Advanced Cancer Classification Using AI and Pattern Recognition Techniques by Haddou Bouazza Sara, Haddou Bouazza Jihad

“…In this study, we explored how combining multiple feature selection methods with various classifiers enhances the identification of marker genes for four cancers: leukemia, lung, lymphoma, and ovarian cancer. …”
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A Multi-Branch Convolution and Dynamic Weighting Method for Bearing Fault Diagnosis Based on Acoustic–Vibration Information Fusion by Xianming Sun, Yuhang Yang, Changzheng Chen, Miao Tian, Shengnan Du, Zhengqi Wang

“…By utilizing an attention mechanism for dynamic feature selection and weighting, the robustness of classification is further improved. …”
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The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning by Fatemeh Bahrambanan, Meysam Alizamir, Kayhan Moradveisi, Salim Heddam, Sungwon Kim, Seunghyun Kim, Meysam Soleimani, Saeid Afshar, Amir Taherkhani

“…Based on feature selection models, four different scenarios were developed and five, ten, twenty and thirty features selected for designing a more accurate classification paradigm. …”
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Dual-hybrid intrusion detection system to detect False Data Injection in smart grids. by Saad Hammood Mohammed, Mandeep S Jit Singh, Abdulmajeed Al-Jumaily, Mohammad Tariqul Islam, Md Shabiul Islam, Abdulmajeed M Alenezi, Mohamed S Soliman

“…This paper addresses this gap by proposing a novel IDS that utilizes hybrid feature selection and deep learning classifiers to detect FDIAs in smart grids. …”
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A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables by Hua Li, Deyu Li, Yanhui Zhai, Suge Wang, Jing Zhang

“…Owing to the high dimensionality of multilabel data, feature selection in multilabel learning will be necessary in order to reduce the redundant features and improve the performance of multilabel classification. …”
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On Facial Expression Recognition Benchmarks by Ebenezer Owusu, Jacqueline Asor Kumi, Justice Kwame Appati

“…We selected published works from 2010 to 2021 and extracted, analyzed, and summarized the findings based on the most used techniques in feature extraction, feature selection, validation, databases, and classification. …”
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NEW METHOD FOR BEARING INTELLIGENT DIAGNOSIS BASED ON COMPRESSED SENSING AND MULTILAYER EXTREME LEARNING MACHINE by CHEN WanSheng, WANG Zhen, ZHAO HongJian, WANG FengTao

“…In the era of big data,bearing fault monitoring has the problem that it cannot realize the real-time processing of massive data processing and have the subjectivity about fault feature selection. In order to solve the above problems,a new bearing fault diagnosis method combining Compressed Sensing（ CS） and Multilayer Extreme Learning Machine（ ML-ELM） is proposed. …”
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Seleksi Fitur Menggunakan Hybrid Binary Grey Wolf Optimizer untuk Klasifikasi Hadist Teks Arab by M. Bahrul Subkhi, Chastine Fatichah, Agus Zaenal Arifin

“…This study proposes a feature selection method using the Hybrid Binary Gray Wolf Optimizer for Arabic text hadith classification. …”
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Kombinasi Seleksi Fitur Berbasis Filter dan Wrapper Menggunakan Naive Bayes pada Klasifikasi Penyakit Jantung by Siti Roziana Azizah, Rudy Herteno, Andi Farmadi, Dwi Kartini, Irwan Budiman

“…The purpose of this study is to determine the comparison of the accuracy results of Naive Bayes using several feature selections, namely Forward Selection, Backward Elimination, a combination of union of Forwad Selection and Backward Elimination feature selection results, Information Gain, Gain Ratio, and a combination of union of Information Gain feature selection results with Gain Ratio. …”
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Performance Comparison of IoT Classification Models using Ensemble Stacking and Feature Importance by nabila putri setiawan, Adhitya Nugraha, Ardytha Luthfiarta, Yudha Mulyana

“…Although the accuracy of the stacking model decreased to 92.4% after feature selection, the precision, recall, and F1-score remained high at 92.0. …”
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Comparative analysis of regression algorithms for drug response prediction using GDSC dataset by Soojung Ha, Juho Park, Kyuri Jo

“…Three analyses was conducted to show the effect of feature selection methods, multiomics information, and drug categories on drug response prediction. …”
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Analysis of Emotional Stress of Teachers in Japanese Teaching Process Based on EEG Signal Analysis by Jie Dong

“…An experiment of teachers’ emotional stress relief recognition in Japanese teaching process based on EEG signal was designed. The feature selection algorithm was used to screen the EEG feature combinations of teachers’ emotional stress relief and healthy subjects, and the classification experiment was carried out to verify the difference. …”
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Effects of Pooling Samples on the Performance of Classification Algorithms: A Comparative Study by Kanthida Kusonmano, Michael Netzer, Christian Baumgartner, Matthias Dehmer, Klaus R. Liedl, Armin Graber

“…We evaluate a variety of experimental designs using mock omics datasets with varying levels of pool sizes and considering effects from feature selection. Our results show that feature selection significantly improves classifier performance for non-pooled and pooled data. …”
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Effects of data transformation and model selection on feature importance in microbiome classification data by Zuzanna Karwowska, Oliver Aasmets, Estonian Biobank research team, Tomasz Kosciolek, Elin Org

“…Conclusions Microbiome data transformations can significantly influence feature selection but have a limited effect on classification accuracy. …”
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PLncWX: A Machine-Learning Algorithm for Plant lncRNA Identification Based on WOA-XGBoost by Fei Guo, Zhixiang Yin, Kai Zhou, Jiasi Li

“…There have been a large number of identification tools based on machine-learning and deep learning algorithms, mostly using human and mouse gene sequences as training sets, seldom plants, and only using one or one class of feature selection methods after feature extraction. We developed an identification model containing dicot, monocot, algae, moss, and fern. …”
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AccFIT-IDS: accuracy-based feature inclusion technique for intrusion detection system by C. Rajathi, P. Rukmani

“…To address this, the study proposed AccFIT (Accuracy-based Feature Inclusion Technique) for IDS, combining two-stage Feature Selection Algorithms (FSA). In stage 1, various filter-based feature selection methods are applied extract features from intial dataset. …”
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A novel and highly efficient botnet detection algorithm based on network traffic analysis of smart systems by Li Duan, Jingxian Zhou, You Wu, Wenyao Xu

“…Then, the autoencoder neural network for feature selection is used to improve the efficiency of model construction. …”
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Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University Hospital by Mohammad Alshraideh, Najwan Alshraideh, Abedalrahman Alshraideh, Yara Alkayed, Yasmin Al Trabsheh, Bahaaldeen Alshraideh

“…The primary objective of this study is to enhance prediction accuracy by utilizing a comprehensive approach that includes data preprocessing, feature selection, and model development. Various artificial intelligence techniques, namely, random forest, SVM, decision tree, naive Bayes, and K-nearest neighbours (KNN) were explored with particle swarm optimization (PSO) for feature selection. …”
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Intelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning models by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…These models can be further enhanced by the use of advanced feature selection and hyperparameter tuning techniques.…”
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Detection of COVID-19 Using Protein Sequence Data via Machine Learning Classification Approach by Siti Aminah, Gianinna Ardaneswari, Mufarrido Husnah, Ghani Deori, Handi Bagus Prasetyo

“…This study is aimed at developing an efficient classification model for coronavirus protein sequences using machine learning algorithms and feature selection techniques to aid in the early detection and prediction of novel viruses. …”
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