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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Supervised machine learning and genotype by trait biplot as promising approaches for selection of phytochemically enriched Rhus coriaria genotypes by Hamid Hatami Maleki, Reza Darvishzadeh, Ahmad Alijanpour, Yousef Seyfari

“…Here, the identified phytochemically superior sumac group was effectively distinguished from the inferior sumac group using ISSRs information via supervised machine learning. By using 13 feature selection algorithms, ISSR loci (U823) L1, (U835) L1, (U801) L1, (U816) L2, (U816) L4, (U835) L4, (U854) L1, and (U835) L9 were identified as functional markers which could predict phytochemical response of sumac germplasm. …”
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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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Evaluation of Machine Learning Algorithms for NB-IoT Module Energy Consumption Estimation Based on Radio Channel Quality by Dusan Bortnik, Vladimir Nikic, Srdjan Sobot, Dejan Vukobratovic, Ivan Mezei, Milan Lukic

“…Each data point, generated upon UDP packet transmission, includes metadata such as radio channel quality parameters, temporal parameters (TX and RX time), transmission and reception power, and coverage extension mode. Feature selection through variance and correlation analysis revealed that coverage extension mode and temporal parameters significantly correlate to the energy consumption. …”
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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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Assessment and analysis of factors influencing suicidal ideation in young adults: a large cohort study using an elastic network logistic regression model by Zixuan Guo, Xiaoli Han, Tiantian Kong, Yan Wu, Yimin Kang, Yanlong Liu, Fan Wang

“…An elastic network (EN) was used to optimize feature selection, combined with logistic regression, to determine the influencing factors associated with SI in young adults. …”
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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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Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model by Fırat Dişli, Mehmet Gedikpınar, Hüseyin Fırat, Abdulkadir Şengür, Hanifi Güldemir, Deepika Koundal

“…<b>Conclusions:</b> Unlike earlier works, this approach did not employ additional classifiers or feature selection algorithms. The developed model and image concatenation method offer a novel methodology for epilepsy diagnosis that can be extended to different datasets, potentially providing a valuable tool to support neurologists globally.…”
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SDRG-Net: Integrating multi-level color transformation encryption and ICNN-IRDO feature analysis for robust diabetic retinopathy diagnosis by Venkata Kotam Raju Poranki, B. Srinivasarao

“…Furthermore precisely, an Improved Red Deer Optimization (IRDO) algorithm is introduced for feature selection, which iteratively refines the feature space to retain the most informative features while discarding redundant or noisy ones. …”
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An Improved YOLOv8 Model for Strip Steel Surface Defect Detection by Jinwen Wang, Ting Chen, Xinke Xu, Longbiao Zhao, Dijian Yuan, Yu Du, Xiaowei Guo, Ning Chen

“…Additionally, we incorporate the BAM attention mechanism after the C2f module to strengthen the model’s feature selection capabilities. A bidirectional feature pyramid network is introduced at the neck of the model to improve feature transmission efficiency. …”
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PHYSICS-DRIVEN FEATURE CREATION TO IMPROVE MACHINE LEARNING MODELS PERFORMANCE FOR OIL PRODUCTION RATE PREDICTION by Eghbal Motaei, Seyed Mehdi Tabatabai, Tarek Ganat, Ahmad Khanifar, Sulaiman Dzaiy, Timur Chis

“…Several machine learning techniques, such as SVM, k-NN, Decision Tree, Random Forest, and linear regression, were constructed using PCA feature selection. The models were tuned and validated using k-fold cross-validation. …”
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Efficient diagnosis of diabetes mellitus using an improved ensemble method by Blessing Oluwatobi Olorunfemi, Adewale Opeoluwa Ogunde, Ahmad Almogren, Abidemi Emmanuel Adeniyi, Sunday Adeola Ajagbe, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Asif Mehmood, Habib Hamam

“…This research employs parallel and sequential ensemble ML approaches paired with feature selection techniques to boost classification accuracy. …”
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Ultrasonic Guided Waves-Based Monitoring of Rail Head: Laboratory and Field Tests by Piervincenzo Rizzo, Marcello Cammarata, Ivan Bartoli, Francesco Lanza di Scalea, Salvatore Salamone, Stefano Coccia, Robert Phillips

“…The importance of feature selection to maximize the sensitivity of the inspection system is demonstrated here. …”
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Distribution and Evolution of Chorus Waves Modeled by a Neural Network: The Importance of Imbalanced Regression by Xiangning Chu, Jacob Bortnik, Wen Li, Xiao‐Chen Shen, Qianli Ma, Donglai Ma, David Malaspina, Sheng Huang

“…Using an imbalanced regressive (IR) method, we develop a neural network model of lower‐band (LB) chorus waves using 7‐year observations from the EMFISIS instrument onboard Van Allen Probes. The feature selection process suggests that the auroral electrojet index alone captures most of the variations of chorus waves. …”
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Vegetation Classification in a Mountain–Plain Transition Zone in the Sichuan Basin, China by Wenqian Bai, Zhengwei He, Yan Tan, Guy M. Robinson, Tingyu Zhang, Xueman Wang, Li He, Linlong Li, Shuang Wu

“…The results show the following: (1) multi-feature combinations, especially spectral and topographic features, significantly improved classification accuracy; (2) Recursive Feature Elimination based on Random Forest (RF-RFE) outperformed ReliefF in feature selection, identifying more representative features; (3) all three algorithms performed well, with consistent spatial results. …”
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Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities by Munya A. Arasi, Hussah Nasser AlEisa, Amani A. Alneil, Radwa Marzouk

“…Furthermore, the marine predator algorithm is employed in feature selection. For the detection of indoor activities, the proposed MOEM-SMIADP model utilizes an ensemble of three classifiers, namely the graph convolutional network model, long short-term memory sequence-to-sequence (LSTM-seq2seq) method, and convolutional autoencoder. …”
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