Design and Evaluation of a Leader–Follower Isomorphic Vascular Interventional Surgical Robot by Pengfei Chen, Yutang Wang, Dapeng Tian

“…The classification process includes time-frequency domain feature extraction, feature selection based on the Relief method and random forest (RF) method, and a BP neural network (NN) classifier. …”
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PD_EBM: An Integrated Boosting Approach Based on Selective Features for Unveiling Parkinson's Disease Diagnosis With Global and Local Explanations by Fahmida Khanom, Mohammad Shorif Uddin, Rafid Mostafiz

“…PD_EBM leverages machine learning (ML) algorithms and a hybrid feature selection approach to enhance diagnostic accuracy. …”
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A multigrained preference analysis method for product iterative design incorporating AI-generated review detection by Zhaojing Su, Mei Yang, Qingbo Zhai, Kaiyuan Guo, Yuexin Huang, Yangfan Cong

“…On the basis of the feature selection algorithm, a calculation method for the importance of product design features is proposed by introducing a random idea. …”
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Earthworm optimization algorithm based cascade LSTM-GRU model for android malware detection by Brij B. Gupta, Akshat Gaurav, Varsha Arya, Shavi Bansal, Razaz Waheeb Attar, Ahmed Alhomoud, Konstantinos Psannis

“…The paper used random forest model for feature selection. With a 99% accuracy and the lowest loss values, the proposed model performs better than conventional models including GRU, LSTM, RNN, Logistic Regression, and SVM.. …”
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Combined dynamic multi-feature and rule-based behavior for accurate malware detection by Mohamed Belaoued, Abdelaziz Boukellal, Mohamed Amir Koalal, Abdelouahid Derhab, Smaine Mazouzi, Farrukh Aslam Khan

“…We apply the proposed detection system on a combined set of three types of dynamic features, namely, (1) list of application programming interface calls; (2) application programming interface sequences; and (3) network traffic, which represents the IP addresses and domain names used by malware to connect to remote command-and-control servers. Feature selection and construction techniques, that is, term frequency–inverse document frequency and longest common subsequence, are performed on the three extracted features to generate new set of features, which are used to build behavioral Yet Another Recursive Acronym rules. …”
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Detecting travel modes from smartphone-based travel surveys with continuous hidden Markov models by Guangnian Xiao, Qin Cheng, Chunqin Zhang

“…However, these studies have struggled with three limitations: data collection-, feature selection-, and classification approach–related issues. …”
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Identifying unstable CNG repeat loci in the human genome: a heuristic approach and implications for neurological disorders by Varun Suroliya, Bharathram Uppili, Manish Kumar, Vineet Jha, Achal K. Srivastava, Mohammed Faruq

“…Using a computational approach, 15,069 CNG repeat loci in the coding and noncoding regions of the human genome were identified. Based on the feature selection criteria (repeat length >10 and functional location of repeats), we selected 52 repeats for further analysis and evaluated the repeat length variability in 100 control subjects. …”
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Biocomposite’s Multiple Uses for a New Approach in the Diagnosis of Parkinson’s Disease Using a Machine Learning Algorithm by Abdallah Al-Husban, Mustafa Mahdi Abdulridha, A. A. Hamad Mohamad, Abdelrahman Mohamed Ibrahim

“…Several data groups were constructed using a feature selection technique based on the label’s effect strength. …”
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Cough recognition in pneumoconiosis patients based on a flexible patch with an embedded ACC sensor for remote monitoring by Jiawen Wang, Chunyan Min, Feng Yu, Kai Chen, Ling Mao

“…The top 56% of the highest scoring features were then combined using several feature selection algorithms to perform the cough classification task. …”
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Construction of a health literacy prediction model for diabetic patients: A multicenter study by Zepeng Wang, Junyi Shi, Fangyuan Jiang, Kui Jiang, Yalan Chen

“…Based on the survey results, data from three communities were randomly selected as the test set, with the remaining data used as the training set. Feature selection was performed using recursive feature elimination. …”
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An interpretable machine learning model for predicting in-hospital mortality in ICU patients with ventilator-associated pneumonia. by Junying Wei, Heshan Cao, Mingling Peng, Yinzhou Zhang, Sibei Li, Wuhua Ma, Yuhui Li

“…<h4>Methods</h4>This study extracted VAP patient data from versions 2.2 and 3.1 of the MIMIC-IV database, using version 2.2 for model training and validation, and version 3.1 for external testing. Feature selection was conducted using the Boruta algorithm, and 14 ML models were constructed. …”
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Optimizing hypertension prediction using ensemble learning approaches. by Isteaq Kabir Sifat, Md Kaderi Kibria

