Congridae (Teleostei: Anguilliformes) Family Species of Distributing in the Turkish Seas and Their General Features by Büşra Ulu, Bahar Bayhan

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Multi‐task learning using GNet features and SVM classifier for signature identification by Anamika Jain, Satish Kumar Singh, Krishna Pratap Singh

“…To extract the distinguishing features, a pre‐trained model GoogLeNet, which is fine‐tuned with the largest signature dataset present till date (GPDS Synthetic), has been used. …”
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Features of social and psychological adaptation of foreign students from far and near abroad by A. A. Gorozhankin, M. V. Iontseva

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Image semantic segmentation with hierarchical feature fusion based on deep neural network by Dawei Yang, Yan Du, Hongli Yao, Liyan Bao

“…To solve this problem, we present an image semantic segmentation with hierarchical feature fusion based on deep neural network (ISHF). …”
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Using Electrocardiogram Signal Features and Heart Rate Variability to Predict Epileptic Attacks by Ying Jiang, Yuan Feng, Danni Lu, Lin Yang, Qun Zhang, Haiyan Yang, Ning Li

“…In the proposed method, 12 features are extracted from the heart rate variability signal in time, frequency, time-frequency, and nonlinear domains to predict epileptic seizures. …”
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Unsupervised feature selection based on generalized regression model with linear discriminant constraints by Xiangguang Dai, Mingyu Guan, Facheng Dai, Wei Zhang, Tingji Zhang, Hangjun Che, Xiangqin Dai

“…To tackle these challenges, we present a novel unsupervised feature selection method that leverages the generalized regression model with linear discriminant constraints to learn discriminant and effective features from the data. …”
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Fusion of Deep and Time–Frequency Local Features for Melanoma Skin Cancer Detection by Hamidreza Eghtesaddoust, Morteza Valizadeh, Mehdi Chehel Amirani

“…However, increasing the accuracy of detection is still challenging. This paper presents a new method for MEL detection that considers the combination of deep and handcrafted time–frequency local features. …”
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Liver segmentation network based on detail enhancement and multi-scale feature fusion by Lu Tinglan, Qin Jun, Qin Guihe, Shi Weili, Zhang Wentao

“…Furthermore, to enable the model to better learn liver features at different scales, a Multi-Scale Feature Fusion module (MSFF) is added to the skip connections in the model. …”
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Machine learning for brain tumor classification: evaluating feature extraction and algorithm efficiency by Krishan Kumar, Kiran Jyoti, Krishan Kumar

“…A case study with interpretable machine learning is also presented in the paper.…”
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Clinical and demographic features of patients with dementia attended in a tertiary outpatient clinic by Francisco A.C. Vale, Stênio J.C. Miranda

“…We describe clinical and socio-demographic features of patients with dementia attended in a tertiary outpatient clinic during a three years period (56.9% of the total attendance). …”
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Geochemical features of texture –forming ground ice in diamondiferous areas of western Yakutia by L. P. Alekseeva, S. V. Alekseev, A. M. Kononov

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Clinical and pathological features of patients with recurrent anti-glomerular basement membrane disease by Ya-Ru Liu, Wei Ning, Yuan-Yuan Qi, Zhan-Zheng Zhao

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ECOLOGICAL FEATURES OF THE DISTRUBUTION OF A WOLF (CANIS LUPUS L., 1758) IN THE CRIMEA by Vitaliy L. Yarish, Nadezhda V. Antonets

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Designed to binge? Exploring user perceptions of interface features on video streaming platforms by Cynthia A. Dekker, Anna Tverdina

“…., automatic playing of new content after the previous content has ended). The present study aimed to shed light on the user perspective regarding these interface features by testing an extended Technology Acceptance Model. …”
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Integrated Neural Network for Ordering Optimization with Intertemporal-Dependent Demand and External Features by Minxia Chen, Ke Fu

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Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications by Duarte Coelho, Ana Madureira, Ivo Pereira, Ramiro Gonçalves, Susana Nicola, Inês César, Daniel Alves de Oliveira

“…In the age of rapidly advancing machine learning capabilities, the pursuit of maximum performance encounters the practical limitations imposed by limited resources in several fields. This work presents a cost-effective proposal for feature selection, which is a crucial part of machine learning processes, and intends to partly solve this problem through computational time reduction. …”
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Predictive modeling of visible-light azo-photoswitches’ properties using structural features by Said Byadi, P. K. Hashim, Pavel Sidorov

“…Abstract In this manuscript we present the strategy for modeling photoswitch properties (maximum absorption wavelength and thermal half-life of photoisomers) of visible-light azo-photoswitches using structural data. …”
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Effects of data transformation and model selection on feature importance in microbiome classification data by Zuzanna Karwowska, Oliver Aasmets, Estonian Biobank research team, Tomasz Kosciolek, Elin Org

“…Conclusions Microbiome data transformations can significantly influence feature selection but have a limited effect on classification accuracy. …”
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Nominal Metaphor Aptness: Semantic Features and Degree of Matching between Topic and Vehicle by Sedigheh Vahdat, Omid Khatin Zadeh, Babak Yazdani Fazlabadi

“…The instrument was a test including 20 nominal metaphors, each one followed by 10 semantic features of topic and vehicle. The participants were required to judge the degree of relevancy of each feature on the basis of a Likert scale ranging from 0 (irrelevant) to 3 (completely relevant). …”
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Features of Hydrogen-Enriched Methane–Air Flames Propagating in Hele-Shaw Channels by Sergey Yakush, Sergey Rashkovskiy, Maxim Alexeev, Oleg Semenov

“…The addition of hydrogen alters the kinetics and thermophysical properties of the mixtures, as well as the composition and properties of combustion products, requiring detailed research into the features of flame propagation in hydrogen-enriched hydrocarbon–air mixtures. …”
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