Metadiscourse Features in Aeronautics and Aerospace Engineering: The Use of Interactive and Interactional Markers by Ramsey Ferrer

“…As revealed in the study, the investigated metadiscourse markers resembled Hyland and Tse’s ( 2004 ) and Hyland’s ( 2005 , 2010 ) findings in terms of interactive and interactional markers, which contain similar features of academic writing such as formality and objectivity; however, in the use of interactional resources, it yielded a finding that supports informality through the use of self-mentions. …”
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FEATURES OF USE INTERACTIVE TASKS IN MODERN MEANS OF COMPUTER-BASED TRAINING by Victoria V. Bova, Elmar V. Kuliev, Anton A. Novikov

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Feature Integration and Feature Augmentation for Predicting GPCR-Drug Interaction by Isabelle Bichindaritz, Guanghui Liu

“…To improve the accuracy of GPCR-drug interaction prediction, this paper proposes a new GPCR-Drug interaction prediction method based on multi-feature integration and feature augmentation from deep random forest: First, the sequence features of GPCR from amino acid composition and protein evolution are extracted respectively, and the characteristics of the drug molecule from the molecular fingerprint perspective are formulated; then, the extracted multiple features are combined to obtain the feature representation of the GPCR-Drug pair; finally, based on the proposed GPCR-Drug feature representation method, we use deep random forest to generate augmented features and construct cascaded predictions model. …”
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Predicting drug-target interactions using machine learning with improved data balancing and feature engineering by Md. Alamin Talukder, Mohsin Kazi, Ammar Alazab

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Features of using social networks in self-learning foreign languages by O. Shumskyi

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Combining handcrafted and learned features using deep learning to improve protein-protein interaction prediction performance by Tran Hoai Nhan, Nguyen Phuc Xuan Quynh, Le Anh Phuong

“…Advances made in machine learning techniques, for example, DeepFE-PPI, GcForest-PPI, and DeepPPI, have been applied to enhance PPI prediction performance. However, most methods use either handcrafted or learned features. Improving protein representation quality can significantly impact PPI model performance. …”
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Novel model for medium to long term photovoltaic power prediction using interactive feature trend transformer by Xiang Liu, Qingyu Liu, Shuai Feng, Yangyang Ge, Haoran Chen, Chunling Chen

“…In order to improve the accuracy of medium and long-term photovoltaic power prediction, a unique hybrid deep learning model named interactive feature trend transformer (IFTformer) has been designed. …”
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Predicting Freeway Traffic Crash Severity Using XGBoost-Bayesian Network Model with Consideration of Features Interaction by Yang Yang, Kun Wang, Zhenzhou Yuan, Dan Liu

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Meeting needs: How social interaction anxiety, zoom fatigue, relatedness, and demographics predict virtual meeting feature preferences by Chaeyun Lim, Alex P. Leith, Rabindra Ratan, Maxwell Foxman, Dalton Bouzek

“…We then identified features related to social interaction anxiety (SIA), VM fatigue, and basic psychological need satisfaction, drawing on Self-Determination Theory and uses and gratification theory, and examined how these factors are associated with preferences for VM features by gender, race, and tenure. …”
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Aspect-Level Sentiment Analysis Based on Position Features Using Multilevel Interactive Bidirectional GRU and Attention Mechanism by Xiaodi Wang, Xiaoliang Chen, Mingwei Tang, Tian Yang, Zhen Wang

“…Secondly, the approach extracts the features of target terms and context by using a well-constructed multilevel interactive bidirectional neural network. …”
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Business Ecosystems: Specific Features of Organising Interactions and Communications by A. V. Lopukhin, E. A. Plaksenkov, S. N. Silvestrov

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Adaptive feature interaction enhancement network for text classification by Rui Su, Shangbing Gao, Kefan Zhao, Junqiang Zhang

“…To address this issue, we propose an Adaptive Feature Interactive Enhancement Network (AFIENet). …”
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Psychological Features of 7-8 Grade Students Interaction with Hypertext: A Theoretical and Experimental Study Using an Eye Tracker by N.A. Borisenko, A.A. Ganicheva, K.V. Mironova, S.V. Shishkova

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GGCRB: A Graph Neural Network Approach for Predicting CircRNA–RBP Interactions Using Structural and Sequence Features by Guangyi Tang, Hongyuan Xing, Dengju Yao, Xiaojuan Zhan, Xiangkui Li

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FI‐Net: Rethinking Feature Interactions for Medical Image Segmentation by Yuhan Ding, Jinhui Liu, Yunbo He, Jinliang Huang, Haisu Liang, Zhenglin Yi, Yongjie Wang

“…The multi‐level feature bridging module is used in skip connections to bridge multi‐level features and mask information to assist multi‐scale feature interaction. …”
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Feature fusion with attributed deepwalk for protein–protein interaction prediction by Mei-Yuan Cao, Suhaila Zainudin, Kauthar Mohd Daud

“…However, current computational methods often rely on single feature types or simple feature concatenation, potentially missing the complex nature of protein interactions. …”
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Features of the liquid interaction with surfaces as applied to the problem of aircraft icing by I. A. Amelyushkin, E. V. Krivopalova, M. A. Kudrov

“…A physical-mathematical model has been developed for calculating the elementary interaction act of a flow molecule with a solid body to reduce the time of molecular simulation while taking into account important physical features. …”
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FEATURES REGARDING INTERACTION OF ANCHOR PILES SUCH AS "TITAN" AND "GEOIZOL" WITH SOILS by M.I. Nikitenko, I.P. Dubatovka

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High-Order and Interactive Perceptual Feature Learning for Medical Image Retargeting by Mingjuan Ma, Yuehong Zhang

“…Each identified object-aware patch is then described using multi-channel low-level features. To model human attention and its perception of important elements within a CT image, a locality-preserved and interactive active learning (LIAL) approach is introduced. …”
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The assesment of the identification capabilities of features of the user interacts with a computer mouth by R. V. Borisov, D. N. Zverev, A. Ye. Sulavko, V. Yu. Pisarenko

“…The method of user identification by features of user interacts with computer mouse based on a monitoring of subject activities and using Bayesian networks for identification decision making.…”
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