Meta-Transformers: A Hybrid Approach for Medical Image Segmentation With U-Net and Meta-Learning by Neha Tirpude, Tausif Diwan, Meera Dhabu

“…In this paper, we propose a novel approach that combines U-Net architecture with attention mechanisms and Model-Agnostic Meta-Learning (MAML) to enhance segmentation performance, particularly in scenarios with limited annotated data. …”
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Numerical Simulation and On-Site Measurement of Dynamic Response of Flexible Marine Aquaculture Cages by Xiaoying Zhang, Fei Fu, Jun Guo, Hao Qin, Qian Sun, Zhe Hu

“…The net and mooring lines are modeled using the lumped-mass model, while the flexible floating collar system is assessed with the large-deformation FEM model, and the two models are coupled through an iterative scheme. …”
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A densely connected framework for cancer subtype classification by Yu Li, Denggao Zheng, Kaijie Sun, Chi Qin, Yuchen Duan, Qingqing Zhou, Yunxia Yin, Hongxing Kan, Jili Hu

“…Conclusion The study highlights the outstanding performance of DEGCN in heterogeneous data integration and classification accuracy, demonstrating the model’s potential in cancer subtype prediction and its application in guiding clinical treatment.…”
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Predictors of Outcome in Traumatic Brain Injury: New Insight Using Receiver Operating Curve Indices and Bayesian Network Analysis. by Zsolt Zador, Matthew Sperrin, Andrew T King

“…Bayesian networks showed the predictors that did not feature in the simplified models were associated with those that did.…”
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Machine Learning Framework for Early Detection of Chronic Kidney Disease Stages Using Optimized Estimated Glomerular Filtration Rate by Samit Kumar Ghosh, Namareq Widatalla, Ahsan H. Khandoker

“…Regression models assess predictive performance, specifically Linear Regression (LR) and Support Vector Regression (SVR). …”
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Graph-based neural networks with neural ODEs for robust speech processing in environmental and human-centric systems by Mambetov Saken, Ormanbekova Ainur, Joldasbayev Serik, Duissembayeva Laura, Mailykhanova Bulgyn

“…This paper introduces H-STGNN-ODE-DA, a novel model for voice sentiment analysis that combines multi-scale acoustic feature extraction, hierarchical graph neural networks (GNNs), Neural Ordinary Differential Equations (Neural ODEs), and domain-adversarial adaptation. …”
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Efficient lung cancer detection using computational intelligence and ensemble learning. by Richa Jain, Parminder Singh, Mohamed Abdelkader, Wadii Boulila

“…The proposed model's performance is demonstrated through comparative analysis with existing NB, J48, and SVM approaches, achieving a 98.50% accuracy rate. …”
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Emotion-RGC net: A novel approach for emotion recognition in social media using RoBERTa and Graph Neural Networks. by Jiangting Yan, Pengju Pu, Liheng Jiang

“…Despite its strong performance, we identify areas for future research, including reducing the model's reliance on large labeled datasets, improving computational efficiency, and incorporating temporal dynamics to capture emotion evolution in social networks. …”
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Comparative Analysis of Automated Machine Learning and Optimized Conventional Machine Learning for Concrete’s Uniaxial Compressive Strength Prediction by Chukwuemeka Daniel

“…Among the models assessed, the AutoGluon model stands out for its superior prediction accuracy and result interpretability. …”
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Urban Traffic State Estimation with Online Car-Hailing Data: A Dynamic Tensor-Based Bayesian Probabilistic Decomposition Approach by Wenqi Lu, Ziwei Yi, Dongyu Luo, Yikang Rui, Bin Ran, Jianqing Wu, Tao Li

“…Afterward, the Gibbs sampling method is introduced to calibrate the parameters of the DTBPD models. Finally, the real-world traffic speeds data extracted from online car-hailing trajectories are employed to validate the model performance. …”
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Analyzing risk factors and handling imbalanced data for predicting stroke risk using machine learning by Adiwijaya Adiwijaya, Nur Ghaniaviyanto Ramadhan

“…To evaluate their predictive performance, the research involves comparisons among various machine learning models. …”
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Mitigating Sinkhole Attacks in MANET Routing Protocols using Federated Learning HDBNCNN Algorithm by Sherril Sophie Maria Vincent

