Predictive analysis of Somalia’s economic indicators using advanced machine learning models by Bashir Mohamed Osman, Abdillahi Mohamoud Sheikh Muse

“…This paper evaluates the performance of three machine learning models—Random Forest Regression (RFR), XGBoost, and Prophet—in predicting Somalia's GDP. …”
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DM-YOLO: improved YOLOv9 model for tomato leaf disease detection by Abudukelimu Abulizi, Junxiang Ye, Halidanmu Abudukelimu, Wenqiang Guo

“…The experimental results show that the precision (P) of this model increased by 2.2%, 1.7%, 2.3%, 2%, and 2.1%compared with those of multiple mainstream improved models, respectively. …”
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Application of Fuzzy-RBF-CNN Ensemble Model for Short-Term Load Forecasting by Mohini Yadav, Majid Jamil, Mohammad Rizwan, Richa Kapoor

“…To demonstrate the performance of RBF-CNN, SVMs replace the RBF-CNN regressor, and this model is identified as an ML-SVM having 3 layers.…”
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An Improved Empirical Fuel Cell Polarization Curve Model Based on Review Analysis by Dong Hao, Jianping Shen, Yongping Hou, Yi Zhou, Hong Wang

“…Based on a review analysis of empirical fuel cell polarization curve models in the literature, an improved model that can predict fuel cell performance with only measured current-voltage data is developed. …”
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Data-Driven Neural Differential Equation Model and Stochastic Dynamics for Glioma Prediction by Mohammed Salman, Sanjay Kumar Mohanty

“…We combine the advantages of mathematical models with deep learning features to provide results with precise solutions and high-performance prediction. …”
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Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review by Alexandru Catalin Motofelea, Adelina Mihaescu, Nicu Olariu, Luciana Marc, Lazar Chisavu, Gheorghe Nicusor Pop, Andreea Crintea, Ana Maria Cristina Jura, Viviana Mihaela Ivan, Adrian Apostol, Adalbert Schiller

“…However, the observed heterogeneity among studies indicates a need for further research to refine model performance and standardize predictive features.…”
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Evaluation of eco-driving performance of electric vehicles using driving behavior-enabled graph spectrums: A naturalistic driving study in China by Hui Zhang, Yiyue Luo, Naikan Ding, Toshiyuki Yamamoto, Chenming Fan, Chunhui Yang, Wei Xu, Chaozhong Wu

“…Specifically, it’s vital important to understand the ecological performance of electric vehicles and its association with driving behaviors under varying road and environmental conditions. …”
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Prediction model for nonlinear variation of bolt preload with tightening torque based on mechanism and data fusion by Yueqi Qiao, Bing Zhao, Dingshan Deng, Weijin Ouyang

“…Secondly, sensitivity evaluation indicator is established to conduct parameter sensitivity analysis, and the fusion method of “mechanism model guiding data model to perform feature selection” is determined. …”
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Construction of a disease risk prediction model for postherpetic pruritus by machine learning by Zheng Lin, Yuan Dou, Ru-yi Ju, Ping Lin, Yi Cao

“…Fifty indicators of these participants were collected as potential input features for the model. Features associated with PHI were identified for inclusion in the model using the least absolute shrinkage selection operator (LASSO). …”
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Exploring predictors of insomnia severity in shift workers using machine learning model by Hyewon Yeo, Hyeyeon Jang, Nambeom Kim, Sehyun Jeon, Yunjee Hwang, Chang-Ki Kang, Seog Ju Kim, Seog Ju Kim

“…Our findings align with the traditional insomnia model while also reflecting the distinctive features of shift work such as workplace conditions. …”
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A comparative analysis of large language models on clinical questions for autoimmune diseases by Jing Chen, Juntao Ma, Jie Yu, Weiming Zhang, Yijia Zhu, Jiawei Feng, Linyu Geng, Xianchi Dong, Huayong Zhang, Yuxin Chen, Mingzhe Ning

“…To explore the potential of Large Language Models (LLMs) in providing medical services to patients and assisting physicians in clinical practice, our study evaluated the performance in delivering clinical questions related to autoimmune diseases.Methods46 questions related to autoimmune diseases were input into ChatGPT 3.5, ChatGPT 4.0, and Gemini. …”
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Attention-Based Hydrogen Refueling Imputation Model for Efficient Hydrogen Refueling Stations by Keunsoo Ko, Changgyun Kim

“…It determines the primary features, which improves the predictive performance and helps adapt to new conditions. …”
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A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification by A. M. Mutawa, Sai Sruthi

“…We further analyze feature significance and model behavior using the Local Interpretable Model-Agnostic Explanations (LIME) interpretability technique. …”
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Vessel Trajectory Prediction Method Based on the Time Series Data Fusion Model by Xinyun WU, Jiafei CHEN, Caiquan XIONG, Donghua LIU, Xiang WAN, Zexi CHEN

“…Deep learning techniques have been widely applied to vessel trajectory prediction tasks due to their advantages in fine-grained feature learning and time series modelling. However, most deep learning-based methods use a unified approach for modelling AIS data, ignoring the diversity of AIS data and the impact of noise on prediction performance due to environmental factors. …”
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Spoof speech classification using deep speaker embeddings and machine learning models by Mohammed Hamzah Alsalihi, Dávid Sztahó

“…We leverage four state-of-the-art speaker embedding models: X-vector, Emphasized channel attention, propagation and aggregation in time delay neural network (ECAPA-TDNN), Residual Network-Time Delay Neural Network (ResNet-TDNN), and WavLM, used in both pre-trained and fine-tuned forms, to extract speaker-discriminative features from speech signals. …”
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Comparative Study of Hybrid Deep Learning Models for Kannada Sign Language Recognition by Gurusiddappa Hugar, Ramesh M. Kagalkar, Abhijit Das

“…By hierarchically integrating CNN-extracted spatial features, HKP-derived structural priors, LSTM-processed temporal dynamics, and Transformer-modeled long-range dependencies, this work establishes a new benchmark for KSL recognition while providing a scalable solution for real-world healthcare and assistive technology applications.…”
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Large language model powered knowledge graph construction for mental health exploration by Shan Gao, Kaixian Yu, Yue Yang, Sheng Yu, Chenglong Shi, Xueqin Wang, Niansheng Tang, Hongtu Zhu

“…Applied to predictive modeling in the UK Biobank, MDKG-enhanced representations yielded significant gains in predictive performance across multiple mental disorders. …”
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Evaluation of Machine Learning and Ensemble Learning Models for Classification Using Delivery Data by İrem Karakaya

“…Purpose: This study aims to evaluate the performance of various machine learning and ensemble learning models in classifying delivery times using Amazon delivery data. …”
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CKRT coagulation risk prediction and nursing feedback model based on intelligent algorithms by Xianrong Xu, Mou Chen, Lvjing Chen, Kaixing Huang, Shiqi Cao, Wenwen Gao, Kang Liu, Buyun Wu, Huijuan Mao

“…Extensive numerical experiments were conducted to optimize model parameters and evaluate performance, ensuring high accuracy, stability, and superior AUC values for reliable predictions. …”
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Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients by Olawande Daramola, Tatenda Duncan Kavu, Maritha J. Kotze, Jeanine L. Marnewick, Oluwafemi A. Sarumi, Boniface Kabaso, Thomas Moser, Karl Stroetmann, Isaac Fwemba, Fisayo Daramola, Martha Nyirenda, Susan J. van Rensburg, Peter S. Nyasulu

“…It also offers insight into the combined effect of CV, PCA, and SMOTE on the performance of ML models for COVID-19 mortality risk prediction, which has been little explored.…”
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