Innovative assesment strategies: image based key feature questions for radiology postgraduate trainees by Nasreen Naz, K. Hussain, V. Bari, Nida Rafiq, A. Afzal

“…The purpose of this study is to determine the effectiveness of image-based key feature questions (IBKFQs) compared with traditional multiple-choice questions (MCQs) in radiology examinations. …”
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Integrated CNN‐LSTM for Photovoltaic Power Prediction based on Spatio‐Temporal Feature Fusion by Junwei Ma, Meiru Huo, Jinfeng Han, Yunfeng Liu, Shunfa Lu, Xiaokun Yu

“…The features are then fused according to the strength of the correlation, which allows the features to be combined with spatial and temporal attributes, which promotes faster and more effective training of the model. …”
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A novel feature extractor based on constrained cross network for detecting sleep state by Chenlei Tian, Fei Song

“…This study explores an improved feature extractor based on the Constrained Cross Network to enhance the accuracy of the sleep-wake binary classification problem. …”
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Prediction method of gas emission in working face based on feature selection and BO-GBDT by MA Wenwei

“…The wrapping method was identified as the most effective feature selection algorithm. Based on field conditions, 8 optimal features were selected for prediction. …”
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Feature Feedback-Based Pseudo-Label Learning for Multi-Standards in Clinical Acne Grading by Yung-Yao Chen, Hung-Tse Chan, Hsiao-Chi Wang, Chii-Shyan Wang, Hsuan-Hsiang Chen, Po-Hua Chen, Yi-Ju Chen, Shao-Hsuan Hsu, Chih-Hsien Hsia

“…This study proposes the Feature Feedback-Based Pseudo-Label Learning (FF-PLL) framework to address these limitations through three innovations: (1) an acne feature feedback (AFF) architecture with iterative pseudo-label refinement to improve the training robustness, enhance the pseudo-label quality, and increase the feature diversity; (2) all-facial skin segmentation (AFSS) to reduce background noise, enabling precise lesion feature extraction; and (3) the AcneAugment (AA) strategy to foster model generalization by introducing diverse acne lesion representations. …”
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Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition by Pawel Powroznik, Maria Skublewska-Paszkowska, Krzysztof Dziedzic, Marcin Barszcz

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Using Image Feature Extraction to Identification of Ancient Ceramics Based on Partial Differential Equation by Chuanbao Niu, Mingzhu Zhang

“…Recognition of ancient ceramic image features was realized based on the extraction of the overall image features of ancient ceramics, the extraction and recognition of vessel type features, the quantitative recognition of multidimensional feature fusion ornamentation image features, and the implementation of deep learning based on inscription model recognition image feature classification recognition method; three-layer B/S architecture web application system and cross-platform system language called as the architectural support; and database services, deep learning packaging, and digital image processing. …”
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Rapid estimation method of lithium battery state of health based on novel health feature by DONG Xiaohong, DONG Jinbo, WANG Mingshen, ZENG Fei, PAN Yi

“…Therefore, a rapid estimation method of lithium battery SOH based on novel health feature is proposed in this paper. …”
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A Super-Resolution-Based Feature Map Compression for Machine-Oriented Video Coding by Jung-Heum Kang, Muhammad Salman Ali, Hye-Won Jeong, Chang-Kyun Choi, Younhee Kim, Se Yoon Jeong, Sung-Ho Bae, Hui Yong Kim

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Ultrasound feature-based nomogram model for predicting extrathyroidal extension in papillary thyroid carcinoma by Dong Guo, Chen Chen, Yin Zheng, Yue Shan, Shifei Huang, Tianhan Zhou, Yefei Yao, Zhengxian Zhang, Lu Wang, Dong Xu

“…To develop and validate a nomogram model based on ultrasound features to predict ETE of papillary thyroid carcinoma for preoperative assessment. …”
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Molecular classification of hepatocellular carcinoma based on zoned metabolic feature and oncogenic signaling pathway by Tomoko Aoki, Naoshi Nishida, Yutaka Kurebayashi, Kazuko Sakai, Naoto Fujiwara, Masakatsu Tsurusaki, Kohei Hanaoka, Masahiro Morita, Hirokazu Chishina, Masahiro Takita, Satoru Hagiwara, Hiroshi Ida, Kazuomi Ueshima, Yasunori Minami, Atsushi Takebe, Takaaki Murase, Keiko Kamei, Takuya Nakai, Ippei Matsumoto, Kazuto Nishio, Masatoshi Kudo

