Cancer Classification Using Pattern Recognition and Computer Vision Techniques by Haddou Bouazza Sara, Haddou Bouazza Jihad

“…This study proposes a novel Hybrid Filter-Wrapper Gene Selection (HFWGS) method that integrates filter-based techniques (Signal-to-Noise Ratio, Correlation Coefficient, and ReliefF) with wrapper-based approaches to enhance feature selection for leukemia classification. Additionally, a Hybrid Statistical-Gene Voting (HSGV) approach was implemented to further refine classification accuracy. …”
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Model-Based Sensitivity Analysis on Aerosol Optical Thickness Prediction by Bo Han, Xiaowei Gao, Xiaohui Cui

“…Next, the attribute sensitivity orders are used for feature selection in the context of regression by removing insensitive attribute one at a time or by removing attributes whose sensitive orders are larger than number k . …”
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A New Rule-Based Classification Method Using Shape-Based Properties of Spectral Curves by Songuel Polat, Alain Tremeau, Frank Boochs

“…The effective handling and use of such datasets for classification requires processing steps (dimensionality reduction through feature selection or feature extraction) that are not always goal-oriented. …”
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Compressed Sensing Based Fingerprint Identification for Wireless Transmitters by Caidan Zhao, Xiongpeng Wu, Lianfen Huang, Yan Yao, Yao-Chung Chang

“…Complex analytical wavelet transform is used to obtain the envelope of the transient signal, and the corresponding features are extracted by using the compressed sensing theory. Feature selection utilizing minimum redundancy maximum relevance (mRMR) is employed to obtain the optimal feature subsets for identification. …”
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A Hierarchical Framework Approach for Voice Activity Detection and Speech Enhancement by Yan Zhang, Zhen-min Tang, Yan-ping Li, Yang Luo

“…The modified Wiener filter (MWF) approach is utilized for noise reduction in the speech enhancement block. For the feature selection and voting block, several discriminating features were employed in a voting paradigm for the consideration of reliability and discriminative power. …”
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Analyzing the Application of Machine Learning in Anemia Prediction by Li Yuxi

“…Obstacles such as data quality, feature selection, and model interpretability continue to hinder clinical adoption. …”
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Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques by Jaweria Kainat, Syed Sajid Ullah, Fahd S. Alharithi, Roobaea Alroobaea, Saddam Hussain, Shah Nazir

“…These algorithms have some limits in feature selection for the diseased portion, but they can be used in conjunction with other image processing methods. …”
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Machine learning-driven insights into ctDNA for oral cancer: Applications, models, and future prospects by Dheeraj Kumar, Saraswati Patel

“…SVM and RF models excel in classification and feature selection, while ANN and CNN models capture complex and spatial patterns, respectively. …”
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Fault early warning model of 4G base station out of service based on centralized monitoring data resources by Yang WANG, Yi MAN, Zhipeng CHEN

“…4G wireless base station equipment is an important link which has direct impact on information communication network customer service quality,and 4G wireless base stations out of service fault will directly block users' normal communication.Aiming at these problems,based on the centralized monitoring warning message data resources through association rule mining and time trace deduction analysis,4G base stations out of service fault short-term warning was achieved.Based on centralized monitoring equipment performance data resources,by the classification of network elements (data cleaning,feature selection,network elements clustering),index dimension reduction (grouped by cluster,principal component analysis),principal component expression and out of service fault correlation analysis,performance indicators selection and threshold analysis,4G base stations out of service fault long-term warning was achieved.The test can accurately predict the 27.8% of 4G base station equipment out of service fault next month.…”
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Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies by Paul Bosch, Mauricio Herrera, Julio López, Sebastián Maldonado

“…The proposed methodology is based on using well-established procedures of feature selection, which were used to determine a suitable brain functional network size related to math problem solving strategies and also to discover the most relevant links in this network without including noisy connections or excluding significant connections.…”
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A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods by Jaehee Jung, Heung Ki Lee, Gangman Yi

“…To define the relationship between IPR and GO terms, three pattern recognition techniques have been employed under different conditions, such as feature selection and weighted value, instead of a binary one.…”
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Application research on the time–frequency analysis method in the quality detection of ultrasonic wire bonding by Wuwei Feng, Xin Chen, Cuizhu Wang, Yuzhou Shi

“…Then, the principal component analysis method was further used for feature selection. Finally, an artificial neural network was built to recognize and detect the quality of ultrasonic wire bonding. …”
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Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model by Akshat Gaurav, Brij B. Gupta, Razaz Waheeb Attar, Ahmed Alhomoud, Varsha Arya, Kwok Tai Chui

“…In this context, this work suggests a new method to find drivers by means of a Random Forest model optimized using the osprey optimization algorithm (OOA) for feature selection and the salp swarm optimization (SSO) for hyperparameter tuning based on driving behavior. …”
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Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology by J. M. Urquiza, I. Rojas, H. Pomares, J. Herrera, J. P. Florido, O. Valenzuela

“…In the present work, a new approach is proposed to construct a PPI predictor training a support vector machine model through a mutual information filter-wrapper parallel feature selection algorithm and an iterative and hierarchical clustering to select a relevance negative training set. …”
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A Malware Detection Scheme Based on Mining Format Information by Jinrong Bai, Junfeng Wang, Guozhong Zou

“…Based on in-depth analysis of the static format information of the PE files, we extracted 197 features from format information of PE files and applied feature selection methods to reduce the dimensionality of the features and achieve acceptable high performance. …”
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Time Effort Prediction Of Agile Software Development Using Machine Learning Techniques by Muchamad Bachram Shidiq, Windu Gata, Sigit Kurniawan, Dedi Dwi Saputra, Supriadi Panggabean

“…For this reason, this research aims to predict the time effort of agile software development using Machine Learning techniques, namely the Decision Tree, Random Forest, Gradient Boosting, and AdaBoost algorithms, as well as the use of feature selection in the form of RRelieff and Principal Component Analysis (PCA) to improve prediction accuracy. …”
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Citrus diseases detection using innovative deep learning approach and Hybrid Meta-Heuristic. by Nouman Butt, Muhammad Munwar Iqbal, Shabana Ramzan, Ali Raza, Laith Abualigah, Norma Latif Fitriyani, Yeonghyeon Gu, Muhammad Syafrudin

“…To address these issues, this research examines the implementation of an automated disease classification system using deep learning and optimal feature selection. The system incorporates data augmentation and transfer learning with pre-trained models such as DenseNet-201 and AlexNet to improve diagnostic accuracy, efficiency, and cost-effectiveness. …”
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Modeling of Human Skin by the Use of Deep Learning by Xin Xiong, Xuexun Guo, Yiping Wang

“…Applying good classifier with best feature selection achieved good result in terms of accuracy, 95%, and recognition rate, 93%. …”
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Quantitative Analysis of Comprehensive Influence of Music Network Based on Logistic Regression and Bidirectional Clustering by Yi-Kun Zhao, Guo-Qing Wang, Xiao-Xiao Zhan, Peng-Hui Yang

“…Finally, the lasso region is used for feature selection to obtain the change factors in the process of music evolution and analyze the dynamic changes in the process of music development. …”
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Traffic classification in SDN-based IoT network using two-level fused network with self-adaptive manta ray foraging by Mohammed A. Aleisa

“…Existing traffic classification techniques often rely on manual feature selection, limiting adaptability and efficiency in dynamic environments. …”
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