Unsupervised Feature Representation Based on Deep Boltzmann Machine for Seizure Detection by Tengzi Liu, Muhammad Zohaib Hassan Shah, Xucun Yan, Dongping Yang

“…A novel unsupervised learning approach based on DBM, namely DBM_transient, is proposed by training DBM to a transient state for representing EEG signals in a 2D feature space and clustering seizure and non-seizure events visually. …”
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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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A Meta-Learning-Based Recognition Method for Multidimensional Feature Extraction and Fusion of Underwater Targets by Xiaochun Liu, Yunchuan Yang, Youfeng Hu, Xiangfeng Yang, Liwen Liu, Lei Shi, Jianguo Liu

“…A multidimensional feature information fusion network model based on meta-learning is developed. …”
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MW‐SAM:Mangrove wetland remote sensing image segmentation network based on segment anything model by Yu Zhang, Xin Wang, Jingye Cai, Qun Yang

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Optimisation-Based Feature Selection for Regression Neural Networks Towards Explainability by Georgios I. Liapis, Sophia Tsoka, Lazaros G. Papageorgiou

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

“…Second, we present an enhanced attention mechanism that guides the model to prioritize features that contribute to more robust FFR. This mechanism is trained on the diverse and challenging images of UTKFace and is capable of identifying subtle and discriminative features in faces for more accurate gender, race, and age recognition. …”
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Spoofing speech detection algorithm based on joint feature and random forest by Jiaqi YU, Zhihua JIAN, Jia XU, Lin YOU, Yunlu WANG, Chao WU

“…In order to describe the characteristic information of the speech signal more comprehensively and improve the detection rate of camouflage, a spoofing speech detection method based on the combination of uniform local binary pattern texture feature and constant Q cepstrum coefficient acoustic feature was proposed, which used random forest as the classifier model.The texture feature vector in the speech signal spectrogram was extracted by using the uniform local binary mode, and the joint feature was formed with the constant Q cepstrum coefficient.Then, the obtained joint feature vector was used to train the random forest classifier, so as to realize the camouflage speech detection.In the experiment, the performances of several spoofing detection systems constructed by other feature parameters and the support vector machine classifier model were compared, and the results show that the proposed speech spoofing detection system combined with the joint feature and the random forest model has the best performance.…”
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Spoofing speech detection algorithm based on joint feature and random forest by Jiaqi YU, Zhihua JIAN, Jia XU, Lin YOU, Yunlu WANG, Chao WU

“…In order to describe the characteristic information of the speech signal more comprehensively and improve the detection rate of camouflage, a spoofing speech detection method based on the combination of uniform local binary pattern texture feature and constant Q cepstrum coefficient acoustic feature was proposed, which used random forest as the classifier model.The texture feature vector in the speech signal spectrogram was extracted by using the uniform local binary mode, and the joint feature was formed with the constant Q cepstrum coefficient.Then, the obtained joint feature vector was used to train the random forest classifier, so as to realize the camouflage speech detection.In the experiment, the performances of several spoofing detection systems constructed by other feature parameters and the support vector machine classifier model were compared, and the results show that the proposed speech spoofing detection system combined with the joint feature and the random forest model has the best performance.…”
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Vehicle trajectory prediction based on spatio-temporal Transformer feature fusion by ZHAO Wenhong, WANG Wei, WAN Zilu

“…The proposed model was trained and validated on the publicly available NGSIM dataset. …”
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Adversarial detection based on feature invariant in license plate recognition systems by ZHU Xiaoyu, TANG Peng, ZHANG Haochen, QIU Weidong, HUANG Zheng

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Detection of Road Rage in Vehicle Drivers Based on Speech Feature Fusion by Xiaofeng Feng, Chenhui Liu, Ying Chen

“…These features are first analysed, and each feature selected for fusion is determined preliminarily based on experience. …”
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A Multi-Time-Frequency Feature Fusion Approach for Marine Mammal Sound Recognition by Xiangxu Meng, Xin Liu, Yinan Xu, Yujing Wu, Hang Li, Kye-Won Kim, Suya Liu, Yihu Xu

“…This paper proposes an Evaluation-Adaptive Weighted Multi-Head Fusion Network that integrates CQT and STFT features via a dual-branch ResNet architecture. The model enhances intra-branch features using channel attention and adaptive weighting of each branch based on its validation accuracy during training. …”
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Robust Adversarial Example Detection Algorithm Based on High-Level Feature Differences by Hua Mu, Chenggang Li, Anjie Peng, Yangyang Wang, Zhenyu Liang

“…To address these challenges, this paper introduces a novel adversarial example detection algorithm based on high-level feature differences (HFDs), which is specifically designed to improve robustness against both attacks and preprocessing operations. …”
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Memory-Efficient Batching for Time Series Transformer Training: A Systematic Evaluation by Phanwadee Sinthong, Nam Nguyen, Vijay Ekambaram, Arindam Jati, Jayant Kalagnanam, Peeravit Koad

“…We evaluate our proposed batching framework systematically using peak GPU memory consumption and epoch runtime as efficiency metrics across varying batch sizes, sequence lengths, feature dimensions, and model architectures. Results show consistent memory savings, averaging 90% and runtime improvements of up to 33% across multiple transformer-based models (Informer, Autoformer, Transformer, and PatchTST) and a linear baseline (DLinear) without compromising model accuracy. …”
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Distributed Typhoon Track Prediction Based on Complex Features and Multitask Learning by Yongjiao Sun, Yaning Song, Baiyou Qiao, Boyang Li

“…To this end, we presented a novel typhoon track prediction framework comprising complex historical features—climatic, geographical, and physical features—as well as a deep-learning network based on multitask learning. …”
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Learning Part-Based Features for Vehicle Re-Identification with Global Context by Rajsekhar Kumar Nath, Debjani Mitra

“…We propose a novel part-based model that unifies a global component by taking the distances of the parts from the global feature vector and using them as loss weights during the training of the individual parts, without increasing complexity. …”
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Automatic crop type mapping based on crop-wise indicative features by Junru Yu, Longcai Zhao, Yanfu Liu, Qingrui Chang, Na Wang

“…The time series analysis method (i.e., seasonal trend decomposition) and imputation method was then utilized for the discrete time series of CIF extractors, which were separately trained based on training samples on each day. This process yielded the DCIF extractor that can capture the unique feature pattern on any given day during the entire growing period. …”
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A comprehensive construction of deep neural network‐based encoder–decoder framework for automatic image captioning systems by Md Mijanur Rahman, Ashik Uzzaman, Sadia Islam Sami, Fatema Khatun, Md Al‐Amin Bhuiyan

“…The long short‐term memory network functions as a sequence processor, generating a fixed‐length output vector for final predictions, while the VGG‐19 model is utilized as an image feature extractor. For both training and testing, the study uses a variety of photos from open‐access datasets, such as Flickr8k, Flickr30k, and MS COCO. …”
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Physically Based Dimensionless Features for Pluvial Flood Mapping With Machine Learning by Mark S. Bartlett, Jared VanBlitterswyk, Martha Farella, Jinshu Li, Curtis Smith, Anthony J. Parolari, Lalitha Krishnamoorthy, Assaad Mrad

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A robust covariate‐invariant gait recognition based on pose features by Anubha Parashar, Apoorva Parashar, Rajveer Singh Shekhawat

“…This study proposes an approach based on pose features to attempt gait recognition of people with an overcoat, carrying objects, or other covariates. …”
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