Prediction Model of Phosphorus Content at the End Point of Electroslag Remelting Based on MI and XGBoost Algorithms by Liu　Yuxiao, Dong　Yanwu, Jiang　Zhouhua, Chen　Xi

“…The MI method is utilized for feature selection and assessment of factors affecting the endpoint phosphorus content. …”
Get full text

A Novel Two-Stage Spectrum-Based Approach for Dimensionality Reduction: A Case Study on the Recognition of Handwritten Numerals by Mohammad Amin Shayegan, Saeed Aghabozorgi, Ram Gopal Raj

“…Dimensionality reduction (feature selection) is an important step in pattern recognition systems. …”
Get full text

Identification of hypertension gene expression biomarkers based on the DeepGCFS algorithm. by Zongjin Li, Liqin Tian, Libing Bai, Zeyu Jia, Xiaoming Wu, Changxin Song

“…Finally, it combines integrated feature selection methods to determine the gene biomarkers. …”
Get full text

Dynamic Prediction of Physical Exertion: Leveraging AI Models and Wearable Sensor Data During Cycling Exercise by Aref Smiley, Joseph Finkelstein

“…In addition, Long Short-Term Memory (LSTM) networks were trained on the top features selected by the MRMR and Univariate Feature Ranking algorithms to enhance model performance. …”
Get full text

Prediction of Photovoltaic Power by ANN Based on Various Environmental Factors in India by B. Suresh Kumar, Jenifer Mahilraj, R. K. Chaurasia, Chitaranjan Dalai, A. H. Seikh, S. M. A. K. Mohammed, Ram Subbiah, Abdi Diriba

“…There were three ANN models that predicted PV output power with RMSEs of 2.1436, 6.1555, and 5.3551, respectively, utilising all features using the correlation feature selection (CFS) or relief feature selection (ReliefF) approaches. …”
Get full text

Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer prediction by Xiaojie Huang, Jiajia Chen, Peineng Liu

“…Our novel framework integrates ensemble resampling methods with advanced feature selection techniques, addressing data imbalance and enhancing predictive accuracy. …”
Get full text

An Optimized Deep-Learning-Based Network with an Attention Module for Efficient Fire Detection by Muhammad Altaf, Muhammad Yasir, Naqqash Dilshad, Wooseong Kim

“…In the subsequent phase, the proposed network utilizes an attention-based deep neural network (DNN) named Xception for detailed feature selection while reducing the computational cost, followed by adaptive spatial attention (ASA) to further enhance the model’s focus on a relevant spatial feature in the training data. …”
Get full text

Homogenized Point Mutual Information and Deep Quantum Reinforced Wind Power Prediction by W. G. Jency, J. E. Judith

“…In the first section, informative and relevant features required for robust wind power prediction using input wind turbine data are designed using Homogenized Point Mutual (HPM) Feature Selection model. With the relevant features selected, in the second section, the actual wind power prediction is made using the Deep Quantum Reinforced Learning model. …”
Get full text

Enhancing Sarcopenia Prediction Through an Ensemble Learning Approach: Addressing Class Imbalance for Improved Clinical Diagnosis by Dilmurod Turimov, Wooseong Kim

“…The data preprocessing stage included feature scaling and feature selection processes, utilizing recursive feature elimination to refine feature selection. …”
Get full text

Detecting the Early Stage of Phaeosphaeria Leaf Spot Infestations in Maize Crop Using In Situ Hyperspectral Data and Guided Regularized Random Forest Algorithm by Elhadi Adam, Houtao Deng, John Odindi, Elfatih M. Abdel-Rahman, Onisimo Mutanga

“…Overall, our study showed potential application of hyperspectral data, GRRF feature selection, and RF classifiers in detecting the early stage of PLS infestation in tropical maize.…”
Get full text

Exploring determinant factors influencing muscle quality and sarcopenia in Bilbao's older adult population through machine learning: A comprehensive analysis approach. by Naiara Virto, Danielle Marie Dequin, Xabier Río, Amaia Méndez-Zorrilla, Begoña García-Zapirain

