Monitoring Soil Salinity in Arid Areas of Northern Xinjiang Using Multi-Source Satellite Data: A Trusted Deep Learning Framework by Mengli Zhang, Xianglong Fan, Pan Gao, Li Guo, Xuanrong Huang, Xiuwen Gao, Jinpeng Pang, Fei Tan

“…The study applied four types of feature selection algorithms: Random Forest (RF), Competitive Adaptive Reweighted Sampling (CARS), Uninformative Variable Elimination (UVE), and Successive Projections Algorithm (SPA). …”
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A Hybrid Approach for Sports Activity Recognition Using Key Body Descriptors and Hybrid Deep Learning Classifier by Muhammad Tayyab, Sulaiman Abdullah Alateyah, Mohammed Alnusayri, Mohammed Alatiyyah, Dina Abdulaziz AlHammadi, Ahmad Jalal, Hui Liu

“…The system utilized a hybrid CNN (Convolutional Neural Network) + RNN (Recurrent Neural Network) classifier for event recognition, with Grey Wolf Optimization (GWO) for feature selection. Experimental results showed significant accuracy, achieving 98.5% on the UCF-101 dataset and 99.2% on the YouTube dataset. …”
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Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Ibrahim Elbatal, Ehab M. Almetwally, Marwa M. Eid, El-Sayed M. El-Kenawy

“…The data preparation used in the present methodology involves sophisticated stages such as Principal Component Analysis (PCA) as well as Blind Source Separation (BSS) to reduce noise as well as to improve feature selection. This purified input dataset is used in the DPRNNs model, where both short and long-term temporal dependencies in the data are captured well. …”
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Machine Learning-based Prediction of HBV-related Hepatocellular Carcinoma and Detection of Key Candidate Biomarkers by Zeynep KUCUKAKCALI, Sami AKBULUT, Cemil COLAK

“…Results: According to the feature-selection method, 18 genes were selected, and modeling was performed with these input variables. …”
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Application of Extreme Gradient Boosting Based on Grey Relation Analysis for Prediction of Compressive Strength of Concrete by Liyun Cui, Peiyuan Chen, Liang Wang, Jin Li, Hao Ling

“…One of its highlights is a feature selection methodology, i.e., GRA, which was used to determine the main input variables. …”
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Early Diagnosis of Alzheimer’s Disease Using Adaptive Neuro K-Means Clustering Technique by Karan Kumar, Shweta Agrawal, Isha Suwalka, Celestine Iwendi, Cresantus N. Biamba

“…The approach integrates the Adaptive Moving Self-Organizing Map (AMSOM), a neural network technique for unsupervised training and tissue segmentation, with K-means clustering and Principal Component Analysis (PCA) for feature selection. AMSOM dynamically updates neuron weights to improve segmentation accuracy. …”
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SLG-Net: Small-Large-Global Feature-Based Multilevel Feature Extraction Network for Ultrasound Image Segmentation by Xinya Fan, Jianwen Hu, Kai Hu

“…The LSKA module firstly extracts features by parallel small kernel module and large-scale feature selection (LSFS) module. The extracted features from above modules are added for further information interaction through a following multi-scale feature interaction module. …”
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Development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective dat... by Jiawen Deng, Hemang Yadav, Kiyan Heybati

“…Nested cross validation (CV) will be employed, with a tenfold inner CV loop for model tuning and selection as well as an outer loop using leave-one-site-out CV for external validation. Feature selection will be conducted using Boruta and least absolute shrinkage and selection operator-penalised logistic regression. …”
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Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China by Yudong Li, Zhongke Feng, Shilin Chen, Ziyu Zhao, Fengge Wang

“…In this paper, based on Guangxi’s 2010–2018 satellite monitoring hotspot data, meteorology, terrain, vegetation, infrastructure, and socioeconomic data, the researchers determined the main forest fire driving factors in Guangxi. They used feature selection and backpropagation neural networks and radial basis SVM to build forest fire prediction models. …”
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FECI-RTDETR a Lightweight Unmanned Aerial Vehicle Infrared Small Target Detector Algorithm Based on RT-DETR by Renzheng Xue, Shijie Hua, Haiqiang Xu

