Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait by Anam Naz, Hikmat Ullah Khan, Tariq Alsahfi, Mousa Alhajlah, Bader Alshemaimri, Ali Daud

“…In this research work, we aim to explore diverse natural language processing (NLP) based features and apply state of the art deep learning algorithms for openness trait prediction. Using standard Myers-Briggs Type Indicator (MBTI) dataset, we propose the use of the latest deep features of sentence embeddings which captures contextual semantics of the content to be used with deep learning models. …”
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HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION by Syaiful Anam, M. Rafael Andika Putra, Zuraidah Fitriah, Indah Yanti, Noor Hidayat, Dwi Mifta Mahanani

“…The number of claims plays an important role the profit achievement of health insurance companies. Prediction of the number of claims could give the significant implications in the profit margins generated by the health insurance company. …”
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Predicting anemia management in dialysis patients using open-source machine learning libraries by Takahiro Inoue, Norio Hanafusa, Yuki Kawaguchi, Ken Tsuchiya

“…Performance metrics were compared across models, including XGBoost and LightGBM, to identify the most accurate algorithms. Results LightGBM and XGBoost outperformed logistic regression in predicting ESA and iron dosage changes, achieving high accuracy (e.g., area under the curve (AUC) = 0.86 for iron dosing). …”
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Robust EEG Characteristics for Predicting Neurological Recovery from Coma After Cardiac Arrest by Meitong Zhu, Meng Xu, Meng Gao, Rui Yu, Guangyu Bin

“…By integrating machine learning (ML) algorithms, such as Gradient Boosting Models and Support Vector Machines, with SHAP-based feature visualization, robust screening methods were applied to ensure the reliability of predictions. …”
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A portable retina fundus photos dataset for clinical, demographic, and diabetic retinopathy prediction by Chenwei Wu, David Restrepo, Luis Filipe Nakayama, Lucas Zago Ribeiro, Zitao Shuai, Nathan Santos Barboza, Maria Luiza Vieira Sousa, Raul Dias Fitterman, Alexandre Durao Alves Pereira, Caio Vinicius Saito Regatieri, Jose Augusto Stuchi, Fernando Korn Malerbi, Rafael E. Andrade

“…To validate the utility of mBRSET, state-of-the-art deep models, including ConvNeXt V2, Dino V2, and SwinV2, were trained for benchmarking, achieving high accuracy in clinical tasks diagnosing diabetic retinopathy, and macular edema; and in fairness tasks predicting education and insurance status. The mBRSET dataset serves as a resource for developing AI algorithms and investigating real-world applications, enhancing ophthalmological care in resource-constrained environments.…”
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Enhancing phase change thermal energy storage material properties prediction with digital technologies by Minghao Yu, Jing Liu, Cheng Chen, Mingyue Li

“…To address these limitations, the integration of digital technologies, such as computational modeling and machine learning (ML), has become increasingly important.MethodsThis paper proposes a hybrid multiscale modeling framework that integrates molecular dynamics (MD) simulations, finite element methods (FEM) from continuum mechanics, and supervised ML algorithms—including deep neural networks and gradient boosting regressors—to enable accurate and efficient prediction of material properties across scales. …”
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Feature Selection for Hypertension Risk Prediction Using XGBoost on Single Nucleotide Polymorphism Data by Lailil Muflikhah, Tirana Noor Fatyanosa, Nashi Widodo, Rizal Setya Perdana, Solimun, Hana Ratnawati

“…This study provides compelling evidence that the XGBoost feature selection method outperforms other representative feature selection methods, such as genetic algorithms, analysis of variance, chi-square, and principal component analysis, in predicting hypertension risk, demonstrating its effectiveness. …”
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Stroke risk prediction: a deep learning approach for identifying high-risk patients by Afeez A. Soladoye, Kazeem M. Olagunju, Sunday A. Ajagbe, Ibrahim A. Adeyanju, Precious I. Ogie, Pragasen Mudali

“…The preprocessed dataset was used by GRU for prediction. The system gave average accuracy, Area Under Curve (AUC) and prediction time of 80.42%, 0.8940 and 0.678 s respectively. …”
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Improving Cardiovascular Disease Prediction through Stratified Machine Learning Models and Combined Datasets by Tara Yousif Mawlood, Alla Ahmad Hassan, Rebwar Khalid Muhammed, Aso M. Aladdin, Tarik A. Rashid, Bryar A. Hassan

