Machine learning-based identification of key factors and spatial heterogeneity analysis of urban flooding: a case study of the central urban area of Ordos by Yu Qin, Yingdong Yu, Jiahong Liu, Ruifen Liu

“…The results show: (1) Model performance comparison: All three models have high accuracy, with XGBoost performing well in overall classification (OA = 0.96) and CatBoost performing well in distinguishing flood/non-flood samples (AUC = 0.85). (2) Multi-model adaptability assessment: The proposed “model-factor-space” framework highlights the sensitivity of XGBoost to urbanization indicators, the ability of CatBoost to capture natural geographical elements, and the efficiency of LightGBM in analyzing terrain thresholds. (3) Dynamic thresholds and synergies: Impervious surface density (ISD) is the most critical factor, and when ISD > 0.2, the risk of flooding will continue to increase by 60%. …”
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COMPARATIVE REVIEW OF METHODOLOGIES FOR ESTIMATING THE COST OF ADVERSE DRUG REACTIONS IN THE RUSSIAN FEDERATION AND BRAZIL by G. I. Syraeva, A. S. Kolbin, A. V. Matveev, V. S. Panezhina

“…The models used in the Russian Federation (“the decision tree”, classification of diseases by clinical groups, Markov model) do not take into account the time factor, therefore, when planning the analysis of potential costs for adverse reactions, it is necessary to reinforce the methods with such tools as QALY, YLL, and YLD.…”
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Optimized Ensemble Methods for Classifying Imbalanced Water Quality Index Data by Zaharaddeen Karami Lawal, Ali Aldrees, Hayati Yassin, Salisu Dan'azumi, Sujay Raghavendra Naganna, Sani I. Abba, Saad Sh. Sammen

“…In all experiments, XGBoost performed best individually, while SVM was worst. The ensemble models outperformed individuals, with the GridSearchCV ensemble achieving 97.3% accuracy, an improvement exceeding the existing literature’s models by 2.3%. …”
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N V.V. THE METHOD OF ESTIMATION OF THE ECOSYSTEMS STABILITY LEVEL BY USE OF AUTOMATIC LEARNING by V. N. Exzaryan, Rukavitsi

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Trade-offs between machine learning and deep learning for mental illness detection on social media by Zhanyi Ding, Zhongyan Wang, Yeyubei Zhang, Yuchen Cao, Yunchong Liu, Xiaorui Shen, Yexin Tian, Jianglai Dai

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CropsDisNet: An AI-Based Platform for Disease Detection and Advancing On-Farm Privacy Solutions by Mohammad Badhruddouza Khan, Salwa Tamkin, Jinat Ara, Mobashwer Alam, Hanif Bhuiyan

“…To classify corn, potato, and wheat leaf diseases, we used three representative CNN models for image classification (VGG16, Inception Resnet V2, Inception V3) along with our custom model, and the classification accuracy for these three different crops varied from 92.09% to 98.29%. …”
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Method selection affects the estimates of residency and site fidelity in bottlenose dolphins: testing sensitivity and performance of different methods using mark-resight data by Israel Huesca-Domínguez, Eduardo Morteo, Luis Gerardo Abarca-Arenas, Brian C. Balmer, Tara M. Cox, Christian A. Delfín-Alfonso, Isabel C. Hernández-Candelario

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Development and Application of a Multi-scale Framework for Evaluating Floodplain Restoration Suitability Based on HEC-RAS by Shuoxing LI, Nanxi WANG, Yan ZHA

“…By focusing on the integration of hydrological, ecological, and socio-economic factors, this research seeks to provide a scientifically robust method for prioritizing floodplain restoration efforts.MethodsThis research proposes a multi-dimensional floodplain restoration suitability evaluation framework, integrating Geographic Information Systems (GIS) and the HEC-RAS (Hydrologic Engineering Center’s River Analysis System) hydrodynamic model. The framework incorporates multi-source data, including digital elevation models (DEM), land use classifications, vegetation indices (NDVI), soil types, and socio-economic factors, to evaluate the restoration potential of floodplains. …”
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Large Language Models and Artificial Neural Networks for Assessing 1-Year Mortality in Patients With Myocardial Infarction: Analysis From the Medical Information Mart for Intensive... by Boqun Shi, Liangguo Chen, Shuo Pang, Yue Wang, Shen Wang, Fadong Li, Wenxin Zhao, Pengrong Guo, Leli Zhang, Chu Fan, Yi Zou, Xiaofan Wu