“…Subsequently, we combine the Efficient Additive feature selection mechanism with an intra-scale feature interaction module to form the EA-AIFI module, which strengthens the model’s focus on dense targets and reduces computational burden. …”
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Enhancing Localization Accuracy and Reducing Processing Time in Indoor Positioning Systems: A Comparative Analysis of AI Models by Salwa Sahnoun, Rihab Souissi, Sirine Chiboub, Aziza Chabchoub, Mohamed Khalil Baazaoui, Ahmed Fakhfakh, Faouzi Derbel

“…An in-depth analysis is provided in this paper for data cleaning and feature selection to reduce errors for all the models. …”
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Side-Scan Sonar Small Objects Detection Based on Improved YOLOv11 by Chang Zou, Siquan Yu, Yankai Yu, Haitao Gu, Xinlin Xu

“…Specifically, a new Sparse Feature (SF) module is proposed, replacing the Spatial Pyramid Pooling Fast (SPPF) module from the original YOLOv11 architecture to enhance object feature selection. Furthermore, the proposed YOLOv11-SDC integrates a Dilated Reparam Block (DRB) with a C3k2 module to broaden the model’s receptive field. …”
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Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review. by Rabia Asghar, Sanjay Kumar, Arslan Shaukat, Paul Hynds

“…., parallel data pre-processing, feature selection, and extraction. While some conventional ML models achieved up to 99% accuracy, accuracy was shown to decrease in concurrence with decreasing dataset size. …”
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A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical Infrastructure by Sardar Muhammad Ali, Abdul Razzaque, Muhammad Yousaf, Sardar Sadaqat Ali

“…To address class imbalance, we employed the Synthetic Minority Over-sampling Technique (SMOTE). For feature selection, we used Forward Feature Elimination (FFE), Backward Feature Elimination (BFE), and Recursive Feature Elimination (RFE) to identify the most relevant features. …”
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Molecular Modeling Studies of Substituted 2,4,5-Trisubstituted Triazolinones Aryl and Nonaryl Derivatives as Angiotensin II AT1 Receptor Antagonists by Mukesh C. Sharma, D. V. Kohli, Smita Sharma

“…Multiple linear regression (MLR) methodology coupled with feature selection method namely simulated annealing, was applied to derive Group based QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. …”
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Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking Ensemble by Xianglong Zhu, Ming Meng, Zewen Yan, Zhizeng Luo

“…Methods: Initially, we extract the features of Time Domain, Frequency domain, Time-Frequency domain, and Spatial Domain from the EEG signals, and perform feature selection for each domain to identify significant features that possess strong discriminative capacity. …”
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Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques:... by M. E. Alqaysi, A. S. Albahri, Rula A. Hamid

“…Finally, this study critically reviews the literature and attempts to address the diagnosis ASD research gaps in knowledge and highlights the available ASD datasets, AI techniques and ML algorithms, and the feature selection methods that have been collected from the final set of articles.…”
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A Quantum-Based Machine Learning Approach for Autism Detection Using Common Spatial Patterns of EEG Signals by S. Saranya, R. Menaka

“…Key features, including peak-to-peak amplitude, were extracted, and correlation-based feature selection identified the most informative features. …”
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A Hybrid Framework for Detection and Analysis of Leaf Blight Using Guava Leaves Imaging by Sidrah Mumtaz, Mudassar Raza, Ofonime Dominic Okon, Saeed Ur Rehman, Adham E. Ragab, Hafiz Tayyab Rauf

“…Binary Gray Wolf Optimization (BGWO) was used on the fused features for feature selection. The optimized features were given to the variants of SVM and KNN classifiers for classification. …”
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Intelligent deep federated learning model for enhancing security in internet of things enabled edge computing environment by Nasser Nammas Albogami

“…Besides, the IDFLM-ES technique uses data normalization and golden jackal optimization (GJO) based feature selection as a pre-processing step. Besides, the IDFLM-ES technique learns the individual and distributed feature representation over distributed databases to enhance model convergence for quick learning. …”
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