Artificial neural network model for predicting water inflow into a reservoir by A. N. Shilin, M. A. Bogale, L. A. Konovalova

“…The performance of each model was evaluated using the mean square error (MSE) and efficiency coefficient (R2), which are among the most commonly used statistical methods in hydrological modeling. …”
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Climate Projections and Time Series Analysis over Roma Fiumicino Airport Using COSMO-CLM: Insights from Advanced Statistical Methods by Edoardo Bucchignani

“…By employing advanced statistical methods, such as fractal analysis, this research aims to increase an understanding of climate change and improve the model prediction capability. …”
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Pulse oximetry for the prediction of acute mountain sickness: A systematic review by Johnathan S. L. Goves, Kelsey E. Joyce, Sophie Broughton, Julian Greig, Kimberly Ashdown, Arthur R. Bradwell, Samuel J. E. Lucas

“…AMS is a clinical diagnosis, with symptom severity evaluated using the Lake Louise Score (LLS). Reliable methods of predicting which individuals will develop AMS have not been developed. …”
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A Novel Technique Using Confocal Raman Spectroscopy Coupled with PLS-DA to Identify the Types of Sugar in Three Tropical Fruits by César R. Balcázar-Zumaeta, Jorge L. Maicelo-Quintana, Geidy Salón-Llanos, Miguel Barrena, Lucas D. Muñoz-Astecker, Ilse S. Cayo-Colca, Llisela Torrejón-Valqui, Efraín M. Castro-Alayo

“…The accuracy of the PLS-DA model varied according to the pre-processing methods used. The Savitzky–Golay first derivative method produced a model with 98.69–100% and 100% precision on the training and prediction data, respectively.…”
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Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach by David Carrascal, Paula Bartolomé, Elisa Rojas, Diego Lopez-Pajares, Nicolas Manso, Javier Diaz-Fuentes

“…However, the current literature yields a lack of methods for efficient fault prediction and fast reconfiguration. …”
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Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning by Zheyue Wang, Zhenpeng Guo, Weijia Wang, Qiang Zhang, Suya Song, Yuan Xue, Zhixin Zhang, Jianming Wang

“…Methods Seven feature selection methods and twelve machine learning algorithms were utilized to analyze admission test data from TB patients, identifying predictive features and building prognostic models. …”
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Predicting Atmospheric Dispersion of Industrial Chemicals Using Machine Learning Approaches by Maria Valle, Jairo A. Cardona, Cesar Viloria-Nunez, Christian G. Quintero M.

“…Results demonstrate the effectiveness of the proposed approach, with satisfactory predictions across all evaluated risk areas. Key contributions include the development of a replicable framework adaptable to diverse industrial scenarios, applying hyperparameter tuning to optimize model accuracy, and integrating dimensionality reduction techniques to streamline data processing. …”
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Predicting the invasiveness of pulmonary adenocarcinoma using intratumoral and peritumoral radiomics features by Jingjing Hong, Liyang Yang, Jiekun Huo, Guoci Huang, Bowen Shan, Tingting Cai, Lianlian Zhang, Weikang Huang, Ge Wen

“…ObjectiveTo evaluate the predictive value of CT radiomics features within and surrounding tumors in determining the invasiveness of primary solitary nodular pulmonary adenocarcinoma.MethodsThis retrospective study analyzed 107 patients with pathologically confirmed nodular pulmonary adenocarcinoma who underwent conventional non-enhanced CT Scans in our hospital from 2019 to 2023. …”
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Predicting Employee Attrition: XAI-Powered Models for Managerial Decision-Making by İrem Tanyıldızı Baydili, Burak Tasci

“…Model performance was evaluated using accuracy, precision, recall, F1 score, and ROC AUC metrics. …”
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Cost Index Predictions for Construction Engineering Based on LSTM Neural Networks by Jiacheng Dong, Yuan Chen, Gang Guan