“…The predictive performance of the 3 models was assessed and compared using the Harrell C-statistic (C-index), the area under the receiver operating characteristic curve (AUROC), calibration plots, Kaplan-Meier curves, and decision curve analysis. …”
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Risk Evaluation for Human Factors of Flight Dispatcher Based on the Hesitant Fuzzy TOPSIS-DEMATEL-ISM Approach: A Case Study in Sichuan Airlines by Jing-Han Zeng, Jing-Yang Huang, Qing-Wei Zhong, Dai-Wu Zhu, Yi Dai

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Economic burden of cardiorespiratory hospitalizations associated with respiratory syncytial virus among United States adults in 2017–2019 by Deesha A Patel, Zachary A Marcum, Aisara Chansakul, Astra Toyip, Katherine Nerney, Catherine A Panozzo, Samantha St Laurent, Darshan Mehta, Parinaz Ghaswalla

“…Number and cost for International Classification of Diseases (ICD)-coded RSV hospitalizations were quantified from MarketScan. …”
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NeuroFuzzXAI: A Hybrid Artificial Intelligence Framework for Epileptic Seizure Detection by Flavia COSTI, Emanuel COVACI, Ovidiu ROMAN, Darian ONCHIS

“…Results: Compared to a standard deep learning model utilizing all features, our hybrid approach improved classification performance by 12%. …”
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COVID-19 risk stratification among older adults: a machine learning approach to identify personal and health-related risk factors by Arezoo Abasi, Seyed Abbas Motevalian, Haleh Ayatollahi

“…Subsequently, several machine learning models—including CatBoost, XGBoost, Random Forest, Generalized Linear Model (GLM), Decision Tree, H2O Deep Neural Network (DNN), and L2 SVM—were used to predict risk classifications. …”
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An Analysis of Factors Influencing Green Supply Chain Drivers in the Indian Real Estate Sector Using the ISM-DEMATEL Approach by Koul Pawan, Roy Ghatak Ranjit

“…Employing the Interpretive Structural Modeling–Dynamic Multi-Attribute Decision-Making Trial and Evaluation Laboratory (ISM–DEMATEL) approach, the hierarchical and contextual relationships among these factors are systematically examined. …”
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T-cell receptor dynamics in digestive system cancers: a multi-layer machine learning approach for tumor diagnosis and staging by Changjin Yuan, Bin Wang, Hong Wang, Fang Wang, Xiangze Li, Ya’nan Zhen

“…Multi-dimensional machine learning models demonstrated exceptional diagnostic performance across all classification tasks. …”
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Unveiling Urban Flood Vulnerability: A Machine Learning Approach for Mapping High Risk Zones in Tetouan City, Northern Morocco by Oussama Mekkaoui, Moad Morarech, Tarik Bouramtane, Laurent Barbiero, Maryem Hamidi, Hamza Akka, Rania Prya Muthusamy Rengasamy

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DS4NN: Direct training of deep spiking neural networks with single spike-based temporal coding by Maryam Mirsadeghi, Majid Shalchian, Saeed Reza Kheradpisheh

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Dynamic Approach to Update Utility and Choice by Emerging Technologies to Reduce Risk in Urban Road Transportation Systems by Francesco Russo, Antonio Comi, Giovanna Chilà

“…The contribution of emerging ICTs to actualization is formally introduced into the models. Intelligent technologies make it possible to improve user decisions, reducing exposure and therefore risk. …”
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Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies by Md. Kabin Hasan Kanchon, Mahir Sadman, Kaniz Fatema Nabila, Ramisa Tarannum, Riasat Khan

“…Next, the text content of the electronic documents is modified by employing different natural language processing (NLP) techniques, including named entity recognition of spaCy, knowledge graph, generative pre-trained transformer 3 (GPT-3), and text-to-text transfer transformer (T5) model, to accommodate diverse learning styles. …”
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Transparent brain tumor detection using DenseNet169 and LIME by Lincy Annet Abraham, Gopinath Palanisamy, Goutham Veerapu

“…The model was trained and evaluated on the publicly available Brain Tumor MRI Dataset containing 2,870 images spanning three tumor types. …”
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