Integration of graph neural networks and long short-term memory models for advancing heart failure prediction by Ibrahim Alrashdi, Ahmed I. Taloba

Subjects: “…Heart failure prediction…”
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DB-Net and DVR-Net: Optimized New Deep Learning Models for Efficient Cardiovascular Disease Prediction by Aymin Javed, Nadeem Javaid, Nabil Alrajeh, Muhammad Aslam

“…It is used to visualize features contribution to the output of DB-Net and DVR-Net in CVD prediction. Furthermore, 10-Fold Cross Validation is performed for evaluating the proposed models performance. …”
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Comparison of AI and NWP Models in Operational Severe Weather Forecasting: A Study on Tropical Cyclone Predictions by Yang Shi, Rong Hu, Naigeng Wu, Hualong Zhang, Xinhang Liu, Zhilin Zeng, Jing Zhu, Pucheng Han, Cong Luo, Hongyan Zhang, Jie He, Xiaoming Shi

“…Abstract Data‐driven artificial intelligence weather prediction (AIWP) models show great potential in weather forecasts, facilitating paradigm shift of prediction from a deductive to an inductive inference. …”
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Machine learning models for predicting the bearing capacity of shallow foundations: A Comparative study and sensitivity analysis by Hamid Mohammadnezhad, Seyedmohammad Eslami

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Defect Backlog Size Prediction for Open-Source Projects with the Autoregressive Moving Average and Exponential Smoothing Models by Paulina Anioła, Sushant Kumar Pandey, Miroslaw Staron, Mirosław Ochodek

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Prediction of bacteremia using routine hematological and metabolic parameters based on logistic regression and random forest models by Ting-Qiang Wang, Ying Zhuo, Chun-E Lv, Jing Shi, Ling-Hui Yao, Shi-Yan Zhang, Jinbao Shi

“…The area under the ROC curve (AUC) was 0.75 for the random forest model and 0.74 for logistic regression, with recall rates of 0.69 and 0.60, respectively.ConclusionRoutine laboratory markers integrated into machine learning models demonstrated potential for early bacteremia prediction. …”
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Stroma gene signature predicts responsiveness to chemotherapy in pancreatic ductal adenocarcinoma patient‐derived xenograft models by Alessia Anastasia, Laura Formenti, Paola Ostano, Lucia Minoli, Andrea Resovi, Lavinia Morosi, Claudia Fioravanti, Edoardo Micotti, Cristina Matteo, Eugenio Scanziani, Giovanna Chiorino, Raffaella Giavazzi, Carmen Ghilardi, Dorina Belotti

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Present‐Day Mars' Seismicity Predicted From 3‐D Thermal Evolution Models of Interior Dynamics by A.‐C. Plesa, M. Knapmeyer, M. P. Golombek, D. Breuer, M. Grott, T. Kawamura, P. Lognonné, N. Tosi, R. C. Weber

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Accuracy of the 'Benish' and 'Developed Benish' Models in Predicting the Probability of Restating Financial Statements in Iran's Economic Environment by Sasan Mehrani, Akbar Rahimipoor

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Forecasting Travel Sentiment under the Shock Effects of COVID-19 and Vaccination Using Grey Prediction Models by Cheng-Wen Lee, Peiyi Kong

“…The results showed that the grey prediction models integrated with residual modification model contributed to improving the prediction accuracy. …”
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Prediction of Soil Pollution Risk Based on Machine Learning and SHAP Interpretable Models in the Nansi Lake, China by Min Wang, Ruilin Zhang, Beibei Yan, Chengyuan Song, Yang Lv, Hengyi Zhao

“…To assess and predict the Nansi Lake soil pollution risk, we evaluate the soil environmental quality in the Nansi Lake region using machine learning techniques, combined with the SHapley Additive exPlanations (SHAP) model for interpretability. …”
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Prediction of bladder cancer prognosis and immune microenvironment assessment using machine learning and deep learning models by Weihao Nie, Yiheng Jiang, Luhan Yao, Xinqing Zhu, Abdullah Y. AL-Danakh, Wenlong Liu, Qiwei Chen, Deyong Yang