“…Compared with other advanced cost prediction methods, such as Support Vector Machine (SVM), this framework has advantages such as being able to capture long-distance dependent information and can provide short-term predictions of engineering cost indexes both effectively and accurately. …”
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The indicators of general clinical methods as prognostic markers of the severity of systemic scleroderma complicated by the development of pneumosclerosis. by O. V. Karaseva, V. V. Rodionova

“…Materials and methods: The study included 32 people, all women, the average age – 43.2±2.23 years. …”
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Explainable Artificial Intelligence Models for Predicting Depression Based on Polysomnographic Phenotypes by Doljinsuren Enkhbayar, Jaehoon Ko, Somin Oh, Rumana Ferdushi, Jaesoo Kim, Jaehong Key, Erdenebayar Urtnasan

“…The explainable AI models identified the important features, and their performance was evaluated using cross-validation. The study population, comprising 114 control participants and 39 individuals with depression, was stratified based on validated depression-scoring methods. …”
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Research on Wellbore Trajectory Prediction Based on a Pi-GRU Model by Hanlin Liu, Yule Hu, Zhenkun Wu

“…The results showed that even under unknown geological conditions, the Pi-GRU model could still maintain high-precision predictions. The Pi-GRU model not only outperforms existing methods in terms of prediction accuracy, with an inference delay of only 0.21 milliseconds, but also requires much less computing power for training and inference than the maximum computing power of the Jetson TX2 hardware. …”
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Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network by Xinhai Li, Yong Liang, Zhenhao Yang, Jie Li

“…This facilitates a more comprehensive understanding of the complex social interactions, leading to an enhanced trajectory prediction accuracy. Extensive comparative experiments conducted on the widely used ETH and UCY benchmark datasets demonstrate that the proposed network consistently surpasses the baseline methods across the key evaluation metrics, including the Average Displacement Error (ADE) and Final Displacement Error (FDE). …”
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Risk prediction in acute coronary syndrome depending on the types of myocard revascularization by R. V. Denina

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Drilling Rate of Penetration Prediction Based on CBT-LSTM Neural Network by Kai Bai, Siyi Jin, Zhaoshuo Zhang, Shengsheng Dai

“…This method provides a new solution for ROP prediction in real-time drilling operations, assisting drilling engineers in better planning their operations and reducing drilling cycles.…”
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Comparison of Empirical and Deep Learning Models for Solar Wind Speed Prediction by Seungwoo Ahn, Jihyeon Son, Yong-Jae Moon, Hyun-Jin Jeong

“…To validate the model’s performance, we use two evaluation methods: a statistical approach and an event-based approach. …”
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Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression by Kun Guo, Kun Guo, Bo Zhu, Lei Zha, Yuan Shao, Zhiqin Liu, Naibing Gu, Kongbo Chen

“…Machine Learning (ML) models have emerged as promising tools for predicting stroke prognosis, surpassing traditional methods in accuracy and speed.ObjectiveThe aim of this study was to develop and validate ML algorithms for predicting the 6-month prognosis of patients with Acute Cerebral Infarction, using clinical data from two medical centers in China, and to assess the feasibility of implementing Explainable ML in clinical settings.MethodsA retrospective observational cohort study was conducted involving 398 patients diagnosed with Acute Cerebral Infarction from January 2023 to February 2024. …”
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Precision Adverse Drug Reactions Prediction with Heterogeneous Graph Neural Network by Yang Gao, Xiang Zhang, Zhongquan Sun, Payal Chandak, Jiajun Bu, Haishuai Wang

“…Traditional machine learning‐based methods primarily focus on predicting potential ADRs for drugs, but they often fall short of capturing the complexity of individual demographics and the variations in ADRs experienced by different people. …”
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Development and validation of interpretable machine learning models for postoperative pneumonia prediction by Bingbing Xiang, Yiran Liu, Shulan Jiao, Wensheng Zhang, Shun Wang, Mingliang Yi

“…By evaluating the performance differences among these machine learning models, this study aims to assist clinicians in early prediction and diagnosis of POP, providing optimal interventions and treatments.MethodsRetrospective data from electronic medical records was collected for 264 patients diagnosed with postoperative pneumonia and 264 healthy control surgical patients. …”
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