“…<h4>Methods</h4>A total of 1253 older adults (89.5% women) with a mean age of 78.13 ± 5.78 voluntarily participated in this descriptive cross-sectional study, which examines determining factors in sarcopenia and MQI using machine learning techniques. Feature selection was completed using a variety of techniques and feature datasets were constructed according to feature selection. …”
Get full text

Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network by Guimei Yin, Jie Yuan, Yanjun Chen, Guangxing Guo, Dongli Shi, Lin Wang, Zilong Zhao, Yanli Zhao, Manjie Zhang, Yuan Dong, Bin Wang, Shuping Tan

“…This adaptive approach eliminates the human-specified criteria for feature selection and brain network construction. The trial results demonstrated that, when using a 6-second segment length and time-domain and frequency-domain features, patients with first-episode schizophrenia achieved the highest classification accuracy of 87.64% This method outperforms other feature selection and brain network modeling approaches, providing new insights and directions for the early diagnosis and recognition of schizophrenia.…”
Get full text

Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease by Tiantian Bai, Mengru Xu, Taotao Zhang, Xianjie Jia, Fuzhi Wang, Xiuling Jiang, Xing Wei

“…This study systematically evaluated the performance of each feature selection algorithm under different population sizes, specifically by comparing their average running time and objective function values to identify the optimal feature subset. …”
Get full text

Research prospects of user information detection from encrypted traffic of mobile devices by Tengfei ZHANG, Shunzheng YU

“…Encrypted traffic analysis of mobile devices can obtain multiple types of user information in an active or passive way, which provides protection for network security management and user privacy protection.The basic principles and key methods of data collection, feature selection, models and methods, and evaluation systems involved in these user information detection were analyzed and summarized.The problems in the existing projects were summarized, as well as the future research directions and challenges.…”
Get full text

Delving into biomarkers and predictive modeling for CVD mortality: a 20-year cohort study by Zhen Wu, Abdullahi Mohamud Hilowle, Ying Zhou, Changlin Zhao, Shuo Yang

“…This study aims to develop a predictive model for CVD-related mortality using a machine learning-based feature selection algorithm and assess its performance compared to existing models. …”
Get full text

Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model. by Li Wang, Yu Zhang, Feng Li, Caiyun Li, Hongzeng Xu

“…Firstly, oversampling technique was used to alleviate the class imbalance problem. Secondly, the feature selection method of Recursive Feature Elimination (RFE) was selected for effective feature selection. …”
Get full text

Continuous Patient-Independent Estimation of Respiratory Rate and Blood Pressure Using Robust Spectro-Temporal Features Derived From Photoplethysmogram Only by Muhammad Ahmad Sultan, Wala Saadeh

“…<italic>Conclusion:</italic> By using robust features and feature selection methods, we alleviated patient dependency to have reliable estimates of vitals.…”
Get full text

A Network Traffic Classification Method for Class-Imbalanced Data by Xiaohui Guan, Yaguan Qian

“…Based on the pipelining ensemble，it could be further conduct oversampling and customized feature selection for minority class，which may avoid the disturbance from majority class. …”
Get full text

Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches by Nafiseh Hosseini, Hamid Tanzadehpanah, Amin Mansoori, Mostafa Sabzekar, Gordon A. Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…Selecting the K parameter (nearest neighbor) has an essential impact on reducing the error rate. Feature selection analysis reduces the dimensions of the KNN model and increases the accuracy of final results.…”
Get full text

Comprehensive Evaluation of Bankruptcy Prediction in Taiwanese Firms Using Multiple Machine Learning Models by Hung V. Pham, Tuan Chu, Tuan M. Le, Hieu M. Tran, Huong T.K. Tran, Khanh N. Yen, Son V. T. Dao

“…Additionally, a wrapper-based feature selection (FS) utilizing Binary Particle Swarm Optimization (BPSO) was utilized to find an optimal feature subset and boost the model’s predictive performance. …”
Get full text