Research on injection molded parts defect detection algorithm based on multiplicative feature fusion and improved attention mechanism by Rongnan Zhang, Yang Li, Zhiguang Guan

“…To enhance the accuracy of defect detection in injection molded parts, a new method called MRB-YOLO, based on the YOLOv8 model, has been proposed. This method introduces several key improvements: (1) the MAFHead, a four-detection head based on multiplicative feature fusion, which replaces the original detection head to enhance feature representation; (2) the RepGFPN-SE module, a re-parameterized generalized feature pyramid network that improves detection of small objects by replacing the original C2f. module; (3) and the BiNorma module, employing a bi-level routing attention mechanism to optimize the training process by reducing input distribution changes across layers. …”
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Recognition of Conus species using a combined approach of supervised learning and deep learning-based feature extraction. by Noshaba Qasmi, Rimsha Bibi, Sajid Rashid

“…In this report, we propose an ensemble learning strategy based on the combination of Random Forest (RF) and XGBoost (XGB) methods. …”
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GA-SVM method for single-phase grounding fault line selection in distribution network based on feature fusion by ZHANG Xiaopeng, BAI Jie, SUN Naijun, LI Jie, ZHENG Shuai, WAN Qingzhu

“…The four features are fused by principal component analysis method, the principal component is extracted, and the feature database is established. 80% of the feature database is used as the training set, 20% as the test set, the samples in the feature database are trained by GA-SVM to realize fault line selection. …”
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Research on herd sheep facial recognition based on multi-dimensional feature information fusion technology in complex environment by Fu Zhang, Fu Zhang, Xiaopeng Zhao, Shunqing Wang, Yubo Qiu, Sanling Fu, Yakun Zhang

“…A transfer learning strategy was employed for weight pre-training, and performance was evaluated using FPS, model weight, mean average precision (mAP), and test set accuracy. …”
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Simulation-Based Electrothermal Feature Extraction and FCN–GBM Hybrid Model for Lithium-ion Battery Temperature Prediction by Luyan WANG, Hongliang HAO, Zhongkang ZHOU, Huimin MA, Jin ZHAO, Zeyang LIU, Qiangqiang LIAO

“…The combination of voltage and resistance as input features significantly enhances prediction performance. …”
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Depth-based human activity recognition via multi-level fused features and fast broad learning system by Huang Yao, Mengting Yang, Tiantian Chen, Yantao Wei, Yu Zhang

“…Human activity recognition using depth videos remains a challenging problem while in some applications the available training samples is limited. In this article, we propose a new method for human activity recognition by crafting an integrated descriptor called multi-level fused features for depth sequences and devising a fast broad learning system based on matrix decomposition for classification. …”
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A data-centric and interpretable EEG framework for depression severity grading using SHAP-based insights by Anruo Shen, Jingnan Sun, Xiaogang Chen, Xiaorong Gao

“…Methods We proposed a data-centric, interpretable framework for EEG-based depression severity grading. A hybrid feature selection method was used, combining p-value and SHapley Additive exPlanations (SHAP) methods to select features that are both independently significant and jointly informative. …”
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Ubigo-X: Protein ubiquitination site prediction using ensemble learning with image-based feature representation and weighted voting by Disline Manli Tantoh, Jen-Chieh Yu, Ching-Hsuan Chien, Wei-Yi Yeh, Yen-Wei Chu

“…Single-Type SBF used amino acid composition (AAC), amino acid index (AAindex), and one-hot encoding; Co-Type SBF used Single-Type SBF via k-mer encoding; and S-FBF used secondary structure, relative solvent accessibility (RSA)/absolute solvent-accessible area (ASA), and signal peptide cleavage sites. S-FBF was trained using XGBoost, while Single-Type SBF and Co-Type SBF were transformed into image-based features and trained using Resnet34. …”
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Multiclass leukemia cell classification using hybrid deep learning and machine learning with CNN-based feature extraction by Sazzli Kasim, Sorayya Malek, JunJie Tang, Xue Ning Kiew, Song Cheen, Bryan Liew, Norashikin Saidon, Raja Ezman, Raja Shariff

