Advancing CVD Risk Prediction with Transformer Architectures and Statistical Risk Factor Filtering by Parul Dubey, Pushkar Dubey, Pitshou N. Bokoro

“…The HEART framework employs correlation-based filtering, Akaike information criterion (AIC), and statistical significance testing to refine feature subsets. The novelty lies in combining statistical risk factor filtration with attention-driven learning, enhancing both model performance and interpretability. …”
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Automated System Using HMM for Lung Disease Recognition Based on Cough Sounds by Shing-Tai Pan, Bo-Kai Lee, You-Qian Wu

“…This paper will use Hidden Markov Models (HMM) and Delta Mel-Frequency Cepstral Coefficients (MFCC) feature extraction to recognize the disease of a cough. …”
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Specificity of the operator’s activity performing the work with the forecasting tools of technologically and chronologically interconnected events in the system of continuous forec... by V. V. Savluchinskij

“…Creation of an automated continuous forecasting system based on tracking information flows requires the development of a number of algorithms and machine programs to build a model of the forecast object based on the obtained identification features, to optimize a branched technologically and chronologically interconnected network of hierarchically coordinated events with an example of the work of the operator performing the work with the prediction tool in the system continuous forecasting and tracking information E flows.…”
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Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics by Simona Ruxandra Volovăț, Tudor Ovidiu Popa, Dragoș Rusu, Lăcrămioara Ochiuz, Decebal Vasincu, Maricel Agop, Călin Gheorghe Buzea, Cristian Constantin Volovăț

“…Integrating autoencoder-derived features generally enhanced model performance. Logistic Regression saw an accuracy increase from 0.91 to 0.94, and SVM improved from 0.85 to 0.96. …”
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Surface defect detection on bolt surface using a real-time fine-tuned YOLOv6 model by Chhaya Gupta, Nasib Singh Gill, Preeti Gulia, Faeiz M. Alserhani, Piyush Kumar Shukla, J. Shreyas

“…The backbone design of the proposed YOLOBolt employs the Hybrid Extraction of Features Algorithm (HEFA) for feature extraction. Additionally, YOLOBolt employs the Convolutional Block-Attention Mechanism (CBAM) to enhance the model's precision in detecting small-sized flaws. …”
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Lane Detection Based on CycleGAN and Feature Fusion in Challenging Scenes by Eric Hsueh-Chan Lu, Wei-Chih Chiu

“…We use CycleGAN as a domain adaptation model combined with an image segmentation model to boost the model’s performance in different styles of scenes. …”
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Challenges and Perspectives in Interpretable Music Auto-Tagging Using Perceptual Features by Vassilis Lyberatos, Spyridon Kantarelis, Edmund Dervakos, Giorgos Stamou

“…We conducted a human survey to evaluate user trust in our methodology and in a state-of-the-art model, concluding that while the state-of-the-art model offers better performance, there are use cases where the slight deterioration in accuracy is outweighed by the increased trust and value provided by interpretability.…”
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Optimizing Curriculum Vitae Concordance: A Comparative Examination of Classical Machine Learning Algorithms and Large Language Model Architectures by Mohammed Maree, Wala’a Shehada

“…Traditional matching techniques, such as Logistic Regression, Decision Trees, Naïve Bayes, and Support Vector Machines, are constrained by the necessity of manual feature extraction, limited feature representation, and performance degradation, particularly as dataset size escalates, rendering them less suitable for large-scale applications. …”
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Cross-Feature Hybrid Associative Priori Network for Pulsar Candidate Screening by Wei Luo, Xiaoyao Xie, Jiatao Jiang, Linyong Zhou, Zhijun Hu

“…To enhance pulsar candidate recognition performance and improve model generalization, this paper proposes the cross-feature hybrid associative prior network (CFHAPNet). …”
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A conditional opposition-based particle swarm optimisation for feature selection by Jingwei Too, Ali Safaa Sadiq, Seyed Mohammad Mirjalili

“…Because of the existence of irrelevant, redundant, and noisy attributes in large datasets, the accuracy of a classification model has degraded. Hence, feature selection is a necessary pre-processing stage to select the important features that may considerably increase the efficiency of underlying classification algorithms. …”
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Distinct Views Improve Generalization and Robustness: Combinations of Augmentations With Different Features by Keon Kim, Hyun Woo Kim, Yong Suk Choi

