Parametric Estimation of Directional Wave Spectra from Moored FPSO Motion Data Using Optimized Artificial Neural Networks by Do-Soo Kwon, Sung-Jae Kim, Chungkuk Jin, MooHyun Kim

“…Artificial neural networks (ANNs), trained and optimized through hyperparameter tuning and feature selection, are employed to estimate wave parameters including the significant wave height, peak period, main wave direction, enhancement parameter, and directional-spreading factor. …”
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LASSO–MOGAT: a multi-omics graph attention framework for cancer classification by Fadi Alharbi, Aleksandar Vakanski, Murtada K. Elbashir, Mohanad Mohammed

“…By utilizing differential expression analysis (DEG) with Linear Models for Microarray (LIMMA) and LASSO regression for feature selection and leveraging graph attention networks (GATs) to incorporate protein–protein interaction (PPI) networks, LASSO–MOGAT effectively captures intricate relationships within multi-omics data. …”
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Age group classification based on optical measurement of brain pulsation using machine learning by Martti Ilvesmäki, Hany Ferdinando, Kai Noponen, Tapio Seppänen, Vesa Korhonen, Vesa Kiviniemi, Teemu Myllylä

“…ML experiments utilized support vector machines and random forest learners, along with maximum relevance minimum redundancy and principal component analysis for feature selection. Performance with increasing sample size was estimated using learning curve method. …”
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Complexity-Based Discrepancy Measures Applied to Detection of Apnea-Hypopnea Events by R. E. Rolón, I. E. Gareis, L. E. Di Persia, R. D. Spies, H. L. Rufiner

“…In the context of feature selection problems, several complexity-based measures have been proposed. …”
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Development of Hybrid Intrusion Detection System Leveraging Ensemble Stacked Feature Selectors and Learning Classifiers to Mitigate the DoS Attacks by P. Mamatha, S. Balaji, S. Sai Anuraghav

“…To tackle this aforementioned problem, this research article presents the hybrid IDS based on the combination of stacked feature selection methods such as Random Boruta Selector (RFS), Relief, Pearson coefficient (PCE) and Stacked learning classifiers (SLF). …”
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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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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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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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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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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’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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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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Adaptive Gaussian Incremental Expectation Stadium Parameter Estimation Algorithm for Sports Video Analysis by Lizhi Geng

“…The features with more discriminative power are selected from the set of positive and negative templates using a feature selection mechanism, and a sparse discriminative model is constructed by combining a confidence value metric strategy. …”
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Zero-day exploits detection with adaptive WavePCA-Autoencoder (AWPA) adaptive hybrid exploit detection network (AHEDNet) by Ahmed A. Mohamed, Abdullah Al-Saleh, Sunil Kumar Sharma, Ghanshyam G. Tejani

“…Additionally, a novel “Meta-Attention Transformer Autoencoder (MATA)” for enhancing feature extraction which address the subtlety issue, and improves the model’s ability and flexibility to detect new security threats, and a novel “Genetic Mongoose-Chameleon Optimization (GMCO)” was introduced for effective feature selection in the case of addressing the efficiency challenges. …”
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EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism by Jayoti Bansal, Gaurav Gangwar, Mohammad Aljaidi, Ali Alkoradees, Gagandeep Singh

“…Using an autoencoder for feature extraction, the Reptile Search Algorithm for feature selection, and a modified ResNet architecture for model training comprise the technique. …”
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