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. …”
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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. …”
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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. …”
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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.…”
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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.…”
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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. …”
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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. …”
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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.…”
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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.…”
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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. …”
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Spatial analysis of hyperspectral images for detecting adulteration levels in bon-sorkh (Allium jesdianum L.) seeds: Application of voting classifiers by Golshid Fathi, Seyed Ahmad Mireei, Mehrnoosh Jafari, Morteza Sadeghi, Hassan Karimmojeni, Majid Nazeri

“…Among the different feature selection methods, CARS-selected features performed best, with the corresponding voting model satisfactorily detecting adulteration levels, resulting in a RMSE of 6.69%. …”
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Bearing Defect Classification Algorithm Based on Autoencoder Neural Network by Manhuai Lu, Yuanxiang Mou

“…Comparative experiments show that the neural network can effectively complete feature selection and substantially improve classification accuracy while avoiding the laborious algorithm of the conventional method.…”
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A Convolutional Network-Based Intelligent Evaluation Algorithm for the Quality of Spoken English Pronunciation by Xia Zhan

“…Based on audio and convolutional neural network learning and training, it realizes the feature selection and classification recognition of spoken English pronunciation. …”
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Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models by Khabat Khosravi, Aitazaz A. Farooque, Amir Naghibi, Salim Heddam, Ahmad Sharafati, Javad Hatamiafkoueieh, Soroush Abolfathi

“…Notably, manually constructed input scenarios outperformed automated feature selection methods, with maximum temperature emerging as the most significant predictor of Ep variability. …”
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Model Klasifikasi Dengan Logistic Regression Dan Recursive Feature Elimination Pada Data Tidak Seimbang by Sutarman, Rimbun Siringoringo, Dedy Arisandi, Edi Kurniawan, Erna Budhiarti Nababan

“…The research results show that feature selection and class balancing have a positive impact. …”
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Diagnosis of Schizophrenia and Its Subtypes Using MRI and Machine Learning by Hosna Tavakoli, Reza Rostami, Reza Shalbaf, Mohammad‐Reza Nazem‐Zadeh

“…This result was achieved by the k‐nearest neighbor algorithm, utilizing 12 brain neuroimaging features, selected by the feature selection method of minimum redundancy maximum relevance (MRMR). …”
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Evolutionary Approach for Relative Gene Expression Algorithms by Marcin Czajkowski, Marek Kretowski

“…As checking all possible subsets of genes is computationally infeasible, the RXA algorithms require feature selection and multiple restrictive assumptions. …”
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The Expanded Invasive Weed Optimization Metaheuristic for Solving Continuous and Discrete Optimization Problems by Henryk Josiński, Daniel Kostrzewa, Agnieszka Michalczuk, Adam Świtoński

“…The algorithm is evaluated by solving three well-known optimization problems: minimization of numerical functions, feature selection, and the Mona Lisa TSP Challenge as one of the instances of the traveling salesman problem. …”
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Tibetan Microblog Emotional Analysis Based on Sequential Model in Online Social Platforms by Lirong Qiu, Huili Zhang, Zhen Zhang, Qiumei Pu

“…In addition, our experimental results on the Sina Weibo dataset clearly demonstrate the effectiveness of feature selection and the efficiency of our classification method.…”
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Retinopathy Diabetic Recognition and Detection Using Novel Intelligent Algorithms by Liaquat Ali Rahoo

“…The proposed approach consists of four levels: pre-processing for noise removal and standardization of the input dataset, image segmentation using Spiking Neural Network (SNN) based on edge detection, dimension reduction and feature selection using percolation theory, and the final step of combining SNN and percolation theory for retinopathy area detection. …”
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