“…This approach can prevent models from properly learning key features of objects, such as texture and shape. …”
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Bitcoin price direction prediction using on-chain data and feature selection by Ritwik Dubey, David Enke

“…A comparative analysis of feature selection, learning model performance, and trading strategy performance is also conducted. …”
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Recognizing and localizing chicken behaviors in videos based on spatiotemporal feature learning by Yilei Hu, Jinyang Xu, Zhichao Gou, Di Cui

“…This limitation highlights the insufficient temporal resolution of video-based behavior recognition models. This study presents a chicken behavior recognition and localization model, CBLFormer, which is based on spatiotemporal feature learning. …”
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Spoofing speech detection algorithm based on joint feature and random forest by Jiaqi YU, Zhihua JIAN, Jia XU, Lin YOU, Yunlu WANG, Chao WU

“…In order to describe the characteristic information of the speech signal more comprehensively and improve the detection rate of camouflage, a spoofing speech detection method based on the combination of uniform local binary pattern texture feature and constant Q cepstrum coefficient acoustic feature was proposed, which used random forest as the classifier model.The texture feature vector in the speech signal spectrogram was extracted by using the uniform local binary mode, and the joint feature was formed with the constant Q cepstrum coefficient.Then, the obtained joint feature vector was used to train the random forest classifier, so as to realize the camouflage speech detection.In the experiment, the performances of several spoofing detection systems constructed by other feature parameters and the support vector machine classifier model were compared, and the results show that the proposed speech spoofing detection system combined with the joint feature and the random forest model has the best performance.…”
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Spoofing speech detection algorithm based on joint feature and random forest by Jiaqi YU, Zhihua JIAN, Jia XU, Lin YOU, Yunlu WANG, Chao WU

“…In order to describe the characteristic information of the speech signal more comprehensively and improve the detection rate of camouflage, a spoofing speech detection method based on the combination of uniform local binary pattern texture feature and constant Q cepstrum coefficient acoustic feature was proposed, which used random forest as the classifier model.The texture feature vector in the speech signal spectrogram was extracted by using the uniform local binary mode, and the joint feature was formed with the constant Q cepstrum coefficient.Then, the obtained joint feature vector was used to train the random forest classifier, so as to realize the camouflage speech detection.In the experiment, the performances of several spoofing detection systems constructed by other feature parameters and the support vector machine classifier model were compared, and the results show that the proposed speech spoofing detection system combined with the joint feature and the random forest model has the best performance.…”
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MHFS-FORMER: Multiple-Scale Hybrid Features Transformer for Lane Detection by Dongqi Yan, Tao Zhang

“…It fuses multi-scale features with the Transformer Encoder to obtain enhanced multi-scale features. …”
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A Malware Classification Method Based on Knowledge Distillation and Feature Fusion by Xin Guan, Guodong Zhang

“…In the classification phase, an attention mechanism (global attention block) is added to better extract features and improve model robustness.MCF-RV also uses knowledge distillation with vit (Vision Transformer) as a faculty modeler, which can effectively optimize and improve the performance of large models. …”
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An explainable feature selection framework for web phishing detection with machine learning by Sakib Shahriar Shafin

“…Hence, effective detection depends on identifying the most critical features. Traditional feature selection (FS) methods often struggle to enhance ML model performance and instead decrease it. …”
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An ensemble machine learning approach for classification tasks using feature generation by Wenjuan Feng, Jin Gou, Zongwen Fan, Xiang Chen

“…This new data set is used as the input to the second layer (meta-classifier) to obtain the final model. Experiments based on the 20 data sets show that our proposed model FESVM has the best performance compared to the other machine learning classifiers under comparison. …”
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Multi-label feature selection via dual HSIC and sparse regularization by LI Bangna, HE Xingshi, ZHU Junwei

“…To rationally utilize the sample information and label information in multi-label data and improve the classification performance of the model, multi-label feature selection (DHSR) via dual Hilbert-Schmidt independence criterion (HSIC) and sparse regularization was proposed. …”
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