Mf-net: multi-feature fusion network based on two-stream extraction and multi-scale enhancement for face forgery detection by Hanxian Duan, Qian Jiang, Xin Jin, Michal Wozniak, Yi Zhao, Liwen Wu, Shaowen Yao, Wei Zhou

“…In order to improve the cross-dataset detection performance of the model, this paper proposes a multi-feature fusion network based on two-stream extraction and multi-scale enhancement. …”
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Wheat Leaf Disease Detection: A Lightweight Approach with Shallow CNN Based Feature Refinement by Oumayma Jouini, Mohamed Ould-Elhassen Aoueileyine, Kaouthar Sethom, Anis Yazidi

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A population spatialization method based on the integration of feature selection and an improved random forest model. by Zhen Zhao, Hongmei Guo, Xueli Jiang, Ying Zhang, Changjiang Lu, Can Zhang, Zonghang He

“…The results showed that utilizing feature selection methods improves model accuracy to varying degrees compared with RF based on all factors, and the MDA-RF had the lowest MAPE of 0.174 and the highest R2 of 0.913 among them. …”
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Research on Bearing Fault Diagnosis Method for Varying Operating Conditions Based on Spatiotemporal Feature Fusion by Jin Wang, Yan Wang, Junhui Yu, Qingping Li, Hailin Wang, Xinzhi Zhou

“…The framework constructs a feature extraction and domain adaptation network based on a parallel architecture, designed to capture the complex dynamic characteristics of vibration signals. …”
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Deepfake Face Detection and Adversarial Attack Defense Method Based on Multi-Feature Decision Fusion by Shanzhong Lei, Junfang Song, Feiyang Feng, Zhuyang Yan, Aixin Wang

“…To improve the accuracy of deepfake face detection models and strengthen their resistance to adversarial attacks, this manuscript introduces a method for detecting forged faces and defending against adversarial attacks based on a multi-feature decision fusion. This approach allows for rapid detection of fake faces while effectively countering adversarial attacks. …”
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Evaluation of Shelf Life Prediction for Broccoli Based on Multispectral Imaging and Multi-Feature Data Fusion by Xiaoshuo Cui, Xiaoxue Sun, Shuxin Xuan, Jinyu Liu, Dongfang Zhang, Jun Zhang, Xiaofei Fan, Xuesong Suo

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Optimization Strategy for Underwater Target Recognition Based on Multi-Domain Feature Fusion and Deep Learning by Yanyang Lu, Lichao Ding, Ming Chen, Danping Shi, Guohao Xie, Yuxin Zhang, Hongyan Jiang, Zhe Chen

“…The NLARN was optimized based on the ResNet architecture, with the SE attention mechanism embedded. …”
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Exploring Consistent Feature Selection for Software Fault Prediction: An XAI-Based Model-Agnostic Approach by Adam Khan, Asad Ali, Jahangir Khan, Fasee Ullah, Muhammad Faheem

“…In this study we evaluated the consistency of two prominent XAI-based techniques, Permutation Feature Importance (PFI) and SHapley Additive exPlanations (SHAP), across five ML models: Linear Regression (LR), Multi-layer Perceptron (MLP), Random Forest (RF), Decision Trees (DT), and Support Vector Machines (SVM). …”
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Notice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion by Ma Jingyi, Tiejun Zhang, Jing Guodong, Yan Wenjun, Yang Bin

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Comparative Analysis of Deep Learning-Based Feature Extractors for Change Detection in Automotive Radar Maps by Harihara Bharathy Swaminathan, Aron Sommer, Uri Iurgel, Andreas Becker, Martin Atzmueller

“…In this paper, we present a set of experiments involving state-of-the-art deep learning architectures based on both convolution (CNN) and attention mechanisms such as AlexNet, GoogLeNet, VGG, ResNet, Vision Transformer, and Shifted Windows Transformer as possible candidates for the feature extractor backbone module in the Siamese architecture to detect changes caused by the disappearance and appearance of construction zones. …”
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Radar-Based Activity Recognition in Strictly Privacy-Sensitive Settings Through Deep Feature Learning by Giovanni Diraco, Gabriele Rescio, Alessandro Leone

“…A dataset was collected from seven volunteers performing ten activities which are part of daily living, including activities unique to bathroom environments, such as face washing, teeth brushing, dressing/undressing, and resting on the toilet seat. Deep learning models based on pre-trained feature extractors combined with bidirectional long short-term memory networks were employed for classification. …”
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Deep learning based local feature classification to automatically identify single molecule fluorescence events by Shuqi Zhou, Yu Miao, Haoren Qiu, Yuan Yao, Wenjuan Wang, Chunlai Chen

“…In this study, we introduce DEBRIS (Deep lEarning Based fRagmentatIon approach for Single-molecule fluorescence event identification), a deep-learning model focusing on classifying local features and capable of automatically identifying steady fluorescence signals and dynamically emerging signals of different patterns. …”
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Novel feature extraction method for signal analysis based on independent component analysis and wavelet transform. by Mariusz Topolski, Jędrzej Kozal

“…Recently many approaches for signal feature extraction were created. However, plenty of proposed methods are based on convolutional neural networks. …”
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Computer Vision-Based Drowsiness Detection Using Handcrafted Feature Extraction for Edge Computing Devices by Valerius Owen, Nico Surantha

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MemoCMT: multimodal emotion recognition using cross-modal transformer-based feature fusion by Mustaqeem Khan, Phuong-Nam Tran, Nhat Truong Pham, Abdulmotaleb El Saddik, Alice Othmani

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Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques by Abbas Jafar, Myungho Lee

“…Performance evaluation shows that the extra trees classifier, when using optimized selected features, achieved the highest performance with an accuracy of 97.70% and an area under the curve of 99.55%, outperforming previous models trained on raw and complete processed feature sets. …”
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Detection of Gallbladder Disease Types Using a Feature Engineering-Based Developed CBIR System by Ahmet Bozdag, Muhammed Yildirim, Mucahit Karaduman, Hursit Burak Mutlu, Gulsah Karaduman, Aziz Aksoy

“…<b>Results:</b> The developed model is compared with two different textural and six different Convolutional Neural Network (CNN) models accepted in the literature—the developed model combines features obtained from three different pre-trained architectures for feature extraction. …”
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An Efficient Deep Learning Method for Typhoon Track Prediction Based on Spatiotemporal Similarity Feature Mining by Kaiwen Lixia, Mingyue Lu, Yifei Lu, Hui Liu, Ping Li

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Enhancing parkinson disease detection through feature based deep learning with autoencoders and neural networks by P. Valarmathi, Y. Suganya, K. R. Saranya, S. Shanmuga Priya

“…This study presents an innovative approach to identify Parkinson’s disease (PD) through the examination of audio waves using Feature Based - Deep Neural Network (FB-DNN) techniques. …”
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A construction of heterogeneous transfer learning model based on associative fusion of image feature data by Wen-Fei Tian, Ming Chen, Zhong Shu, Xue-jun Tian

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