“…This study introduces a robust machine learning (ML) framework for predicting CVD risk by integrating two large, feature-identical datasets containing clinical and biological indicators along with patient history. …”
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An ensemble time-embedded transformer model for traffic conflict prediction at RRFB pedestrian crossings by Md Jamil Ahsan, Mohamed Abdel-Aty, B M Tazbiul Hassan Anik, Zubayer Islam

“…Fifty-two hours of video data were collected using portable CCTV cameras and analyzed using computer vision algorithms. A bounding box system was employed to predict vehicle conflict points and collision pairs. …”
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Deep learning-based time series prediction in multispectral and hyperspectral imaging for cancer detection by Lijun Hao, Changmin Wang, Jinshan Che, Mingming Sun, Yuhong Wang

“…Deep learning has recently been introduced to address these limitations, yet existing models often lack robust feature extraction, generalization capability, and effective domain adaptation strategies.MethodsIn this study, we propose a novel deep learning-based time series prediction framework for multispectral and hyperspectral medical imaging analysis. …”
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Ensemble Learning-Based Wine Quality Prediction Using Optimized Feature Selection and XGBoost by Sonam Tyagi, Ishwari Singh Rajput, Bhawnesh Kumar, Harendra Singh Negi

“…The study shows how feature selection improves wine quality prediction in different machine learning algorithms. …”
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A framework for predicting zoonotic hosts using pseudo-absences: the case of Echinococcus multilocularis by Andrea Simoncini, Dimitri Giunchi, Marta Marcucci, Alessandro Massolo

“…The predicted richness of intermediate hosts peaked in Central-Eastern Europe, Western North America and Central Asia, while the ratio of predicted hosts to total rodent richness was highest in the northern latitudes and the Tibetan Plateau. …”
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Predicting Nottingham grade in breast cancer digital pathology using a foundation model by Jun Seo Kim, Jeong Hoon Lee, Yousung Yeon, Doyeon An, Seok Jun Kim, Myung-Giun Noh, Suehyun Lee

“…Abstract Background The Nottingham histologic grade is crucial for assessing severity and predicting prognosis in breast cancer, a prevalent cancer worldwide. …”
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Research on predicting the risk level of coal mine roof accident based on machine learning by Zhao-Yang Guan, Jin-Ling Xie, Shen-Kuang Wu, Chao Liang

“…Through comparison of model performance evaluation metrics, the Random Forest integration algorithm is introduced to improve the evaluation and prediction of the model, and the prediction accuracy jumps to 0.94. …”
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Predicting the time to get back to work using statistical models and machine learning approaches by George Bouliotis, M. Underwood, R. Froud

“…Objectives To compare model performance and predictive accuracy of classic regressions and machine learning approaches using data from the Inspiring Families programme. …”
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Utilizing Machine Learning Techniques for Cancer Prediction and Classification based on Gene Expression Data by Mariwan Mahmood Hama Aziz, Sozan Abdullah Mahmood

“…Lately, several studies have delved into cancer classification by leveraging data mining techniques, machine learning algorithms, and statistical methods to thoroughly analyze high-dimensional datasets. …”
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An interpretable disruption predictor on EAST using improved XGBoost and SHAP by D.M. Liu, X.L. Zhu, Y.S. Jiang, S. Wang, S.B. Shu, B. Shen, B.H. Guo, L.C. Liu

“…Based on the physical characteristics of the disruption, 2094 disruption shots and 4858 non-disruption shots from 2022 to 2024 were screened as training shots, and then the disruption prediction model was trained using the eXtreme Gradient Boosting (XGBoost) algorithm from training samples consisting of 16 diagnostic signals, such as plasma current, density, and radiation. …”
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Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study by Yubing Wang, Chao Qu, Jiange Zeng, Yumin Jiang, Ruitao Sun, Changlei Li, Jian Li, Chengzhi Xing, Bin Tan, Kui Liu, Qing Liu, Dianpeng Zhao, Jingyu Cao, Weiyu Hu

“…Using 12 machine learning algorithms, 110 combination predictive models were constructed. …”
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PREDICTION INTERVALS IN MACHINE LEARNING: RESIDUAL BOOTSTRAP AND QUANTILE REGRESSION FOR CASH FLOW ANALYSIS by Wa Ode Rahmalia Safitri, Farit Mochamad Afendi, Budi Susetyo

“…This study implements multivariate time series forecasting using gradient boosting algorithms (XGBoost, CatBoost, and LightGBM) to predict cash flow patterns in banking transactions, focusing on constructing reliable prediction intervals. …”
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