SMoFFI-SegFormer: a novel approach for ovarian tumor segmentation based on an improved SegFormer architecture by Qiuyin Xie, Jianuo Huang, Jingyang Sun, Chenxi Huang, Caixu Xu

“…In this study, we introduce SMoFFI-SegFormer, an advanced deep learning model specifically designed to enhance multi-scale feature representation and address the complexities of ovarian tumor segmentation. …”
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Handwriting Difficulties: A Review of Recent Advances in the Identification and Intervention by Suatmi Murnani, Kazuharu Hashitsume, Tomohiko Igasaki

“…Despite recent advances, challenges in standardization, dataset diversity, and model explainability still limit cross-study comparability and real-world applicability. …”
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Designing a hybrid stack ensemble model to enhance sepsis classification using data triangulation approach by Safiya Parvin A․, Saleena B․

“…A hybrid stack ensemble classifier was designed to test the performance of all the models. The optimized model (OPTCM) demonstrated superior performance with a 99.2% accuracy, outperforming the original CHOPS dataset's 95.3% and MENR's 96.6%. …”
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Optimizing Group Activity Recognition With Actor Relation Graphs and GCN-LSTM Architectures by M. R. Tejonidhi, C. V. Aravinda, S. V. Aruna Kumar, C. K. Madhu, A. M. Vinod

“…By integrating the ARG with a hybrid model that combines Graph Convolutional Network (GCN), Long Short-Term Memory (LSTM), and Attention mechanisms, our approach significantly enhances the extraction of spatial and relational features compared to conventional techniques. …”
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Efficient Sedimentary Facies Recognition Using Vision Transformer and Weakly Supervised Deep Multi-View Clustering by Hao Wu, Yu-Jie Dai, Xin-Yu Liu

“…Secondly, we introduce deep multi-view clustering, which integrates multi-angle features such as color, texture, and shape, improving the model’s robustness and classification accuracy. …”
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Cross modality learning of cell painting and transcriptomics data improves mechanism of action clustering and bioactivity modelling by Son V. Ha, Steffen Jaensch, Maciej M. Kańduła, Dorota Herman, Paul Czodrowski, Hugo Ceulemans

“…We show that in the absence of TX features for new compounds, using learned embeddings like those obtained from Constrastive Learning enhances performance of CP features on tasks where TX features excels but CP features does not. …”
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Identification of Fake Comments in E-Commerce Based on Triplet Convolutional Twin Network and CatBoost Model by Juanjuan Peng

“…The F1 scores on the four key features were 0.8136, 0.8267, 0.8046, and 0.7966, all of which were superior to other comparison models. …”
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GLLR-HAD: Global-local low-rank integration for hyperspectral image anomaly detection by Yu Bai, Yanling Zhang, Lili Zhang, Tan Zhao

“…However, existing methods often struggle to simultaneously model global structures and local discriminative features, which hampers their ability to detect structural anomalies and adapt to spectral variability in complex scenarios. …”
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Multi-Domain Controversial Text Detection Based on a Machine Learning and Deep Learning Stacked Ensemble by Jiadi Liu, Zhuodong Liu, Qiaoqi Li, Weihao Kong, Xiangyu Li

“…Firstly, considering the multidimensional complexity of textual features, we integrate comprehensive feature engineering, i.e., encompassing word frequency, statistical metrics, sentiment analysis, and comment tree structure features, as well as advanced feature selection methodologies, particularly lassonet, i.e., a neural network with feature sparsity, to effectively address dimensionality challenges while enhancing model interpretability and computational efficiency. …”
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Building Surface Defect Detection Based on Improved YOLOv8 by Xiaoxia Lin, Yingzhou Meng, Lin Sun, Xiaodong Yang, Chunwei Leng, Yan Li, Zhenyu Niu, Weihao Gong, Xinyue Xiao

“…Second, for bottlenecks in fine crack detection, an explicit vision center (EVC) feature fusion module is introduced. It focuses on integrating specific details and overall context, improving the model’s effectiveness. …”
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Construction of advanced persistent threat attack detection model based on provenance graph and attention mechanism by Yuancheng LI, Hao LUO, Xinyu WANG, Jiexuan YUAN

