Manet: motion-aware network for video action recognition by Xiaoyang Li, Wenzhu Yang, Kanglin Wang, Tiebiao Wang, Chen Zhang

“…Actions in videos may vary at different speeds or scales, and it is difficult to cope with a wide variety of actions by relying on a single spatio-temporal scale to extract features. To address this problem, we propose a Motion-Aware Network (MANet), which includes three key modules: (1) Local Motion Encoding Module (LMEM) for capturing local motion features, (2) Spatio-Temporal Excitation Module (STEM) for extracting multi-granular motion information, and (3) Multiple Temporal Aggregation Module (MTAM) for modeling multi-scale temporal information. …”
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ATDMNet: Multi-Head Agent Attention and Top-k Dynamic Mask for Camouflaged Object Detection by Rui Fu, Yuehui Li, Chih-Cheng Chen, Yile Duan, Pengjian Yao, Kaixin Zhou

“…Camouflaged object detection (COD) encounters substantial difficulties owing to the visual resemblance between targets and their environments, together with discrepancies in multiscale representation of features. Current methodologies confront obstacles with feature distraction, modeling far-reaching dependencies, fusing multiple-scale details, and extracting boundary specifics. …”
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Interpretable Machine Learning for the German residential rental market – shedding light into model mechanics by Severin Bachmann

“…Main results are the superiority of XGB and LR showing higher SHAP values overall and thus explaining its lower predictive efficacy. DT models capture intricate interactions among variables with fewer features, while XGB accommodates more variables, emphasizing its higher complexity and thus superior performance. …”
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Flexible Adaptive Marine Predator Algorithm and Its Application in Fault Detection for Wind Turbines by WANG Wen, YI Jiabiao

“…Verification results showed the ability of FAMPA in effectively extracting key features from high-dimensional data, reducing data dimensions, and improving the accuracy and real-time performance of fault diagnosis models based on machine learning. …”
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Distillation-Based Cross-Model Transferable Adversarial Attack for Remote Sensing Image Classification by Xiyu Peng, Jingyi Zhou, Xiaofeng Wu

“…In the training stage, DMFAA distills features from Mamba to train surrogate model, ensuring that it retains its own structure while incorporating features from other models. …”
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Remote Sensing Image Change Captioning Using Multi-Attentive Network with Diffusion Model by Yue Yang, Tie Liu, Ying Pu, Liangchen Liu, Qijun Zhao, Qun Wan

“…Specifically, we introduce a diffusion feature extractor based on RS image dataset pre-trained diffusion model to capture the multi-level and multi-time-step feature representations of bitemporal RS images. …”
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Interpretable model based on MRI radiomics to predict the expression of Ki-67 in breast cancer by Li Zhang, Qinglin Du, Mengyi Shen, Xin He, Dingyi Zhang, Xiaohua Huang

“…The Shapley Additive Explanation (SHAP) algorithm was employed to explain the optimal model, and the AUC was used to assess the model’s performance. …”
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A comparative study of explainable machine learning models with Shapley values for diabetes prediction by Keona Pang

“…A detailed comparison of the performance of these models is given. Shapley values explain the nature of various machine learning models using visualization by color graphs to demonstrate the reliability of different machine learning models. …”
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Retrieval of Passive Seismic Virtual Source Data Under Non-Ideal Illumination Conditions Based on Enhanced U-Net by Wensha Huang, Pan Zhang, Binghui Zhao, Donghao Zhang, Liguo Han

“…The reconstructed recordings have a clear advantage in the reverse time migration (RTM) imaging results, with strong generalization performance across various velocity models.…”
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COVID-19 detection using machine learning and fusion-based deep learning models by Fatima Raheem, Manaf K. Hussein

“…Other scenarios are proposed in which the SVM Gabor-Wavelet and deep features are fused, and the three deep learning models are also fused to get better performance. …”
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Deep Learning Based Cross Domain Sentiment Classification for Urdu Language by Amna Altaf, Muhammad Waqas Anwar, Muhammad Hasan Jamal, Sana Hassan, Usama Ijaz Bajwa, Gyu Sang Choi, Imran Ashraf

“…In this paper, we present a baseline method for cross-domain sentiment analysis in the Urdu language using two different domains. Feature extraction is performed using n-grams and word embedding techniques. …”
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Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction by Jesutofunmi Ayo Fajemisin, Glebys Gonzalez, Stephen A. Rosenberg, Ghanim Ullah, Gage Redler, Kujtim Latifi, Eduardo G. Moros, Issam El Naqa

“…Various feature selection techniques and machine models interrogate the relevant radiomics features for predicting cancer treatment outcomes. …”
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A prediction method for radiation proctitis based on SAM-Med2D model by Ning Zhang, Haifeng Ling, Wenyu Zhang, Mei Zhang

“…Specifically, we utilize the Transformer-based SAM-Med2D model to extract visual features from CT images of cervical cancer patients. …”
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Development of a radiomic model to predict CEACAM1 expression and prognosis in ovarian cancer by Xiaoxue Zhang, Liping Han, Fangfang Nie, Huimin Zhang, Liming Li, Ruopeng Liang

“…The selected radiomic features were used to construct a radiomic model to predict CEACAM1 expression. …”
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A novel encrypted traffic detection model based on detachable convolutional GCN-LSTM by Xiaogang Yuan, Jianxin Wan, Dezhi An, Huan Pei

“…To address this issue, this paper proposes a Detachable Convolutional GCN-LSTM (DC-GL) model. The proposed model constructs graph-structured data by integrating protocol-layer features and traffic statistical features extracted from encrypted flows. …”
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Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model by Zhiwei Zhou, Qing Tao, Na Su, Jingxuan Liu, Qingzheng Chen, Bowen Li

“…These features were then used as inputs for the machine learning recognition model. …”
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A Novel Deep Learning Model for Motor Imagery Classification in Brain–Computer Interfaces by Wenhui Chen, Shunwu Xu, Qingqing Hu, Yiran Peng, Hong Zhang, Jian Zhang, Zhaowen Chen

“…The experimental results show that DB-BISAN achieves state-of-the-art performance. Also, ablation studies confirm that the Dual-Branch Feature Extraction and Blocked-Integration Self-Attention Mechanism are critical to the model’s performance. …”
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An interpretable coupled model (SWAT-STFT) for multispatial-multistep evapotranspiration prediction in the river basin by Zhonghui Guo, Chang Feng, Liu Yang, Qing Liu

“…These results demonstrated that the SWAT-STFT coupled model achieved satisfactory ET MMP performance. Additionally, the coupled model's SSTI leveraged model-internal attention weights to provide new insights into further understanding watershed ET spatiotemporal mechanisms. …”
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An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer by Boya Liu MM, Xiangrong Gu MM, Danling Xie MM, Bing Zhao BM, Dong Han MD, Yuli Zhang BM, Tao Li PhD, Jingqin Fang MD, PhD

“…A nomogram was created based on the best-performing model. Results A total of 1712 radiomics features were extracted from the intratumor and peritumor regions. …”
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DeepAT: A Deep Learning Wheat Phenotype Prediction Model Based on Genotype Data by Jiale Li, Zikang He, Guomin Zhou, Shen Yan, Jianhua Zhang

“…Deep learning has become an important tool for phenotype prediction due to its abilities in automatic feature learning, nonlinear modeling, and high-dimensional data processing. …”
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