“…Therefore, understanding the tumor immune microenvironment (TIME) landscape in BCa is crucial for prognostic prediction and guiding precision therapy. In this study, we integrated 10 machine learning algorithms to develop an immune-related machine learning signature (IRMLS) and subsequently created a deep learning model to detect the IRMLS subtype based on pathological images. …”
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PREDICTING STOCK PRICE DIRECTION OF EUROZONE BANKS: CAN DEEP LEARNING TECHNIQUES OUTPERFORM TRADITIONAL MODELS? by Bogdan Ionuț ANGHEL

“…This study compares the predictive performance of Bidirectional Long Short-Term Memory (BiLSTM) and Long Short Term Memory (LSTM) with traditional models - Extreme Gradient Boosting (XGBoost) and Logistic Regression - in predicting the daily stock price direction of the ten largest Eurozone banks by market capitalization. …”
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Comparative artificial neural network models for predicting kinetic parameters of biomass pyrolysis from the biomass characteristics by Kiattikhoon Phuakpunk, Benjapon Chalermsinsuwan, Suttichai Assabumrungrat

“…Overall, these ANN models demonstrated a reliable and cost-effective alternative to conventional TGA methods for predicting the kinetic parameters of biomass pyrolysis.…”
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Analysis and Prediction of Traffic Conditions Using Machine Learning Models on Ikorodu Road in Lagos State, Nigeria by Udeme Udo Imoh, Majid Movahedi Rad

“…The analysis involved an examination of historical traffic data, specifically focusing on daily and hourly traffic volumes. The prediction involved the use of machine learning models, including decision trees, gradient boosting, and random forest classifiers. …”
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Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models by In-Hwan Kim, Jiheon Jeong, Jun-Sik Kim, Jisup Lim, Jin-Hyoung Cho, Mihee Hong, Kyung-Hwa Kang, Minji Kim, Su-Jung Kim, Yoon-Ji Kim, Sang-Jin Sung, Young Ho Kim, Sung-Hoon Lim, Seung-Hak Baek, Jae-Woo Park, Namkug Kim

“…GPOSC-Net consists of two key components: a landmark prediction model that estimates post-surgical cephalometric changes and a latent diffusion model that generates realistic synthesizes post-operative lateral cephalograms images based on predicted landmarks and segmented profile lines. …”
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Predicting Postoperative Progression of Ossification of the Posterior Longitudinal Ligament in the Cervical Spine Using Interpretable Radiomics Models by Siyuan Qin, Ruomu Qu, Ke Liu, Ruixin Yan, Weili Zhao, Jun Xu, Enlong Zhang, Feifei Zhou, Ning Lang

“…Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. …”
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STUDY OF PREDICTION AND CLASSIFICATION MODELS IN THE PROBLEMS OF DIABETES AMONG PATIENTS WITH A STROKE IN DIFFERENT LIVING CONDITIONS by Nural Huliiev, Maksym Peretiaha, Artem Khovrat, Denys Teslenko, Alexei Nazarov

“…Results: the initial conditions for choosing the best model are met by logistic regression. Conclusions: as a result of the study, the optimal model for predicting the development of the disease was selected. …”
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Artificial Intelligence Models for Bankruptcy Prediction in Agriculture: Comparing the Performance of Artificial Neural Networks and Decision Trees by Dominika Gajdosikova, Jakub Michulek

“…ANN and DT models are found to perform significantly better than traditional forecast methods. …”
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Predicting Running Vertical Ground Reaction Forces Using Neural Network Models Based on an IMU Sensor by Shangxiao Li, Jiahui Pan, Dongmei Wang, Shufang Yuan, Jin Yang, Weiya Hao

“…Using sagittal-axis acceleration data may be an ideal model with good prediction accuracy and less input data. …”
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