Efficient Chlorophyll Prediction and Sampling in the Sea: A Real-Time Approach With UCB-Based Path Planning by Perihan Karakose, Cafer Bal

“…To address these challenges, the research utilizes real-time measurable parameters, such as temperature and salinity, to predict chlorophyll levels. A feature selection method is employed to identify relevant factors, such as wind speed and conductivity, ensuring accurate predictions with minimal uncertainty. …”
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Machine Learning Approaches for Developing Land Cover Mapping by Ali Alzahrani, Awos Kanan

“…In this work, a genetic algorithm-based feature selection approach is used to enhance the performance of urban land cover classification. …”
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A Simultaneous Fault Diagnosis Method Based on Cohesion Evaluation and Improved BP-MLL for Rotating Machinery by Yixuan Zhang, Rui Yang, Mengjie Huang, Yu Han, Yiqi Wang, Yun Di, Dongke Su, Qidong Lu

“…In this paper, an improved simultaneous fault diagnostic algorithm with cohesion-based feature selection and improved backpropagation multilabel learning (BP-MLL) classification is proposed to localize and diagnose different simultaneous faults on gearbox and bearings in rotating machinery. …”
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Capsule neural network and adapted golden search optimizer based forest fire and smoke detection by Luling Liu, Li Chen, Mehdi Asadi

“…Testing this model on wildfire smoke imagery and the BowFire dataset reveals that the proposed methodology outperformed traditional feature selection and classification methods. The integration of the modified CNN and AGSO facilitated rapid response and mitigation efforts, enhancing the accuracy and dependability of forest fire identification. …”
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Analysis and Implementation of Optimization Techniques for Facial Recognition by Justice Kwame Appati, Huzaifa Abu, Ebenezer Owusu, Kwaku Darkwah

“…In this study, the principal component analysis (PCA) method with the inherent property of dimensionality reduction was adopted for feature selection. The resultant features were optimized using the particle swarm optimization (PSO) algorithm. …”
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Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization by S. Sakinah S. Ahmad, Witold Pedrycz

“…In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. …”
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An Approach to Fault Diagnosis for Gearbox Based on Image Processing by Yang Wang, Yujie Cheng

“…This work addresses a fault diagnosis method based on an image processing method for a gearbox, which overcomes the limitations of manual feature selection. Differing from the analysis method in a one-dimensional space, the computing method in the field of image processing in a 2-dimensional space is applied to accomplish autoextraction and fault diagnosis of a gearbox. …”
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Feature subset evaluation method for upper limb rehabilitation training based on joint feature discernibility by Guoyu Zuo, Zhaokun Xu, Jiahao Lu, Daoxiong Gong

“…A feature subset discernibility hybrid evaluation method using Fisher score based on joint feature and support vector machine is proposed for the feature selection problem of the upper limb rehabilitation training motion of Brunnstrom 4–5 stage patients. …”
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Abnormal traffic detection method based on LSTM and improved residual neural network optimization by Wengang MA, Yadong ZHANG, Jin GUO

“…Problems such as a difficulty in feature selection and poor generalization ability were prone to occur when traditional method was exploited to detect abnormal network traffic.Therefore, an abnormal traffic detection method based on the long short term memory network (LSTM) and improved residual neural network optimization was proposed.Firstly, the features and attributes of network traffic were analyzed, and the variability of the feature values was reduced by preprocessing of network traffic.Then, a three-layer stacked LSTM network was designed to extract network traffic features of different depths.Moreover, the problem of weak adaptability of feature extraction was solved.Finally, an improved residual neural network with skipping connecting line was designed to optimize the LSTM.The defects of deep neural network such as overfitting and gradient vanishing were optimized.The accuracy of abnormal traffic detection was improved.Experimental results show that the proposed method has higher training accuracy and better visibility of data processing.The classification accuracy rates under two classifications and multiple classifications are 92.3% and 89.3%.It has the lowest false positive rate when the parameters such as precision rate and recall rate are optimal.Moreover, it has strong robustness when the sample is destroyed.Furthermore, better generalization ability can be achieved.…”
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Automated Diagnosis of Coronary Artery Disease: A Review and Workflow by Qurat-ul-ain Mastoi, Teh Ying Wah, Ram Gopal Raj, Uzair Iqbal

