Proteomics and machine learning: Leveraging domain knowledge for feature selection in a skeletal muscle tissue meta-analysis by Alireza Shahin-Shamsabadi, John Cappuccitti

“…Using biological context for interpreting the data resulted in improved model performance and made tailored analysis possible by reducing the dimensionality and increasing signal-to-noise ratio as well as only preserving biologically relevant features in each category. This integration of domain knowledge into data analysis and model training facilitated the discovery of new patterns while ensuring the retention of critical details, often overlooked when blind feature selection methods are used to exclude proteins with minimal expressions or variances. …”
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Insights from feature tracking of optical satellite data for studying rock glacier kinematics in the Northern Tien Shan by Ella Wood, Ella Wood, Tobias Bolch, Tobias Bolch, Richard Streeter

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Extraction and Recognition of Color Feature in true Color Images Using Neural Network Based on Colored Histogram Technique by Orjuwan Aljawadi

“…The importance of this research is based on developing the ability of (BPNN) in images ‘objects recognition based on color feature that is very important feature in artificial intelligence and colored image processing fields from developing the systems of alarms robots in fire recognition , medical digenesis of tumors, certain pattern’s recognition in different segments of an image , face and eyes’ iris recognition as a part of security systems , it helps solve the problem of limitation of recognition process in neural networks in many fields.…”
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Intelligent Classification Method for Rail Defects in Magnetic Flux Leakage Testing Based on Feature Selection and Parameter Optimization by Kailun Ji, Ping Wang, Yinliang Jia

“…The optimized PSO-RBF demonstrates superior capability in extracting MFL signal patterns, particularly for discriminating abrasions, spalling, indentations, and shelling defects, setting a new benchmark for industrial rail inspection.…”
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A Topology Identification Strategy of Low-Voltage Distribution Grids Based on Feature-Enhanced Graph Attention Network by Yang Lei, Fan Yang, Yanjun Feng, Wei Hu, Yinzhang Cheng

“…This paper proposes a topology identification strategy for LVDGs based on a feature-enhanced graph attention network (F-GAT). …”
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The Use of Cognition by Amphibians Confronting Environmental Change: Examples from the Behavioral Ecology of Crawfish Frogs (<i>Rana areolata</i>) by Michael J. Lannoo, Rochelle M. Stiles

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An Explainable Deep Learning Framework for Sorghum Weed Classification Using Multi-Scale Feature Enhanced DenseNet by Armaano Ajay, S. Sandosh, Aryan Saji, Harsh Agarwal

“…These blocks improve multi-scale feature extraction and adaptively capture complex patterns from the weed images. …”
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MBFE-UNet: A Multi-Branch Feature Extraction UNet with Temporal Cross Attention for Radar Echo Extrapolation by Huantong Geng, Han Zhao, Zhanpeng Shi, Fangli Wu, Liangchao Geng, Kefei Ma

“…Additionally, we introduce a Temporal Cross Attention Fusion Unit to model the temporal correlation between features from different network layers, which helps the model to better capture the temporal evolution patterns of radar echoes. …”
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Model-Based Feature Extraction and Classification for Parkinson Disease Screening Using Gait Analysis: Development and Validation Study by Ming De Lim, Tee Connie, Michael Kah Ong Goh, Nor ‘Izzati Saedon

“…These features were processed using advanced filtering techniques and analyzed through machine learning methods to distinguish between normal and PD-affected gait patterns. …”
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HierbaNetV1: a novel feature extraction framework for deep learning-based weed identification by Justina Michael, Thenmozhi Manivasagam

“…Extracting the essential features and learning the appropriate patterns are the two core character traits of a convolution neural network (CNN). …”
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Influence of adjacent crop type on occurrence of California ground squirrels on levees in the Sacramento Valley, California by Michael C. McGrann, Dirk H. Van Vuren, Miguel A. Ordeñana

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Associations of type D personality with amplitude-time ECG parameters by O. V. Kremleva