“…A multi-faceted feature selection approach was employed, incorporating Boruta, Lasso Regression, Forward and Backward Selection, and Random Forest feature importance, and found 13 common features that were considered for prediction. …”
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XGBoost-enhanced ensemble model using discriminative hybrid features for the prediction of sumoylation sites by Salman Khan, Sumaiya Noor, Tahir Javed, Afshan Naseem, Fahad Aslam, Salman A. AlQahtani, Nijad Ahmad

“…By fusing word embeddings with evolutionary descriptors, it applies the SHapley Additive exPlanations (SHAP) algorithm for optimal feature selection and uses eXtreme Gradient Boosting (XGBoost) for classification. …”
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Model of the malicious traffic classification based on hypergraph neural network by Wenbo ZHAO, Zitong MA, Zhe YANG

“…As the use and reliance on networks continue to grow, the prevalence of malicious network traffic poses a significant challenge in the field of network security.Cyber attackers constantly seek new ways to infiltrate systems, steal data, and disrupt network services.To address this ongoing threat, it is crucial to develop more effective intrusion detection systems that can promptly detect and counteract malicious network traffic, thereby minimizing the resulting losses.However, current methods for classifying malicious traffic have limitations, particularly in terms of excessive reliance on data feature selection.To improve the accuracy of malicious traffic classification, a novel malicious traffic classification model based on Hypergraph Neural Networks (HGNN) was proposed.The traffic data was represented as hypergraph structures and HGNN was utilized to capture the spatial features of the traffic.By considering the interrelations among traffic data, HGNN provided a more accurate representation of the characteristics of malicious traffic.Additionally, to handle the temporal features of traffic data, Recurrent Neural Networks (RNN) was introduced to further enhance the model’s classification performance.The extracted spatiotemporal features were then used for the classification of malicious traffic, aiding in the detection of potential threats within the network.Through a series of ablative experiments, the effectiveness of the HGNN+RNN method was verified.These experiments demonstrate the model’s ability to efficiently extract spatiotemporal features from traffic, resulting in improved classification performance for malicious traffic.The model achieved outstanding classification accuracy across three widely-used open-source datasets: NSL-KDD (94% accuracy), UNSW-NB15 (95.6% accuracy), and CIC-IDS-2017 (99.08% accuracy).These results underscore the potential significance of the malicious traffic classification model based on hypergraph neural networks in enhancing network security and its capacity to better address the evolving landscape of network threats within the domain of network security.…”
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Constructing Attention-LSTM-VAE Power Load Model Based on Multiple Features by Chaoyue Ma, Ying Wang, Feng Li, Huiyan Zhang, Yong Zhang, Haiyan Zhang

“…Second, the correlation-based feature selection with maximum information coefficient (CFS-MIC) method is employed to select weather features based on their relevance, a subset of features with high correlation and low redundancy is chosen as model inputs. …”
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Hybrid GNSS time-series prediction method based on ensemble empirical mode decomposition with long short-term memory by Yu Zhou, Xiaoxing He, Shengdao Wang, Shunqiang Hu, Xiwen Sun, Jiahui Huang

“…To address the shortcomings of traditional GNSS time series prediction methods including insufficient feature selection, limited stability, and low predictive accuracy, this paper proposes a prediction model that combines the Ensemble Empirical Mode Decomposition (EEMD) with Long Short-Term Memory (LSTM) algorithm. …”
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Design of an Early Prediction Model for Parkinson&#x2019;s Disease Using Machine Learning by K. Velu, N. Jaisankar

“…Challenges such as class imbalance, feature selection, and interpretable predictive analysis still need to be addressed. …”
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MonoSeg: An Infrared UAV Perspective Vehicle Instance Segmentation Model with Strong Adaptability and Integrity by Peng Huang, Yan Yin, Kaifeng Hu, Weidong Yang

“…Our approach introduces three key innovations: (1) the Ghost Feature Bottle Cross module (GFBC), which enhances backbone feature extraction efficiency while significantly reducing computational over-head; (2) the Scale Feature Recombination module (SFR), which optimizes feature selection in the Neck stage through adaptive multi-scale fusion; and (3) Comprehensive Loss function that enforces precise instance boundary delineation. …”
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Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images by Fatemeh Zandie, Mohammad Salehi, Asghar Maziar, Mohammad Reza Bayatiani, Reza Paydar

“…A pipeline considering combinations of two feature selection (FS) methods and six ML classifiers was developed and evaluated. …”
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Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment by Xinfeng Ge, Jing Zhang, Ye Zhou, Jianguo Cai, Hui Zhang, Hongchang Hua, Dong Chen, Ming Zhao, Jinqi Du, Yuan Zheng

“…A rough set neural network (RS-BP hybrid model) of shaft orbit recognition is established, which uses just 13 moment eigenvalues reserved by the rough set feature selection algorithm as input variables; it has the same calculation error and recognition rate and reduces the calculation time step. …”
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