“…Subsequently, stages (feature selection and classification) are same for both workflows. …”
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Cloud-Based Framework for COVID-19 Detection through Feature Fusion with Bootstrap Aggregated Extreme Learning Machine by Amjad Rehman Khan, Tanzila Saba, Tariq Sadad, Seng-phil Hong

“…The gain ratio is applied for feature selection to remove insignificant features. An extreme learning machine (ELM) is a neural network modification with a high capability for pattern recognition and classification problems for COVID-19 detection. …”
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Looking for Peer Circles: Graph-Mining-Based Educational Assessment and Refinement Toward Physical Instructors by Yuhe Zhu

“…To overcome these challenges, we propose a geometry-based feature selection technique to identify high-quality features that best represent each instructor’s teaching style. …”
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Enhanced classification of medicinal plants using deep learning and optimized CNN architectures by Hicham Bouakkaz, Mustapha Bouakkaz, Chaker Abdelaziz Kerrache, Sahraoui Dhelim

“…In this framework, a CNN architecture with residual and inverted residual block configurations is selected, and a set of data augmentation is applied to improve the dataset. Concerning feature selection, it adopts Binary Chimp Optimization and serial feature fusion regarding accuracy and speed. …”
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Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine by FU Qiang, HU Dong, YANG Tongliang, LUO Guoqing, TAN Weimin

“…ObjectiveIn response to challenges such as large sampling data, extended diagnosis time, and subjective fault feature selection in traditional bearing fault diagnosis, a CS-DMKELM intelligent diagnosis model for rolling bearings is proposed based on compressed sensing(CS) and deep multi-kernel extreme learning machine(D-MKELM) theory.MethodsFirstly, sparse signals were obtained through threshold processing of transformed domain signals. …”
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Flight Delay Classification Prediction Based on Stacking Algorithm by Jia Yi, Honghai Zhang, Hao Liu, Gang Zhong, Guiyi Li

“…In this research, the principle of the Stacking classification algorithm is introduced, the SMOTE algorithm is selected to process imbalanced datasets, and the Boruta algorithm is utilized for feature selection. There are five supervised machine learning algorithms in the first-level learner of Stacking including KNN, Random Forest, Logistic Regression, Decision Tree, and Gaussian Naive Bayes. …”
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A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests by Mahnaz Behroozi, Ashkan Sami

“…When our methodology comes with filter-based feature selection, it enhances classification accuracy up to 15%.…”
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Bankruptcy forecasting in enterprises and its security using hybrid deep learning models by Akshat Gaurav, Brij B. Gupta, Shavi Bansal, Konstantinos E. Psannis

“…We address the high-dimensional data and imbalanced problems by introducing feature selection strategically and Synthetic Minority Over-sampling Technique (SMOTE). …”
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Application of an Improved TF-IDF Method in Literary Text Classification by Lin Xiang

“…Using the improved TF-IDF method suggested in this research with the random forest (RF) classifier, the experimental results show that the classifier has a good classification impact, which can meet the actual work needs, based on comparative experiments on feature dimension selection, feature selection algorithm, feature weight algorithm, and classifier. …”
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HYEI: A New Hybrid Evolutionary Imperialist Competitive Algorithm for Fuzzy Knowledge Discovery by D. Jalal Nouri, M. Saniee Abadeh, F. Ghareh Mohammadi

“…Initially, the best feature subset is selected by using the embedded ICA feature selection, and then these features are used to generate basic fuzzy-classification rules. …”
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GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism by Burak Gülmez

“…The model's superior performance is attributed to its unique integration of evolutionary optimization, attention-based feature selection, and fuzzy logic's ability to handle uncertainty in financial data.…”
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