“…Background/Aims Previously, we advocated the importance of classifying hepatocellular carcinoma (HCC) based on physiological functions. This study aims to classify HCC by focusing on liver-intrinsic metabolism and glycolytic pathway in cancer cells. …”
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Benchmarking ML in ADMET predictions: the practical impact of feature representations in ligand-based models by Gintautas Kamuntavičius, Tanya Paquet, Orestis Bastas, Dainius Šalkauskas, Alvaro Prat, Hisham Abdel Aty, Aurimas Pabrinkis, Povilas Norvaišas, Roy Tal

“…Abstract This study, focusing on predicting Absorption, Distribution, Metabolism, Excretion, and Toxicology (ADMET) properties, addresses the key challenges of ML models trained using ligand-based representations. We propose a structured approach to data feature selection, taking a step beyond the conventional practice of combining different representations without systematic reasoning. …”
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Study on the predictive value of preoperative CT features for the mitotic index of GIST based on the nomogram by Ren Yingzheng, Jiang Linlin, Yang Yang, An Junjie, Dong Yonghong

“…Abstract This study aimed to construct a Nomogram based on preoperative CT features to predict the mitotic index in gastrointestinal stromal tumors and to establish preoperative risk stratification. …”
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Selective Feature Sets Based Fake News Detection for COVID-19 to Manage Infodemic by Manideep Narra, Muhammad Umer, Saima Sadiq, Ala' Abdulmajid Eshmawi, Hanen Karamti, Abdullah Mohamed, Imran Ashraf

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Transfer learning based feature selection for feedforward neural network for speech emotion classifier by D. V. Krasnoproshin, M. I. Vashkevich

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Series-arc-fault diagnosis using feature fusion-based deep learning model by Won-Kyu Choi, Se-Han Kim, Ji-Hoon Bae

“…We propose a series-arc-fault detector that uses a transfer learning (TL)-based feature fusion model. The model is trained stagewise for various features in the time and frequency domains using a one-dimensional convolutional neural network combined with a long short-term memory model that uses an attention mechanism to accurately detect arc-fault features. …”
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An adaptive power system transient stability assessment method based on shared feature extraction by Jiexiang Hu, Le Zheng, Wei Ai, Yansong Li, Jun Liu, Xinglei Chen

“…This paper proposes a robust and transferable adaptive TSA method based on shared feature extraction of the power system. …”
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Frequency hopping modulation recognition based on time-frequency energy spectrum texture feature by Hongguang LI, Ying GUO, Ping SUI, Zisen QI

“…For frequency hopping modulation identification,a novel method based on time-frequency energy spectrum texture feature was proposed.Firstly,the time-frequency diagram of the frequency hopping signal was obtained by smoothed pseudo Wigner-Ville distribution,and the background noise of the time-frequency diagram was removed by two-dimensional Wiener filtering to improve the resolution of the time-frequency diagram under low SNR conditions.Then,the connected-domain detection algorithm was used to extract the time-frequency energy spectrum of each hop signal and convert it into a time-frequency gray-scale image.The histogram statistical features and the gray-scale co-occurrence matrix feature were combined to form a 22-dimensional eigenvector.Finally,the feature set was trained,classified and identified by optimized support vector machine classifier.Simulation experiments show that the multi-dimensional feature vector extracted by the algorithm has strong representation ability and avoids the misjudgment caused by the similarity of single features.The average recognition accuracy of the six modulation methods of frequency hopping signals BPSK,QPSK,SDPSK,QASK,64QAM and GMSK is 91.4% under the condition of -4 dB SNR.…”
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A Block Object Detection Method Based on Feature Fusion Networks for Autonomous Vehicles by Qiao Meng, Huansheng Song, Gang Li, Yu’an Zhang, Xiangqing Zhang

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Graph-based fault diagnosis for rotating machinery: Adaptive segmentation and structural feature integration by Moirangthem Tiken Singh

“…To address these, this study introduces a novel graph-based framework for fault diagnosis that emphasizes interpretability, efficiency, and robustness. …”
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