“…In this study, Federated Learning is utilized to improve the security and privacy through empowering nodes to train the model deprived of distribution complex data. Then, every node gathers information about the local routing and contributes towards an inclusive model, which captures behaviour of the entire network when conserving its specific privacy. …”
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YOLOv8-CBAM: a study of sheep head identification in Ujumqin sheep by Qing Qin, Qing Qin, Qing Qin, Xingyu Zhou, Xingyu Zhou, Xingyu Zhou, Jiale Gao, Jiale Gao, Jiale Gao, Zhixin Wang, A. Naer, Long Hai, Suhe Alatan, Haijun Zhang, Zhihong Liu, Zhihong Liu, Zhihong Liu

“…IntroductionThe facial coloration of sheep is not only a critical characteristic for breed and individual identification but also serves as a significant indicator for assessing genetic diversity and guiding selective breeding efforts.MethodsIn this study, 201 Ujumqin sheep were used as research objects and 1713 head image data were collected. We delineated feature points related to the facial coloration of Ujumqin sheep and successfully developed a head color recognition model (YOLOv8-CBAM) utilizing the YOLOv8 architecture in conjunction with the CBAM attention mechanism.ResultsThe model demonstrated impressive performance in recognizing four head color categories, achieving an average precision (mAP) of 97.7% and an F1 score of 0.94. …”
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Thai Morning Glory Price Forecasting Using Deep Learning by Kanokwan Waeodi, Laor Boongasame, Karanrat Thammarak

“…Preliminary experiments revealed that the LSTM model with feature selection outperformed the other models, particularly in feature selection. …”
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CGDINet: A Deep Learning-Based Salient Object Detection Algorithm by Chengyu Hu, Jianxin Guo, Hanfei Xie, Qing Zhu, Baoxi Yuan, Yujie Gao, Xiangyang Ma, Jialu Chen, Juan Tian

“…The experimental results validate that CGDINet can effectively address the issues of incomplete global feature extraction and insufficient attention to key areas, thereby significantly enhancing the performance of significance object detection.…”
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LGR-Net: A Lightweight Defect Detection Network Aimed at Elevator Guide Rail Pressure Plates by Ruizhen Gao, Meng Chen, Yue Pan, Jiaxin Zhang, Haipeng Zhang, Ziyue Zhao

“…The experimental results show that LGR-Net outperforms other YOLO-series models in terms of overall performance, achieving optimal results in terms of precision (<i>p</i> = 98.7%), recall (R = 98.9%), mAP (99.4%), and parameter count (2,412,118). …”
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Low-voltage/temperature double responsive N-isopropylacrylamide based shape-changing double network hydrogel with enhanced mechanical properties for controlled drug release and its... by Qiang Ma, Linyan Wang, Guohe Xu, Mengru Wang, Jiale Li, Zetao He

“…Bovine Serum Albumin (BSA) was served as the drug model to evaluate the drug loading/releasing performance and potential of hydrogel. …”
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A Deep Learning-Based Ensemble Framework for Robust Android Malware Detection by Sainag Nethala, Pronoy Chopra, Khaja Kamaluddin, Shahid Alam, Soltan Alharbi, Mohammad Alsaffar

“…The extracted byte data is converted into 1D vectors and reshaped into 2D grayscale images, enabling efficient feature learning through CNNs. The proposed ensemble of CNN-based models undergoes comprehensive training, validation, and evaluation, demonstrating superior performance compared to existing approaches. …”
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Research on Pork Cut and Freshness Determination Method Based on Computer Vision by Shihao Song, Qiqi Guo, Xiaosa Duan, Xiaojing Shi, Zhenyu Liu

“…In contrast, other models showed significant performance differences (<i>p</i>-value < 0.05). …”
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Virtual and Real Occlusion Processing Method of Monocular Visual Assembly Scene Based on ORB-SLAM3 by Hanzhong Xu, Chunping Chen, Qingqing Yin, Chao Ma, Feiyan Guo

“…It is then proposed to densify the sparse depth map using the double-three interpolation method, and the complete depth map of the real scene is created by combining the 3D model depth information in the process model. Finally, by comparing the depth values of each pixel point in the real and virtual scene depth maps, the virtual occlusion relationship of the assembly scene is correctly displayed. …”
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