Search Results - dynamic recursive feature selection algorithm

An improved synergistic dual-layer feature selection algorithm with two type classifier for efficient intrusion detection in IoT environment

Authors: G Logeswari K Thangaramya M Selvi et al.

Source: Scientific Reports, Vol 15, Iss 1, Pp 1-21 (2025)

Subject Terms: Intrusion detection system, Internet of things, Dynamic feature selection, Machine learning, Anomaly detection, Security systems, Medicine, Science

File Description: electronic resource

Relation: https://doaj.org/toc/2045-2322

Access URL: https://doaj.org/article/6f81eba9c6d8445fb74168f75cc6c241

FE-DIoT: IoT Device Classification Through Dynamic Feature Selection and Adaptive Cross-Network Model

Authors: Hanxi Zheng Huanpu Yin Haisheng Li

Source: IEEE Access, Vol 12, Pp 149099-149114 (2024)

Subject Terms: Internet of things, device identification, feature selection, dynamic weight adjustment, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/10707635/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/72e8f8a2afc64dea926f9b3c79329855

Feature selection for intrusion detection based on an improved rime optimization algorithm.

Authors: Peng, Qingyuan Wang, Xiaofeng Tang, Ao

Source: Molecular & Cellular Biomechanics; 2024, Vol. 21 Issue 3, p1-20, 20p

Subject Terms: INTRUSION detection systems (Computer security), FEATURE selection, OPTIMIZATION algorithms, CLASSIFICATION, DATA structures, INTERNET security

Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks.

Authors: Nancy, Periasamy Muthurajkumar, S. Ganapathy, S. et al.

Source: IET Communications (Wiley-Blackwell); Mar2020, Vol. 14 Issue 4, p888-895, 8p

Subject Terms: CONVOLUTIONAL neural networks, WIRELESS sensor network security, TELECOMMUNICATION security, ARTIFICIAL intelligence, DECISION trees, INTRUSION detection systems (Computer security), FEATURE selection

Enhancing Sarcopenia Prediction Through an Ensemble Learning Approach: Addressing Class Imbalance for Improved Clinical Diagnosis.

Authors: Turimov, Dilmurod Kim, Wooseong

Source: Mathematics (2227-7390); Jan2025, Vol. 13 Issue 1, p26, 23p

Subject Terms: ENSEMBLE learning, FEATURE selection, SUPPORT vector machines, MUSCLE mass, RANDOM forest algorithms

Innovative design of digital neural network-based biodata integration technology in cultural tourism management: Insights from cellular molecular biomechanics perspective.

Authors: Zhang, Qingying Li, Yiling

Source: Molecular & Cellular Biomechanics; 2025, Vol. 22 Issue 1, p1-22, 22p

Subject Terms: TOURISM management, BIOMETRIC identification, ARTIFICIAL neural networks, EMOTIONAL state, FEATURE selection, TOURISM, CELL communication

Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks

Authors: Sannasi Ganapathy M. Selvi S. V. N. Santhosh Kumar et al.

Source: IET Communications. 14:888-895

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-com.2019.0172
https://research.vit.ac.in/publication/intrusion-detection-using-dynamic-feature-selection-and-fuzzy
https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-com.2019.0172
https://dblp.uni-trier.de/db/journals/iet-com/iet-com14.html#NancyMGKSA20
https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-com.2019.0172
https://doi.org/10.1049/iet-com.2019.0172
https://digital-library.theiet.org/content/journals/10.1049/iet-com.2019.0172

Optimising the selection of welfare indicators in farm animals.

Authors: Day, Jon Ben Haddou, Mohamed Kylling, Rita et al.

Source: Frontiers in Veterinary Science. 2025, p1-19. 19p.

Dynamic Bayesian Network and Hidden Markov Model of Predicting IoT Data for Machine Learning Model Using Enhanced Recursive Feature Elimination

Authors: Noeiaghdam, S. Balamuralitharan, S. Govindan, V.

Source: Bulletin of the South Ural State University. Series "Mathematical Modelling, Programming and Computer Software". 15:111-126

Subject Terms: hidden markov model, principal component analysis, dynamic bayesain network, enhanced recursive feature elimination, 02 engineering and technology, динамическая байесовская сеть, машинное обучение, IoT данные здравоохраненияв, machine learning, расширенное рекурсивное устранение признаков, 0202 electrical engineering, electronic engineering, information engineering, healthcare IoT data, анализ главных компонентов, УДК 519.217, скрытая марковская модель

File Description: application/pdf

Access URL: http://dspace.susu.ru/xmlui/handle/00001.74/46645

dRFEtools: dynamic recursive feature elimination for omics

Authors: Kynon JM Benjamin Tarun Katipalli Apuã CM Paquola

Source: Bioinformatics

Subject Terms: Machine Learning, 0301 basic medicine, Applications Note, 03 medical and health sciences, 0206 medical engineering, 02 engineering and technology, Algorithms

