Specific features of selection of promising hybrids of the genus Syringa for the variety testing by В. К. Горб

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Distinctive features of the biochemical composition sea buckthorn fruits of Altai selection by E. D. Rozhnov, M. N. Shkolnikova, O. V. Chugunova

“…The information on the distinctive features of the most common varieties in the selection of Azhurnaya, Altai, Inya, Chechek, Chuiskaya is summarized. …”
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Selective dorsal rhizotomy in cerebral palsy: the efficiency and the specific features of rehabilitation by Dinara B. Kurmanova, Saule T. Turuspekova, Vitalie S. Lisnic, Gulnara A. Mukhambetova, Bayan K. Demesinova, Nurmukhamed K. Mamashayev

“…The provided literature review contains a historic reference on the development of the technology along with the current data on the efficiency of surgical intervention with the analyzed research results in terms of the methods and the specific features of rehabilitation among the patients with the diagnosis of cerebral palsy after selective dorsal rhizotomy. …”
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Spectroscopic detection of cotton Verticillium wilt by spectral feature selection and machine learning methods by Weinan Li, Weinan Li, Weinan Li, Lisen Liu, Jianing Li, Weiguang Yang, Weiguang Yang, Yang Guo, Yang Guo, Longyu Huang, Longyu Huang, Zhaoen Yang, Jun Peng, Jun Peng, Xiuliang Jin, Xiuliang Jin, Yubin Lan, Yubin Lan

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Optimizing multimodal scene recognition through relevant feature selection approach for scene classification by Sumathi K, Pramod Kumar S, H R Mahadevaswamy, Ujwala B S

Subjects: “…Multimodal Feature extraction and Relevant Feature selection using Filter and Embedded approach…”
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Simultaneous Instance and Attribute Selection for Noise Filtering by Yenny Villuendas-Rey, Claudia C. Tusell-Rey, Oscar Camacho-Nieto

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A feature selection method utilizing path accumulation cost, redundancy minimization, and interaction maximization for the diagnosis of coronary heart disease by Jiayao Jiang, Zheng Yue, Hongling Zhu, Yan Wang, Hongsen Cai, Wenguang Hou

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Lung and Colon Cancer Classification Using Multiscale Deep Features Integration of Compact Convolutional Neural Networks and Feature Selection by Omneya Attallah

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CatBoost Optimization Using Recursive Feature Elimination by Agus Hadianto, Wiranto Herry Utomo

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Towards Ensemble Feature Selection for Lightweight Intrusion Detection in Resource-Constrained IoT Devices by Mahawish Fatima, Osama Rehman, Ibrahim M. H. Rahman, Aisha Ajmal, Simon Jigwan Park

Subjects: “…ensemble feature selection…”
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AccFIT-IDS: accuracy-based feature inclusion technique for intrusion detection system by C. Rajathi, P. Rukmani

“…In stage 1, various filter-based feature selection methods are applied extract features from intial dataset. …”
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Optimizing Solar Radiation Prediction with ANN and Explainable AI-Based Feature Selection by Ibrahim Al-Shourbaji, Abdalla Alameen

“…This paper presents an Artificial Neural Network (ANN) model optimized using feature selection techniques based on Explainable AI (XAI) methods to enhance SR prediction performance. …”
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Elevating Accuracy: Enhanced Feature Selection Methods for Type 2 Diabetes Prediction by Ghazaleh Kakavand Teimoory, MohammadReza Keyvanpour

“…In this study using the Pima dataset, we applied a preprocessing technique that utilized the most important features identified by the Random Forest algorithm and we used an ensemble method combining the SVM algorithm and Naïve Bayes for the model. …”
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Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP by YaFeng Li, XinGang Xu, WenBiao Wu, Yaohui Zhu, LuTao Gao, XiangTai Jiang, Yang Meng, GuiJun Yang, HanYu Xue

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Sparse Linear Discriminant Analysis With Constant Between-Class Distance for Feature Selection by Shuangle Guo, Yongxia Li, Jianguang Zhang, Yue Liu, Tian Tian, Mengchen Guo

Subjects: “…Feature selection…”
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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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Optimizing Mental Stress Detection via Heart Rate Variability Feature Selection by Mohsen Behradfar, Shotabdi Roy, Joseph Nuamah

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Adversarial Threats to Cloud IDS: Robust Defense With Adversarial Training and Feature Selection by Hariprasad Holla, Shashidhar Reddy Polepalli, Arun Ambika Sasikumar

“…To mitigate these vulnerabilities, we explicitly propose a dual-layered defense strategy: (i) adversarial training, explicitly incorporating adversarial examples into model training to improve robustness, and (ii) SHAP-based robust feature selection, explicitly enhancing interpretability and resilience by identifying stable, attack-resistant features. …”
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Steganographer identification of JPEG image based on feature selection and graph convolutional representation by Qianqian ZHANG, Yi ZHANG, Hao LI, Yuanyuan MA, Xiangyang LUO

“…Aiming at the problem that the feature dimension of JPEG image steganalysis is too high, which leads to the complexity of distance calculation between users and a decrease in the identification performance of the steganographer, a method for steganographer recognition based on feature selection and graph convolutional representation was proposed.Firstly, the steganalysis features of the user’s images were extracted, and the feature subset with highseparability was selected.Then, the users were represented as a graph, and the features of users were obtained by training the graph convolutional neural network.Finally, because inter-class separability and intra-class aggregation were considered, the features of users that could capture the differences between users were learned.For steganographers who use JPEG steganography, such as nsF5, UED, J-UNIWARD, and so on, to embed secret information in images, the proposed method can reduce the feature dimensions and computing.The identification accuracy of various payloads can reach more than 80.4%, and it has an obvious advantage at the low payload.…”
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Dynamic analysis of malicious behavior propagation based on feature selection in software network by Huajian Xue, Huajian Xue, Yali Wang, Qiguang Tang

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