“…In response to the difficulty of existing attack detection methods in dealing with advanced persistent threat (APT) with longer durations, complex and covert attack methods, a model for APT attack detection based on attention mechanisms and provenance graphs was proposed.Firstly, provenance graphs that described system behavior based on system audit logs were constructed.Then, an optimization algorithm was designed to reduce the scale of provenance graphs without sacrificing key semantics.Afterward, a deep neural network (DNN) was utilized to convert the original attack sequence into a semantically enhanced feature vector sequence.Finally, an APT attack detection model named DAGCN was designed.An attention mechanism was applied to the traceback graph sequence.By allocating different weights to different positions in the input sequence and performing weight calculations, sequence feature information of sustained attacks could be extracted over a longer period of time, which effectively identified malicious nodes and reconstructs the attack process.The proposed model outperforms existing models in terms of recognition accuracy and other metrics.Experimental results on public APT attack datasets show that, compared with existing APT attack detection models, the accuracy of the proposed model in APT attack detection reaches 93.18%.…”
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Interpretability Study on the Fault Diagnosis Model of the Heat pipe/ Vapor Compression Composite Air Conditioning System by ZHANG Yiqi, HUANG Shuoquan, LI Xiuming, DI Yanqiang, SONG Mengjie, HAN Zongwei

“…This study develops a composite fault diagnosis model based on typical machine learning algorithms, compares the diagnostic performance of different models, and finally conducts interpretability research on the diagnostic models using the SHAP method. …”
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Improved model MASW YOLO for small target detection in UAV images based on YOLOv8 by Xianghe Meng, Fei Yuan, Dexiang Zhang

“…Abstract The present paper proposes an algorithmic model, MASW-YOLO, that improves YOLOv8n. This model aims to address the problems of small targets, missed detection, and misdetection of UAV viewpoint feature detection targets. …”
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SWRVM: Sliding Window Recurrent Vision Mamba Model for Long-Term Spatial-Temporal Prediction by Ge Peng, Yujing Zhong

“…The proposed SWRVM model combines improved embedding module and sliding window recurrent mechanisms into vision mamba, while the improved embedding module is to retain more spatial and temporal feature, the sliding window recurrent mechanism is the key structure for long-term prediction, and the vision mamba model gives effective results by global receptive field and computational efficiency. …”
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Simple model based on artificial neural network for early prediction and simulation winter rapeseed yield by Gniewko Niedbała

“…The aim of the research was to create a prediction model for winter rapeseed yield. The constructed model enabled to perform simulation on 30 June, in the current year, immediately before harvesting. …”
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Class-weighted Dempster–Shafer in dual-level fusion for multimodal fake real estate listings detection by Maifuza Mohd Amin, Nor Samsiah Sani, Mohammad Faidzul Nasrudin

“…Furthermore, a new weighting scheme is introduced to optimize Dempster–Shafer in decision fusion to help the model achieve optimal performance and as a result, our method improves the classification. …”
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Advanced Wind Speed Forecasting: A Hybrid Framework Integrating Ensemble Methods and Deep Neural Networks for Meteorological Data by Daniel Díaz-Bedoya, Mario González-Rodríguez, Oscar Gonzales-Zurita, Xavier Serrano-Guerrero, Jean-Michel Clairand

“…The framework also includes a feature selection stage to identify the most relevant predictors and a hyperparameter optimization process to improve model generalization. …”
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Hybrid classical and quantum computing for enhanced glioma tumor classification using TCGA data by Emine Akpinar, Murat Oduncuoglu

“…Additionally, VQC-1 identified IDH1, age at diagnosis, PTEN, EGFR, and ATRX, in descending order of importance, as the most informative features distinguishing LGGs from HGGs. Compared to classical machine learning models, VQC-1 demonstrated performance comparable to that of XGBoost and GBM, while outperformed KNN, SVC, DTC, and RFC in five-fold cross-validation experiments. …”
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Neural-based automatic scoring model for Chinese-English interpretation with a multi-indicator assessment by Xinguang Li, Xiaoning Li, Shuai Chen, Shanxian Ma, Fenfang Xie

“…In the feature vectorisation stage, the pre-training model Bert is utilised to vectorise the keywords and content, and a random initialisation is used for the grammar. …”
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Modeling of Must Fermentation Processes for Enabling CO₂ Rate-Based Control by Nicoleta Stroia, Alexandru Lodin

“…Digital signal processing performed on an embedded board involves acquisition, filtering, analysis, and feature extraction. …”
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