Fusion‐Brain‐Net: A Novel Deep Fusion Model for Brain Tumor Classification by Yasin Kaya, Ezgisu Akat, Serdar Yıldırım

“…Integrating the pre‐trained CNN models, VGG16, ResNet50, and MobileNetV2, the model enhanced comprehensive feature extraction while mitigating overfitting issues, improving the model's performance. …”
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OWL model of multi-agent Smart-system of distance learning for people with vision disabilities by Galina A. Samigulina, Asem S. Shayakhmetova, Adlet Nyusupov

“…The benefits of using of the developed ontological model of smartsystem of distance learning for visually impaired people based on multifunctional agents are: complex approach, based on the use of various intellectual, cognitive and statistical methods; possibility of developing an individual trajectory of learning for visually impaired people including the psychophysiological features of perception information; distance access to the latest technological equipment for performing laboratory and practical works by visually impaired people in the shared laboratories in real time. …”
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Automatic Mushroom Species Classification Model for Foodborne Disease Prevention Based on Vision Transformer by Boyuan Wang

“…These results surpass previous approaches in reducing intraclass variability and generating well-separated feature embeddings. The proposed method is a promising deep learning model capable of automatically classifying mushroom species, helping wild mushroom consumers avoid eating toxic mushrooms, safeguarding food safety, and preventing public health incidents of food poisoning. …”
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Predictive modeling of longitudinal cracking in CRCP using PSO-tuned gradient boosting machines by Ali Alnaqbi, Ghazi G. Al-Khateeb, Waleed Zeiada

“…Despite the model’s exceptional predictive accuracy, computational demands and data availability may limit its practical application. …”
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ENDORSE: a prognostic model for endocrine therapy in estrogen‐receptor‐positive breast cancers by Aritro Nath, Adam L Cohen, Andrea H Bild

“…We extensively validated ENDORSE in multiple ER+ clinical trial datasets and demonstrated superior and consistent performance of the model over clinical covariates, proliferation markers, and multiple published signatures. …”
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Analysis of Gearbox Bearing Fault Diagnosis Method Based on 2D Image Transformation and 2D-RoPE Encoding by Xudong Luo, Minghui Wang, Zhijie Zhang

“…The constructed RoPE-DWTrans model implemented a unified feature extraction process, significantly improving cross-dataset adaptability and model performance. …”
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A hybrid extraction model for semantic knowledge discovery of water conservancy big data by Yanna Feng, Feng Zhang, Yongheng Zhang, Jiangang Dong, PengJu Wang

“…Experimental results demonstrate that the WIEM-DL model substantially outperforms traditional information extraction methods, setting a new benchmark for extraction performance.…”
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Dynamic convolutional model based on distribution-collaboration strategy for remote sensing scene classification by Chenjun Xu, Jingqian Shu, Zhenghan Wang, Jialin Wang

“…We chose to conduct extensive experiments on three challenging datasets and compared them with the state-of-the-art models, our model has certain advantages in classification accuracy, inference efficiency, computational performance, and number of parameters.…”
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Study on prediction model of nitrogen oxide concentration in reprocessing plant based on random forest by Xuankun Wei, Yan Xu, Xiaomeng Li, Gengxin Fan, Xuening Cheng, Tiantian Yu, Baihua Jiang

“…Feature engineering was employed to select variables, further improving the model's R2 to 0.92. …”
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A nomogram model based on clinical and 3D-EIT parameters for CTEPH diagnosis by Jian Xu, Yuhan Wang, Ying Gong, Lu Wang, Yuanlin Song, Xu Wu

“…Then, history and laboratory results were collected and 3D-EIT examination was performed. Out of 70 enrolled patients, 50 cases were used as the training set to construct the nomogram model. …”
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Antitumor effect of recombinant interferon-gamma in an experimental model of Ehrlich’s bilateral solid carcinoma by V. G. Isaeva, L. Y. Grivtsova, L. P. Zhovtun, S. M. Samborsky, N. A. Falaleeva

