Software Defect Prediction For Quality Evaluation Using Learning Techniques Ensemble Stacking by Muhammad Romadhona Kusuma, Windu Gata, Sigit Kurniawan, Dedi Dwi Saputra, Supriadi Panggabean

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Survey on phishing detection research by Xi FU, Hui LI, Xingwen ZHAO

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ML-Based Quantitative Analysis of Linguistic and Speech Features Relevant in Predicting Alzheimer’s Disease by Tripti Tripathi, Rakesh Kumar

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Machine Learning-Based Classification of Turkish Music for Mood-Driven Selection by Nazime Tokgöz, Ali Değirmenci, Ömer Karal

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Stacking ensemble learning with heterogeneous models and selected feature subset for prediction of service trust in internet of medical things by Junyu Ren, Haibin Wan, Chaoyang Zhu, Tuanfa Qin

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Feature Selection for Physical Activity Prediction Using Ecological Momentary Assessments to Personalize Intervention Timing: Longitudinal Observational Study by Devender Kumar, David Haag, Jens Blechert, Josef Niebauer, Jan David Smeddinck

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A Comprehensive Approach to Intrusion Detection in IoT Environments Using Hybrid Feature Selection and Multi-Stage Classification Techniques by G. Logeswari, J. Deepika Roselind, K. Tamilarasi, V. Nivethitha

“…This paper’s key contribution lies in the integration of feature selection and classification techniques tailored for IoT environments, filling a critical gap in the state of the art and offering a more adaptive and efficient solution for real-time intrusion detection.…”
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Efficient Distributed Denial of Service Attack Detection in Internet of Vehicles Using Gini Index Feature Selection and Federated Learning by Muhammad Dilshad, Madiha Haider Syed, Semeen Rehman

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MSBKA: A Multi-Strategy Improved Black-Winged Kite Algorithm for Feature Selection of Natural Disaster Tweets Classification by Guangyu Mu, Jiaxue Li, Zhanhui Liu, Jiaxiu Dai, Jiayi Qu, Xiurong Li

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A hybrid approach for intrusion detection in vehicular networks using feature selection and dimensionality reduction with optimized deep learning. by Fayaz Hassan, Zafi Sherhan Syed, Aftab Ahmed Memon, Saad Said Alqahtany, Nadeem Ahmed, Mana Saleh Al Reshan, Yousef Asiri, Asadullah Shaikh

“…We proposed a hybrid approach uses automated feature engineering via correlation-based feature selection (CFS) and principal component analysis (PCA)-based dimensionality reduction to reduce feature matrix size before a series of dense layers are used for classification. …”
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ResInceptNet-SA: A Network Traffic Intrusion Detection Model Fusing Feature Selection and Balanced Datasets by Guorui Liu, Tianlin Zhang, Hualin Dai, Xinyang Cheng, Daoxuan Yang

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A two-tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things network security by Kashi Sai Prasad, P Udayakumar, E. Laxmi Lydia, Mohammed Altaf Ahmed, Mohamad Khairi Ishak, Faten Khalid Karim, Samih M. Mostafa

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Retrieval of Land Surface Temperature From Passive Microwave Observations Using CatBoost-Based Adaptive Feature Selection by Yang Dai, Yingbao Yang, Xin Pan, Penghua Hu, Xiangjin Meng, Fanggang Li, Zhenwei Wang

“…In this article, we proposed a PMW-LST retrieval method that integrates CatBoost-Based adaptive feature selection. First, we categorized the data into six groups based on the underlying surface types and data view time. …”
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Efficient Feature Selection and Hyperparameter Tuning for Improved Speech Signal-Based Parkinson’s Disease Diagnosis via Machine Learning Techniques by Deepak Painuli, Suyash Bhardwaj, Utku Kose

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Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: A network-based feature selection approach by Sara Dalmonte, Maria Adriana Cocozza, Dajana Cuicchi, Daniel Remondini, Lorenzo Faggioni, Paolo Castellucci, Andrea Farolfi, Emilia Fortunati, Alberta Cappelli, Riccardo Biondi, Arrigo Cattabriga, Gilberto Poggioli, Stefano Fanti, Gastone Castellani, Francesca Coppola, Nico Curti

“…Radiomic features were extracted from suspected lesion volumes identified by physicians and the most relevant radiomic features were selected to predict the presence or absence of LRRC. Feature selection was performed using a novel approach derived from gene expression analysis, based on the DNetPRO algorithm. …”
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Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques. by Mahade Hasan, Farhana Yasmin, Md Mehedi Hassan, Xue Yu, Soniya Yeasmin, Herat Joshi, Sheikh Mohammed Shariful Islam

“…Additionally, we have developed a novel voting system with feature selection techniques to advance heart disease classification. …”
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Circle chaotic map tuna swarm optimization (CCMTSO) based feature selection and deep learning approach for air quality prediction by Aradhyamatada Swamy, Rohitha U.M.

“…The significant achievement of this work was the design of a new FS (Feature selection) and prediction method for air quality. …”
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Enhanced thyroid disease prediction using ensemble machine learning: a high-accuracy approach with feature selection and class balancing by Md. Rezaul Islam, Aniruddha Islam Chowdhury, Sharmin Shama, Md. Masudul Hasan Lamyea

“…Our experimental results demonstrate that the proposed model outperforms existing methods, achieving 100% sensitivity and 99.72% accuracy using the XGBoost algorithm and SelectKBest feature selection. By addressing feature reduction and high class-imbalance, the ensemble ML classifier with hard voting proves more effective in handling classification challenges.…”
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Imagined Movement Recognition in People with Disabilities Using Common Sparse Spatio Spectral Pattern (CSSSP) and Sequential Features Selection (SFS) by Alireza Pirasteh, Manouchehr Shamseini Ghiyasvand, Majid Pouladian

“…With using sequential feature selection for feature extraction, it was revealed that CSSSP performance has been better compared to the CSP and CSSP in most cases and the average accuracy was 92.55%.…”
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Feature Selection and Classification Optimization of Transformer Oil Odor Data With Recursive Feature Elimination Using Grid Search and Cross-Validation by Bilge Han Tozlu

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