question. …”
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Fusion Methods for Face Presentation Attack Detection by Faseela Abdullakutty, Pamela Johnston, Eyad Elyan

“…However, the question remains as to whether features learned in existing, deep neural networks sufficiently encompass traditional, low-level features in order to achieve optimal performance on PA detection tasks. …”
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Investigating vulnerability of watermarking neural network by Chua, Viroy Sheng Yang

“…Method 2 involves using the basic features learn by the early convolutional layers of the watermarked model to train a model with comparable performance. …”
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A hybrid machine learning technique for complex non-stationary classification problems by Vijaya Krishna Yalavarthi

“…This results in an impending need to develop new machine learning methods to address sequential learning for non-stationary data samples featuring learning parameters. In this project, a novel technique that is independent of the number of class constraints and can adapt to the introduction of new classes it will encounter is developed. …”
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Compression of a Deep Competitive Network Based on Mutual Information for Underwater Acoustic Targets Recognition by Sheng Shen, Honghui Yang, Meiping Sheng

“…However, redundant features learned by deep neural network have negative effects on recognition accuracy and efficiency. …”
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Lightweight End-to-End Neural Network Model for Automatic Heart Sound Classification by Tao Li, Yibo Yin, Kainan Ma, Sitao Zhang, Ming Liu

“…These features were sent to the improved two-dimensional convolutional neural network (CNN) model for features learning and classification. Considering the imbalance of positive and negative samples, we introduced FocalLoss as the loss function, verified our network model with multiple random verifications, and, hence, obtained a better classification result. …”
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Investigation of semi- and self-supervised learning methods in the histopathological domain by Benjamin Voigt, Oliver Fischer, Bruno Schilling, Christian Krumnow, Christian Herta

“…Moreover, our observations suggest that features learned from a particular dataset, i.e., tissue type, are only in-domain transferable to a certain extent. …”
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Feature Extraction for Finger-Vein-Based Identity Recognition by George K. Sidiropoulos, Polixeni Kiratsa, Petros Chatzipetrou, George A. Papakostas

“…In addition, the case of non-handcrafted features learned in a deep learning framework is also examined. …”
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A DEEP AUTOENCODER-BASED REPRESENTATION FOR ARABIC TEXT CATEGORIZATION by Fatima-zahra El-Alami, Abdelkader El Mahdaouy, Said Ouatik El Alaoui, Noureddine En-Nahnahi

“…It consisted of three stages: (1) Extracting from Arabic WordNet the most relevant concepts based on feature selection processes (2) Features learning via an unsupervised algorithm for text representation (3) Categorizing text using deep Autoencoder. …”
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Critical Success Factors for Virtual Reality Applications in Orthopaedic Surgical Training: A Systematic Literature Review by Mohd Yazid Bajuri, Youcef Benferdia, Mohammad Nazir Ahmad

“…The CSFs were divided into six general categories: HCI/VR Features, Learning Outcome, Usability, Control and Active Learning, Student and Limitation factors. …”
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A Robust Deep Learning Ensemble-Driven Model for Defect and Non-Defect Recognition and Classification Using a Weighted Averaging Sequence-Based Meta-Learning Ensembler by Okeke Stephen, Samaneh Madanian, Minh Nguyen

“…In the proposed method, a unique base model is constructed and fused together with other co-learning pretrained models using a sequence-driven meta-learning ensembler that aggregates the best features learned from the various contributing models for better and superior performance. …”
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A deep autoencoder-based representation for Arabic text categorization by El-Alami, Fatima-Zahra, El Mahdaouy, Abdelkader, El Alaoui, Said Ouatik, En-Nahnahi, Noureddine

“…It consisted of three stages: (1) Extracting from Arabic WordNet the most relevant concepts based on feature selection processes (2) Features learning via an unsupervised algorithm for text representation (3) Categorizing text using deep Autoencoder. …”
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Speech emotion recognition via graph-based representations by Anastasia Pentari, George Kafentzis, Manolis Tsiknakis

“…As a consequence, a variety of engineering approaches have been developed addressing the challenge of the SER problem, exploiting different features, learning algorithms, and datasets. In this paper, we propose the application of the graph theory for classifying emotionally-colored speech signals. …”
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Breast Tumor Tissue Image Classification Using Single-Task Meta Learning with Auxiliary Network by Jiann-Shu Lee, Wen-Kai Wu

“…Furthermore, the Silhouette Score corresponding to the features learned by the model has increased by 31.85%, reflecting that the proposed model can learn more discriminative features, and the generalization ability of the overall model is also improved.…”
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Image-Based Vehicle Classification by Synergizing Features from Supervised and Self-Supervised Learning Paradigms by Shihan Ma, Jidong J. Yang

“…This paper introduces a novel approach to leveraging features learned from both supervised and self-supervised paradigms, to improve image classification tasks, specifically for vehicle classification. …”
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Face with Mask Detection in Thermal Images Using Deep Neural Networks by Natalia Głowacka, Jacek Rumiński

“…It was shown that the use of transfer learning based on features learned from visible light images results in mAP greater than 82% for half of the investigated models. …”
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Prediction of Synaptically Localized RNAs in Human Neurons Using Developmental Brain Gene Expression Data by Anqi Wei, Liangjiang Wang

“…We examined the relevant expression features learned by PredSynRNA and used an independent test dataset to further validate the model performance. …”
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Explainable ensemble learning method for OCT detection with transfer learning. by Jiasheng Yang, Guanfang Wang, Xu Xiao, Meihua Bao, Geng Tian

“…The impact of pre-trained weights on the performance of individual networks was first compared, and then these networks were ensemble using majority soft polling. Finally, the features learned by the networks were visualized using Grad-CAM and CAM. …”
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Scene context-aware salient object detection by Siris, A, Jiao, J, Tam, GKL, Xie, X, Lau, RWH

“…Specifically, two modules are proposed to achieve the goal: 1) a Semantic Scene Context Refinement module to enhance contextual features learned from salient objects with scene context, and 2) a Contextual Instance Transformer to learn contextual relations between objects and scene context. …”