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A novel spatial heterogeneity decay model for depicting scale-effects in urban land by Weilin Wang, Rong Wang, Yangyang Lu, Yuanyuan Meng, Zhou Zhou

“…The model demonstrated strong performance, achieving an average goodness-of-fit (R2 = 0.944) across the 79 cities. …”
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Early diagnosis of autism across developmental stages through scalable and interpretable ensemble model by Nasirul Mumenin, Maisha Mumtaz Rahman, Mohammad Abu Yousuf, Farzan M. Noori, Md Zia Uddin

“…Comparative analysis with standard machine learning models underscores the superior performance of the proposed framework. …”
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A predictive healthcare model using machine learning and psychological factors for medication adherence by Junwu Dong, Minyi Chu, Yirou Xu

“…Five machine learning algorithms – multiple logistic regression, decision tree, adaptive boosting, random forest and support vector machine (SVM) – were utilized to identify MAB levels and assess feature importance. Among these, the random forest model achieved the highest performance, with an accuracy of 0.637, recall of 0.538, precision of 0.556, and an F1 score of 0.544. …”
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Implementing Symptom-Based Predictive Models for Early Diagnosis of Pediatric Respiratory Viral Infections by Antoni Soriano-Arandes, Cristina Andrés, Aida Perramon-Malavez, Anna Creus-Costa, Anna Gatell, Ramona Martín-Martín, Elisabet Solà-Segura, Maria Teresa Riera-Bosch, Eduard Fernández, Mireia Biosca, Ramon Capdevila, Almudena Sánchez, Isabel Soler, Maria Chiné, Lidia Sanz, Gabriela Quezada, Sandra Pérez, Dolors Canadell, Olga Salvadó, Marisa Ridao, Imma Sau, Ma Àngels Rifà, Esperança Macià, Sílvia Burgaya-Subirana, Mònica Vila, Jorgina Vila, Asunción Mejías, Andrés Antón, Pere Soler-Palacin, Clara Prats

“…Model performance was evaluated via area under the curve (AUC), sensitivity, specificity, and Shapley additive explanations (SHAP) values for feature importance. (3) Results: The model performed better for RSV (AUC: 0.81, sensitivity: 0.64, specificity: 0.77) and influenza viruses (AUC: 0.71, sensitivity: 0.70, specificity: 0.59) and effectively ruled out SARS-CoV-2 based on symptom absence, such as crackles and wheezing. …”
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A cutting-edge ensemble model for enhanced underwater image restoration and quality improvement by A. Sarala, C. Vinoth kumar

“…We evaluated the proposed model on multiple underwater datasets and compared its performance against nine state-of-the-art models, including CLAHE, FUnIE-GAN, WaterGAN, and Haze-Line Prior Model. …”
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Development of deep learning models for high-resolution exposome mapping and health impact assessment by Lan Luan, Zhu Daoyu

“…Complementing AMSEN, the Adaptive Exposure Optimization Strategy (AEOS) enhances model efficiency and accuracy through dynamic resource allocation, uncertainty-guided refinement, and domain-specific prior enforcement.ResultsThese methodologies significantly advance the capabilities of exposome research by providing a robust, adaptive, and high-resolution modeling framework.DiscussionThe experimental findings highlight the effectiveness of our approach, showcasing enhancements in exposure prediction precision, computational performance, and practical insights for public health policymaking. …”
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Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models by Mohammed Hilal Mukhsaf, Weiqin Li, Ghassan Husham Jani

“…The performance rankings, based on the adjusted R-squared (adj. …”
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3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness by Wei Zhuang, Yunhong Zhang, Yuan Wang, Kaiyang He

“…However, traditional evaluation methods suffer from one sidedness in feature extraction and high complexity in model construction, often making it difficult to fully explore the deep value of emotional data. …”
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Research on GNNs with stable learning by Wenbin Li, Wenxuan Wei, Peiyang Wang, Li Pan, Bo Yang, Yanling Xu

“…By introducing a feature sample weighting decorrelation technique in the random Fourier transform space and combining it with a baseline GNN model, a Stable-GNN model and a constrained sampling weight gradient update algorithm are designed. …”
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