BCDnet: Parallel heterogeneous eight-class classification model of breast pathology. by Qingfang He, Guang Cheng, Huimin Ju

“…In the comparison experiment, the BCDnet model performed outstandingly, and the correct recognition rate of the eight-class classification model is higher than 98%. …”
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Research on Control-Oriented Modeling for Turbocharged SI and DI Gasoline Engines by Feitie Zhang, Shoudao Huang, Xuelong Li, Fengjin Guan

“…In order to analyze system performance and develop model-based control algorithms for turbocharged spark ignition and direct injection (SIDI) gasoline engines, a control oriented mean value model is developed and validated. …”
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Predicting Software Perfection Through Advanced Models to Uncover and Prevent Defects by Tariq Shahzad, Sunawar Khan, Tehseen Mazhar, Wasim Ahmad, Khmaies Ouahada, Habib Hamam

“…The models were trained and tested on preprocessed and feature-selected data, followed by optimization through hyperparameter tuning. …”
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Deep learning for enhanced prediction of diabetic retinopathy: a comparative study on the diabetes complications data set by Weijun Gong, You Pu, Tiao Ning, Yan Zhu, Gui Mu, Jing Li

“…To enhance the interpretability of the deep learning model, SHAP analysis was employed to assess feature importance and provide insights into the key drivers of retinopathy prediction.ConclusionDeep learning models can accurately predict retinopathy in diabetic patients. …”
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Fatigue State Evaluation of Urban Railway Transit Drivers Using Psychological, Biological, and Physical Response Signals by Hao Wu, Yubo Jiao, Chaozhe Jiang, Tong Wang, Jiangbo Yu

“…The results indicate that as the length of the time window increases, the data captures more comprehensive information, leading to improved fatigue detection accuracy. Furthermore, multi-feature fusion significantly enhanced model performance. …”
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Cross-Domain Adversarial Learning for Sea Surface Temperature Super-Resolution by Wenhui Li, Jingyi Wang, Dan Song, Zhengya Sun, Zhiqiang Wei, An-An Liu

“…Finally, a random adversarial classifier is proposed to dynamically alter adversarial samples during training, enabling the model to focus on global properties and intrinsic patterns rather than specific regional characteristics, thus achieving more consistent and generalized performance across different areas. …”
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Real time weed identification with enhanced mobilevit model for mobile devices by Xiaoyan Liu, Qingru Sui, Zhihui Chen

“…The MobileViT model within our feature extraction network is engineered to concurrently learn local and global semantic information. …”
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Modeling of Merging Decision during Execution Period Based on Random Forest by Gen Li, Jianxiao Ma, Qiangru Shen

“…After the variable selection process, an RF model with 9 key feature variables is finally built. …”
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Hybrid LSTM–Attention and CNN Model for Enhanced Speech Emotion Recognition by Fazliddin Makhmudov, Alpamis Kutlimuratov, Young-Im Cho

“…The empirical outcomes highlighted the model’s superior performance, with accuracy rates reaching 99.8% for TESS and 95.7% for RAVDESS. …”
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A machine learning model for the prediction of hail-affected area in Germany by Siyu Li, Peter Knippertz, Michael Kunz, Jannik Wilhelm, Julian Quinting

“…Model performance is assessed against climatology- and persistence-based reference forecasts, and sensitivity analyses using gradient-weighted class activation mapping (Grad-CAM) are conducted to interpret the predictions. …”
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Bone scintigraphy based on deep learning model and modified growth optimizer by Omnia Magdy, Mohamed Abd Elaziz, Abdelghani Dahou, Ahmed A. Ewees, Ahmed Elgarayhi, Mohammed Sallah

“…We evaluate the performance of the proposed FS model, named GOAOA using a set of 18 UCI datasets. …”
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Research on Seismic Signal Denoising Model Based on DnCNN Network by Li Duan, Jianxian Cai, Li Wang, Yan Shi

“…The findings demonstrate that the DnCNN model not only significantly enhances the SNR and correlation coefficient of the processed seismic signals but also achieves superior noise reduction performance.…”
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Analysis of the Effectiveness of Traditional and Ensemble Machine Learning Models for Mushroom Classification by Neny Sulistianingsih, Galih Hendro Martono

“…Notably, both Random Forest and Stacking achieved flawless accuracy, reaching 100%, underscoring the high predictive capacity of these models in complex categorical scenarios. Conversely, Naïve Bayes exhibited significantly weaker performance—achieving only 59.8% accuracy—likely due to its underlying assumption of feature independence, which does not hold for this dataset. …”
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Prediction of a Panel of Programmed Cell Death Protein-1 (PD-1) Inhibitor–Sensitive Biomarkers Using Multiphase Computed Tomography Imaging Textural Features: Retrospective Cohort... by Shiqi Wang, Na Chai, Jingji Xu, Pengfei Yu, Luguang Huang, Quan Wang, Zhifeng Zhao, Bin Yang, Jiangpeng Wei, Xiangjie Wang, Gang Ji, Minwen Zheng

“…ResultsOf the 461 patients, 147 patients (31.9%) were classified into the panel-positive group. The clinical features were similar between the 2 groups. The fused model demonstrated superior performance in the test set (AUC 0.82, 95% CI 0.68‐0.95), significantly outperforming AP-only (AUC 0.61, 95% CI 0.47‐0.74) and PP-only models (AUC 0.70, 95% CI 0.49‐0.91). …”
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Construction of ubiquitination-related risk model for predicting prognosis in lung adenocarcinoma by Dawei Sun, Xiaohong Duan, Ning Li, Ou Qiao, Yingjie Hou, Zihuan Ma, Siyao Liu, Yanhua Gong, Zichuan Liu

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Balancing Depth for Robustness: A Study on Reincarnating Reinforcement Learning Models by Gang Li, Zhuxiao Wang, Shaowei He, Xiyuan Chen, Yunlei Xie, Jiajun Hu, Kehe Wu, Jingping Jia

“…This paper investigates the impact of adaptive network depth selection on the robustness and performance of Regenerative Reinforcement Learning (RRL) models. …”
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Semantic Co-Occurrence and Relationship Modeling for Remote Sensing Image Segmentation by Yinxing Zhang, Haochen Song, Qingwang Wang, Pengcheng Jin, Tao Shen

“…By embedding SCRM into both classic and state-of-the-art segmentation models, our method leverages contextual relationships to improve segmentation performance. …”
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Application of CT Radiomics in Predicting Differentiation Level of Lung Adenocarcinoma by Shuai ZHANG, Peng HAN, Suya ZHANG, Dingli YE, Zhicheng HUANG

“…ResultsThe poorly differentiation group consisted of 175 cases, while the moderate-to-high differentiation group had 332 cases. The XGBoost model demonstrated the best performance, with the AUC, accuracy, specificity, and sensitivity of this model on the validation set being 0.878, 0.829, 0.667, and 0.727, respectively. …”
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Cryptocurrency Forecasting Using Deep Learning Models: A Comparative Analysis by Rachid Bourday, Issam Aatouchi, Mounir Ait Kerroum, Ali Zaaouat

“…Therefore, we trained these models using historical Bitcoin data from 2016 to 2023 and evaluated their performance on a test dataset. …”
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Transmuted exponential-compound Weibull distribution for modelling of positively skewed data by Nnaemeka Martin Eze, Waheed Babatunde Yahya

“…The new model is characterized by a flexible structure ideal for analyzing positive data and featuring a hazard rate function that has bathtub shaped which makes it to offer more flexibility to solve the problem of elongation and asymmetry than the competing distributions. …”
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