Multimodal Feature-Driven Deep Learning for the Prediction of Duck Body Dimensions and Weight by Wenbo Xiao, Qiannan Han, Gang Shu, Guiping Liang, Hongyan Zhang, Song Wang, Zhihao Xu, Weican Wan, Chuang Li, Guitao Jiang, Yi Xiao

“…A dataset of 1023 Linwu ducks, comprising over 5000 samples with diverse postures and conditions, was collected to support model training. The proposed method innovatively employs PointNet++ to extract key feature points from point clouds, extracts and computes corresponding 3D geometric features, and fuses them with multi-view convolutional 2D features. …”
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Effective classification of android malware families through dynamic features and neural networks by Gianni D'Angelo, Francesco Palmieri, Antonio Robustelli, Arcangelo Castiglione

“…Therefore, the main aim of this paper is proposing a new dataset called Unisa Malware Dataset (UMD) available on http://antlab.di.unisa.it/malware/, which is based on the extraction of static and dynamic features characterising the malware activities. …”
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A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM by Xin-Sheng Zhang

“…We formulate this classification problem as sparse feature learning based classification on behalf of the test samples with a set of training samples, which are also known as a “vocabulary” of visual parts. …”
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Advanced ECG feature extraction and SVM classification for predicting defibrillation success in OHCA by Haqi Zhang, Haqi Zhang, Xiaotian Pan, Xiaotian Pan, Shan Zhou, Weiwei Zhang, Jing Chen, Limin Pan

“…A Support Vector Machine (SVM) classifier, trained on these selected features, demonstrated a prediction accuracy of 95.6%, highlighting the efficacy of combining targeted ECG signal features with machine learning techniques to forecast defibrillation success accurately. …”
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Video image feature extraction method for police drones investigation and evidence collection by CHEN Sixin, HE Fengwei

“…Firstly, we made a detailed analysis of the HSV color of the target object in the video image. Then, based on the results of the HSV color space, attention mechanism was introduced for video image feature extraction, and constructed spectral multi-scale feature extraction network and spatial multi-scale feature extraction network with attention mechanism. …”
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A multi-scale temporal feature fusion framework for sheep voiceprint recognition by Xipeng Wang, Delong Wang, Weijiao Dai, Cheng Zhang, Yudongchen Liang, Yong Zhou, Juan Yao, Fang Tian

“…The model uses the feature pyramid network (FPN) structure and a one-dimensional convolutional block attention module (1D-CBAM) for feature fusion to enhance the classification ability of the model. …”
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MCGFE-CR: Cloud Removal With Multiscale Context-Guided Feature Enhancement Network by Qiang Bie, Xiaojie Su

“…Currently, cloud removal methods with better performance are mainly based on Convolutional Neural Networks (CNNs). However, they fail to capture global context information, resulting in the loss of global context features in image reconstruction. …”
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Remote Sensing Surveillance Using Multilevel Feature Fusion and Deep Neural Network by Laiba Zahoor, Haifa F. Alhasson, Mohammed Alnusayri, Mohammed Alatiyyah, Dina Abdulaziz Alhammadi, Ahmad Jalal, Hui Liu

“…Our system presents a reliable drone-based human action system through the integration of state-of-the-art methods for multilevel feature extraction and object detection. …”
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Methodological features of the development of the communicative competence of local general practitioner in preventive counseling by S. Yu. Astanina, A. M. Kalinina, R. N. Shepel, O. M. Drapkina

“…There is a contradiction between the need of teachers of internal medicine departments in the methodology for communicative competence (CC) of general practitioners in brief preventive counseling and the lack of evidence-based classes on CC formation in brief preventive counseling.The existing contradiction made it possible to identify the research problem — the need to develop a methodology for the development of CC of general practitioners in brief preventive counseling.In the context of the problem, the study aim was determined — to define the methodological features of CC formation in the general practitioner in brief preventive counseling.To achieve this goal, the following research methods were used: theoretical: theoretical analysis of philosophical, pedagogical, psychological and methodological literature; designing the educational process; experimental: direct and indirect pedagogical observation, pedagogical experiment, questioning, control sections.The methodological features of CC formation are the simultaneous development of the intellectual and emotional fields of the doctor’s personality (communication skills in conducting all stages of brief preventive counseling) and the volitional field of the doctor’s personality (belief in the need to master the CC of doctor-patient interaction).The method of doctor’s CC development is based on the technology of educational training. …”
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Multiple-Stream Models for a Single-Modality Dataset with Fractal Dimension Features by Yen-Ching Chang

