Demographic, Clinical Features and Outcome Determinants of Thoracic Trauma in Sri Lanka: A Multicentre Prospective Cohort Study by Yasith Mathangasinghe, Iddagoda Hewage Don Saman Pradeep, Dhammike Rasnayake

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Distribution, fishery and some features of biology for Sclerocrangon salebrosa and Argis lar (Caridea, Crangonidae) in the northwestern Okhotsk Sea by D. N. Yuriev, V. S. Lukyanov, A. Yu. Povarov

“…State of the fishery and distribution patterns of S. salebrosa and A. lar in the northwestern Okhotsk Sea are investigated on the data collected by the authors aboaed commercial fishing vessels and in the accounting trawl survey conducted aboard RV Dmitry Peskov in summer of 2019, as well as some features of their biology are considered. …”
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Clinical and neurophysiological features of the hereditary neuropathy with liability to pressure palsy due to the 17p11.2 deletion by Aline Pinheiro Martins de Oliveira, Raquel Campos Pereira, Patrícia Toscano Onofre, Vanessa Daccach Marques, Gilberto Brown de Andrade, Amilton Antunes Barreira, Wilson Marques Junior

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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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Morphological and structural features of the CdxPb1−xS films obtained by CBD from ethylenediamine-citrate bath by A. D. Kutyavina, L. N. Maskaeva, V. I. Voronin, I. A. Anokhina, V. F. Markov

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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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Influence of Ocean Current Features on the Performance of Machine Learning and Dynamic Tracking Methods in Predicting Marine Drifter Trajectories by Huan Lin, Weiye Yu, Zhan Lian

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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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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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Prenatal Ultrasound and Magnetic Resonance Imaging Features and Postnatal Outcomes of Congenital Hepatic Hemangioma: A Retrospective Analysis by Luyao Yang, Jianbo Teng, Xinhong Wei

“…This study aimed to explore the ultrasound and magnetic resonance features, growth patterns, and clinical outcomes of CHH. …”
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Micro-XCT analysis of anatomical features and dimensions of the incisive canal: implications for dental implant treatment in the anterior maxilla by Vladimir S. Todorovic, Mia-Michaela Beetge, Judy Kleyn, Jakobus Hoffman, Andre W. van Zyl

“…Furthermore, these anatomical features are compared between various racial and gender groupings in the study. …”
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Phenotyping Nontuberculous Mycobacterial Lung Disease: Comparative Analysis of Clinical and Imaging Features in a TB-Endemic Setting by Feng Y, Guo J, Luo S, Zhang Z

“…Yinping Feng, Jing Guo, Shuirong Luo, Zunjing Zhang Department of Tuberculosis, Lishui Hospital of Traditional Chinese Medicine Affiliated to Zhejiang University of Traditional Chinese Medicine, Lishui Tuberculosis Clinical Medical Research Center, Lishui, Zhejiang, People’s Republic of ChinaCorrespondence: Zunjing Zhang, Email zjzhang1979@126.comObjective: To systematically analyze clinical features and imaging characteristics of nontuberculous mycobacterial pulmonary disease (NTM-PD) patients in a tuberculosis specialty setting, establishing diagnostic and management references.Methods: We conducted a retrospective analysis of 204 NTM-PD cases admitted to our tuberculosis department from January 2018 to December 2023, evaluating clinical manifestations, mycobacterial speciation, and radiological patterns.Results: The cohort comprised 118 males and 86 females (mean age 65.34 ± 13.23 years), predominantly rural residents (63.24%). …”
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Exploring Voice Acoustic Features Associated with Cognitive Status in Korean Speakers: A Preliminary Machine Learning Study by Jiho Lee, Nayeon Kim, Ji-Wan Ha, Kyunghun Kang, Eunhee Park, Janghyeok Yoon, Ki-Su Park

“…Several acoustic features emerged as potentially important indicators, with DDA shimmer from the /i/ task and stdevF0 from the /puh-tuh-kuh/ task showing consistent patterns across classification tasks. …”
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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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Comprehensive Performance Comparison of Signal Processing Features in Machine Learning Classification of Alcohol Intoxication on Small Gait Datasets by Muxi Qi, Samuel Chibuoyim Uche, Emmanuel Agu

“…Statistical features yielded the highest accuracy (83.89%), followed by time-domain (83.22%) and frequency-domain features (82.21%). …”
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Machine Learning-Based Differential Diagnosis of Parkinson’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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