“…The purpose of the study is to identify the features of amplitude-time ECG parameters in type D personalities and to establish associations of amplitude-time ECG patterns in a non-cardiological sample of women with type D indicators and its components — NA and SI. …”
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Epidemiological characteristics of respiratory tract infections during and after the pandemic of COVID-19 from 2021 - 2023 in Shenzhen, southern China by Qiaoruo Jin, Sheng Yu, Jiuxin Qu

“…The analysis focused on changes in detection rates, epidemiological characteristics, and clinical features of respiratory pathogens, including three viruses and eight bacteria. …”
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Contactless Detection of Abnormal Breathing Using Orthogonal Frequency Division Multiplexing Signals and Deep Learning in Multi-Person Scenarios by Muneeb Ullah, Xiaodong Yang, Zhiya Zhang, Tong Wu, Nan Zhao, Lei Guan, Malik Muhammad Arslan, Akram Alomainy, Hafiza Maryum Ishfaq, Qammer H. Abbasi

“…A hybrid deep learning model, VGG16-GRU, combining convolutional neural networks (CNNs) and gated recurrent units (GRUs), was developed to capture both spatial and temporal features of continuous respiratory data. The model successfully classified 11 distinct breathing patterns with high accuracy, achieving an overall accuracy of 99.07&#x0025;, precision of 99.08&#x0025;, recall of 99.09&#x0025;, and an F1-score of 99.07&#x0025;. …”
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Subject based feature selection for hybrid brain computer interface using genetic algorithm and support vector machine by Nida Mateen, Mehreen Naeem, Muhammad Jawad Khan, Talha Yousaf, Ahsan Ali, Wael A. Altabey, Mohammad Noori, Sallam A Kouritem

“…The framework outperforms traditional filter- and wrapper-based feature selection methods on representative subjects, confirming its robustness and adaptability across individual neural patterns. …”
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Diagnosis of Schizophrenia Using Feature Extraction from EEG Signals Based on Markov Transition Fields and Deep Learning by Alka Jalan, Deepti Mishra, Marisha, Manjari Gupta

“…After the transformation, a pre-trained VGG-16 model is employed to extract meaningful features from the images. The extracted features are then passed through two separate classification pipelines. …”
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Machine Learning-Based Differential Diagnosis of Parkinson&#x2019;s Disease Using Kinematic Feature Extraction and Selection by Masahiro Matsumoto, Abu Saleh Musa Miah, Nobuyoshi Asai, Jungpil Shin

“…Initially, 18 kinematic features are extracted, including two newly proposed features: Thumb-to-index vector velocity and acceleration, which provide insights into motor control patterns. …”
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Electromyography-Based Gesture Recognition With Explainable AI (XAI): Hierarchical Feature Extraction for Enhanced Spatial-Temporal Dynamics by Jungpil Shin, Abu Saleh Musa Miah, Sota Konnai, Shu Hoshitaka, Pankoo Kim

“…The third branch, combining a Temporal Convolutional Network (TCN) and Bidirectional LSTM (BiLSTM), captures bidirectional temporal relationships and time-varying patterns. Outputs from all branches are fused using concatenation to capture subtle variations in the data and then refined with a channel attention module, selectively focusing on the most informative features while improving computational efficiency. …”
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Efficient Explainable Models for Alzheimer’s Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning by Yogita Dubey, Aditya Bhongade, Prachi Palsodkar, Punit Fulzele

“…To mitigate data imbalance, a random sampling technique is applied, ensuring a balanced representation of Alzheimer’s and healthy cases. Extensive feature analysis was conducted to identify the most impactful clinical features followed by feature reduction techniques to focus on the most informative clinical features, reducing model complexity and overfitting risks. …”
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Multiclass leukemia cell classification using hybrid deep learning and machine learning with CNN-based feature extraction by Sazzli Kasim, Sorayya Malek, JunJie Tang, Xue Ning Kiew, Song Cheen, Bryan Liew, Norashikin Saidon, Raja Ezman, Raja Shariff

“…Pre-trained CNNs are employed for feature extraction, while the classifiers refine the predictions for improved accuracy. …”
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