“…For comparison, various multiple-stream models are developed based on the same dataset. The experimental results show that, with feature engineering, the accuracy can be raised from 91.67% (one-stream) to 94.52% (two-stream), 94.73% (three-stream), and 94.79% (four-stream), while, without feature engineering, it can be increased from 91.67% to 92.35%, 93.49%, and 93.66%, respectively. …”
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AI-Enhanced Photovoltaic Power Prediction Under Cross-Continental Dust Events and Air Composition Variability in the Mediterranean Region by Pavlos Nikolaidis

“…A new clustering methodology is introduced to classify these inputs and analyze their correlation with PV output, enabling improved feature selection for model training. Importantly, all input parameters are sourced from publicly accessible, internet-based platforms, facilitating wide reproducibility and operational application. …”
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Features of professional activity of an academic staff in the conditions of a digital educational environment by Ludmila Vladimirovna Chistobaeva

“…The purpose of the research is to theoretically substantiate the main areas of professional activity of academic staff in a digital educational environment.The research methodology was based on the study of scientific information and the experience of pedagogical activity in foreign and domestic literature, the use of theoretical and empirical research methods, such as analysis, generalization, deduction.The results of the research are as follows the features of the digital educational environment of a modern university have been substantiated; the structural and content characteristics of the activities of academic staff in the digital space of the university have been disclosed.Key conclusions are: the key role of digital technologies lies in the innovative development of a higher education, namely, in the competence of modern university teachers to work in a dig- ital educational environment; structural and content characteristics of the activity of academic staff in the digital space of a university include a number of areas: interaction with the subjects of education in the electronic information and educational environment of a university, creation of electronic methodological tools for readable disciplines, the use of various forms of organizing training sessions in the electronic educational environment, the creation of a digital portfolio, etc. …”
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Features of the pre-analytical stage in quantitative determination of TREC/KREC in peripheral blood by M. A. Saitgalina, Yu. V. Ostankova, A. V. Sedykh, A. A. Totolian

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The data analysis of sports training by ID3 decision tree algorithm and deep learning by Kaigong Wang, Lei Wang, Jiduo Sun

“…Based on this background, this paper comprehensively evaluates the performance of each model in different dimensions by comparing six key indicators: mean square error (MSE), mean absolute error (MAE), information gain, feature importance, sports performance improvement rate and training target achievement rate. …”
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Probabilistic Forecasting of Provincial Regional Wind Power Considering Spatio-Temporal Features by Gang Li, Chen Lin, Yupeng Li

“…Meanwhile, an efficient channel attention (ECA) mechanism and an improved quantile regression-based loss function are introduced in the training to directly generate prediction intervals. …”
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SPECIFIC FEATURES OF TEACHING THE CONCEPT OF SUSTAINABLE DEVELOPMENT IN EDUCATIONAL ESTABLISHMENTS OF UNTSUKULSKY DISTRICT by Nadira O. Guseynova, Zarema I. Soltanmuradova, Marina Z. Magomedova

“…The aim of the work is to identify the specific features of teaching the concept of sustainable development in the general education establishments of the Untsukul District of Dagestan. …”
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A scoping review of feedback features during clinical education for anaesthesia trainees by Santosh Patel, Franklin Dexter

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Comparison of MRI imaging features to differentiate degenerating fibroids from uterine leiomyosarcomas by William W Loughborough, Andrea G Rockall, Tanja T Gagliardi, Laura Satchwell, Emily Greenlay, Piers Osborne, Nishat Bharwani, Thomas Ind, Ayoma Attygalle, Dione Lother, Georgina Hopkinson, Robin Jones, Charlotte Benson, Aisha Miah, Aslam Sohaib, Christina Messiou

“…Results: Taking the features identified on the univariate analysis, the final diagnostic model was based on AP length ( p = .053), intermediate T2 signal (IT2), volume ( p = .002), and nodular border ( p = .001). …”
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Optimizing Pre-Trained Models for Medical Dataset Classification with a Fine-Tuning Approach by N. Kumar, T. Christopher

“…A set of the most important features is identified by executing Advanced Ensemble Feature Selection (EFS) procedures which include Few-shot Learning and Model-Agnostic Meta-Learning Algorithm (MAML) and Genetic Algorithm-Based Feature Selection (GAFS). …”
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CFP-AL: Combining Model Features and Prediction for Active Learning in Sentence Classification by Keuntae Kim, Yong Suk Choi

“…Additionally, through various comparative experiments with basic methods, we analyzed which data are most beneficial or harmful for model training. Through our research, researchers will be able to expand into the area of considering features in active learning, which has been difficult so far.…”
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