Access URL: https://pubmed.ncbi.nlm.nih.gov/37632789

A dynamic recursive feature elimination framework (dRFE) to further refine a set of OMIC biomarkers

Authors: Yuanyuan Han Lan Huang Fengfeng Zhou

Source: Bioinformatics. 37:2183-2189

Subject Terms: 0301 basic medicine, 0303 health sciences, 03 medical and health sciences

Access URL: https://pubmed.ncbi.nlm.nih.gov/33515240
https://www.ncbi.nlm.nih.gov/pubmed/33515240
https://dblp.uni-trier.de/db/journals/bioinformatics/bioinformatics37.html#HanHZ21
https://pubmed.ncbi.nlm.nih.gov/33515240/
https://academic.oup.com/bioinformatics/article-abstract/37/15/2183/6124282

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms.

Authors: Potharlanka JL M NB

Source: Scientific reports [Sci Rep] 2024 Feb 05; Vol. 14 (1), pp. 2923. Date of Electronic Publication: 2024 Feb 05.

Publication Type: Journal Article

Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE

A ERSF-VIPA framework: scalable wildlife movement modelling for conflict mitigation.

Authors: Chen, Xiaoyi Li, Jie Cao, Xinyu et al.

Source: Movement Ecology. 10/30/2025, Vol. 13 Issue 1, p1-14. 14p.

Estimation of Biochemical Pigment Content in Poplar Leaves Using Proximal Multispectral Imaging and Regression Modeling Combined with Feature Selection.

Authors: Zhang, Changsai Xue, Yong

Source: Sensors (14248220); Jan2024, Vol. 24 Issue 1, p1-19, 19p, 2 Color Photographs, 6 Charts, 6 Graphs

Feature Selection for Stock Movement Direction Prediction Using Sparse Support Vector Machine.

Authors: Miao, Maoxuan Wu, Jinran Cai, Fengjing et al.

Source: Applied Stochastic Models in Business & Industry. May/Jun2025, Vol. 41 Issue 3, p1-26. 26p.

Subject Terms: *FINANCIAL markets, *STOCK price forecasting, FEATURE selection, SUPPORT vector machines, INDEPENDENT variables

Improving Depression Prediction Accuracy Using Fisher Score-Based Feature Selection and Dynamic Ensemble Selection Approach Based on Acoustic Features of Speech.

Authors: Janardhan, Naulegari Kumaresh, Nandhini

Source: Traitement du Signal. Feb2022, Vol. 39 Issue 1, p87-107. 21p.

Subject Terms: FEATURE selection, SPEECH, SUPPORT vector machines, RANDOM forest algorithms, K-nearest neighbor classification, MACHINE learning

Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms

Authors: Yuanyuan Han Lan Huang Fengfeng Zhou

Source: Genes, Vol 12, Iss 1814, p 1814 (2021)

Subject Terms: feature selection, swarm intelligence, machine learning, prediction, program code, Genetics, QH426-470

Relation: https://www.mdpi.com/2073-4425/12/11/1814; https://doaj.org/toc/2073-4425; https://doaj.org/article/a333203058734012b20f838f956b4c82

Availability: https://doi.org/10.3390/genes12111814
https://doaj.org/article/a333203058734012b20f838f956b4c82

Superpixel segmentation integrated feature subset selection for wetland classification over Yellow River Delta.

Authors: Cui L Zhang J Wu Z et al.

Source: Environmental science and pollution research international [Environ Sci Pollut Res Int] 2023 Apr; Vol. 30 (17), pp. 50796-50814. Date of Electronic Publication: 2023 Feb 17.

Publication Type: Journal Article

Journal Info: Publisher: Springer Country of Publication: Germany NLM ID: 9441769 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1614-7499 (Electronic) Linking ISSN: 09441344 NLM ISO Abbreviation: Environ Sci Pollut Res Int Subsets: MEDLINE

MeSH Terms: Wetlands* , Ecosystem*, Rivers ; Algorithms ; Random Forest

An Intelligent RPL attack detection using Machine Learning-Based Intrusion Detection System for Internet of Things.

Authors: Raghavendra, T Anand, M Selvi, M et al.

Source: Procedia Computer Science. 2022, Vol. 215, p61-70. 10p.

Subject Terms: Intrusion detection systems (Computer security), K-nearest neighbor classification, Internet of things, Feature selection, Machine learning, Internet protocols, Classification algorithms

Monkeypox diagnosis based on Dynamic Recursive Gray wolf (DRGW) optimization.

Authors: Saleh, Ahmed I. Hussien, Shaimaa A.

Source: Biomedical Signal Processing & Control. Jan2024:Part A, Vol. 87, pN.PAG-N.PAG. 1p.

Subject Terms: Grey Wolf Optimizer algorithm, Monkeypox, Feature selection, Support vector machines, K-nearest neighbor classification