“…This study presents a novel hybrid methodology that combines pre-trained CNN architectures, including VGG16, InceptionV3, and ResNet50, with advanced classification models such as Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and the deep learning-based Multi-Layer Perceptron (MLP). …”
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Deep learning feature-based model for predicting lymphovascular invasion in urothelial carcinoma of bladder using CT images by Bangxin Xiao, Yang Lv, Canjie Peng, Zongjie Wei, Qiao Xv, Fajin Lv, Qing Jiang, Huayun Liu, Feng Li, Yingjie Xv, Quanhao He, Mingzhao Xiao

“…Principal Component Analysis reduced features to 64. Using the extracted features, Decision Tree, XGBoost, and LightGBM models were trained with 5-fold cross-validation and ensembled in a stacking model. …”
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Prediction Method of Tangerine Peel Drying Moisture Ratio Based on KAN-BiLSTM and Multimodal Feature Fusion by Qi Ren, Jiandong Fang, Yudong Zhao

“…In this study, a prediction model of drying moisture ratio of tangerine peel based on Kolmogorov–Arnold network bidirectional long short-term memory (KAN-BiLSTM) and multimodal feature fusion is proposed. …”
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GA-VAE: Enhancing Local Feature Representation in VQ-VAE Through Genetic Algorithm-Based Token Optimization by Jinghui Jiang, Dongjoon Kim, Bohyoung Kim, Yeong-Gil Shin

“…This paper introduces GA-VAE, a fine-tuning framework that enhances local feature representation in pre-trained Vector Quantized-VAE (VQ-VAE) models through genetic algorithm-based optimization. …”
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A weighted pattern matching approach for classification of imbalanced data with a fireworks-based algorithm for feature selection by N. K. Sreeja

“…To improve the performance of PMC+, Fireworks based Feature and Weight Selection algorithm based on the idea of PMC+ has been proposed. …”
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Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learning by M. Vimala, SatheeshKumar Palanisamy, Sghaier Guizani, Habib Hamam

“…Third, CNN is used to train the retrieved features based on the labels that were found. …”
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A novel feature fusion and mountain gazelle optimizer based framework for the recognition of jute pests in sustainable agriculture by Soner Kiziloluk, Mucahit Karaduman, Serpil Aslan, Muhammed Yildirim, Muhammad Attique Khan, Fatimah Alhayan, Yunyoung Nam

“…In this developed model, two different pre-trained models were used for feature extraction. To improve the performance of the developed model, the features obtained using the DarkNet-53 and DenseNet-201 models were combined. …”
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A HYBRID APPROACH FOR MALARIA CLASSIFICATION USING CNN-BASED FEATURE EXTRACTION AND TRADITIONAL MACHINE LEARNING CLASSIFIERS by omar Mohammed Alzakholi, Walat A. Ahmed, Bafreen N. Mohammed, Asaad Kh. Ibrahim

“…For our study, we utilize VGG16 CNN with a weight pre-trained on ImageNet to extract the features from non-infected and infected blood cell images from malaria. …”
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HierbaNetV1: a novel feature extraction framework for deep learning-based weed identification by Justina Michael, Thenmozhi Manivasagam

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Semantic Segmentation of Aerial Laser Point Clouds Based on Deep-Residual Enhanced Coding of Multi-Feature Information by Xin Luo, Peng Lin, Xiaoxi Li, Zuqi Wei, Hai Li

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Automated Quality Control of Candle Jars via Anomaly Detection Using OCSVM and CNN-Based Feature Extraction by Azeddine Mjahad, Alfredo Rosado-Muñoz

“…The best-performing hybrid model CNN-based feature extraction followed by OCIF also achieved 100% across all evaluation metrics, confirming the effectiveness and robustness of the proposed approach. …”
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Diagnosis of Schizophrenia Using Feature Extraction from EEG Signals Based on Markov Transition Fields and Deep Learning by Alka Jalan, Deepti Mishra, Marisha, Manjari Gupta

“…After the transformation, a pre-trained VGG-16 model is employed to extract meaningful features from the images. …”
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