Research on Credit Default Prediction Model Based on TabNet-Stacking by Shijie Wang, Xueyong Zhang

“…With the development of financial technology, the traditional experience-based and single-network credit default prediction model can no longer meet the current needs. …”
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The Utilization of a Naïve Bayes Model for Predicting the Energy Consumption of Buildings by Behnam Sadaghat, Ali Javadzade Khiavi, Babak Naeim, Erfan Khajavi, Hadi Sadaghat, Amir Reza Taghavi Khanghah

“…To gauge the predictive efficacy of the models, an array of performance metrics, including R2, RMSE, MSE, WAPE, and the NSE, were employed for assessment. …”
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Research on Annual Runoff Prediction Based on EMD-LSTM-ANFIS Model by HU Shunqiang, CUI Dongwen

“…To improve the accuracy of runoff prediction,this paper proposes a runoff prediction model based on the combination of empirical mode decomposition (EMD),long short-term memory (LSTM) neural network,and adaptive neuro-fuzzy inference system (ANFIS),decomposes the original runoff sequence into multiple regular component sequences through EMD,and reconstructs the phase space of each component sequence by the autocorrelation function method (AFM) and the false nearest neighbor method (FNN) to determine the input and output vectors,establishes the EMD-LSTM-ANFIS prediction model,and constructs the EMD-LSTM,EMD-ANFIS,LSTM,ANFIS as comparison models,as well as predicts and compares the annual runoff of the Longtan Station in Yunnan Province by the five models.The results show that the average relative error of the EMD-LSTM-ANFIS model for the annual runoff prediction is 3.18%,which is reduced by 55.0%、65.2%、68.1%、78.4% compared with the EMD-LSTM,EMD-ANFIS,LSTM,and ANFIS models respectively,with higher prediction accuracy and stronger generalization ability.Therefore,the EMD-LSTM-ANFIS model is feasible and reliable for runoff prediction.…”
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A novel model for predicting immunotherapy response and prognosis in NSCLC patients by Ting Zang, Xiaorong Luo, Yangyu Mo, Jietao Lin, Weiguo Lu, Zhiling Li, Yingchun Zhou, Shulin Chen

“…The RF model demonstrated better predictive performance for immunotherapy responses than the Nomogram model. …”
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VOLATILITY ANALYSIS AND INFLATION PREDICTION IN PANGKALPINANG USING ARCH GARCH MODEL by Desy Yuliana Dalimunthe, Elyas Kustiawan, Khadijah -, Niken Halim, Helen Suhendra

“…This data was obtained through publications from the Central Statistics Agency of Bangka Beliltung Islands Province. The ARCH model is used to handle heteroscedasticity in data, while the GARCH model is a development of the ARCH model and serves as a generalization of the volatility model. …”
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Retracted: Advanced Computing Approach for Modeling and Prediction COVID-19 Pandemic by Applied Bionics and Biomechanics

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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.

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Analysis and Prediction of Energy Consumption Data, Using Data Mining Software by KAZOLIS Dimitrios, FANTIDIS Jacob, FOTAKIS Christos Dionyshs, TRAMANTZAS Kostantinos, TSIANTOS Vassilios

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Reliable QoE Prediction in IMVCAs Using an LMM-Based Agent by Michael Sidorov, Tamir Berger, Jonathan Sterenson, Raz Birman, Ofer Hadar

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Hybrid Machine Learning Model for Predicting the Fatigue Life of Plain Concrete Under Cyclic Compression by Lucas Rodrigues Lunardi, Paulo Guilherme Cornélio, Lisiane Pereira Prado, Caio Gorla Nogueira, Emerson Felipe Felix

“…This study introduces a hybrid machine learning model based on the stacking ensemble strategy, integrating Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANNs) to enhance prediction accuracy. …”
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Regression models for the prediction of the influence of magnesium ions on primary endothelial cell (HUVEC) proliferation and migration by Heike Helmholz, Redon Resuli, Marius Tacke, Jalil Nourisa, Sven Tomforde, Roland Aydin, Regine Willumeit-Römer, Berit Zeller-Plumhoff

“…The generated data were utilized to develop regression models in order to assess and predict the cell response on Mg exposition in a concentration range of 2–20 mM Mg in cell culture medium extract. …”
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Development and validation of a risk assessment model for predicting the failure of early medical abortions: A clinical prediction model study based on a systematic review and meta-analysis. by An-Hao Liu, Bin Xu, Xiu-Wen Li, Yue-Wen Yu, Hui-Xin Guan, Xiao-Lu Sun, Yan-Zhen Lin, Li-Li Zhang, Xian-Di Zhuo, Jia Li, Wen-Qun Chen, Wen-Feng Hu, Ming-Zhu Ye, Xiu-Min Huang, Xun Chen

“…<h4>Objective</h4>As the first model in predicting the failure of early medical abortion (EMA) was inefficient, this study aims to develop and validate a risk assessment model for predicting the failure of EMAs more accurately in a clinical setting.…”
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Assessment of binary prediction of fraudulent advertisements in ATS candidate tracking cloud systems by V. V. Ligi-Goryaev, G. A. Mankaeva, T. B. Goldvarg, S. S. Muchkaeva, E. N. Dzhakhnaeva

“…The abstract describes the construction of a binary classification model for predicting the type of job advertisement in cloud-based ATS (Applicant Tracking Systems) as either legitimate or fraudulent. …”
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Comparative analysis of the performance of mixing rules for density prediction of simple chemical mixtures by Babalola F. U., Akanji I. O., Oyegoke T.

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Eigenvector biomarker for prediction of epileptogenic zones and surgical success from interictal data by Sayantika Roy, Armelle Varillas, Armelle Varillas, Emily A. Pereira, Patrick Myers, Patrick Myers, Golnoosh Kamali, Kristin M. Gunnarsdottir, Kristin M. Gunnarsdottir, Nathan E. Crone, Adam G. Rouse, Jennifer J. Cheng, Michael J. Kinsman, Patrick Landazuri, Patrick Landazuri, Utku Uysal, Carol M. Ulloa, Nathaniel Cameron, Sara Inati, Kareem A. Zaghloul, Varina L. Boerwinkle, Sarah Wyckoff, Niravkumar Barot, Jorge González-Martínez, Joon Y. Kang, Sridevi V. Sarma, Sridevi V. Sarma

“…However, one limitation of the SSI is that it is computed heuristically from the parameters of dynamical network models (DNMs).Methods:In this work, we propose a formal method for detecting sink regions from DNMs, which has a strong foundation in linear systems theory. …”
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iPRISM: Intelligent Predicting Response to Cancer Immunotherapy through Systematic Modeling by Yinchun Su, Siyuan Li, Qian Wang, Bingyue Pan, Jiyin Lai, Guangyou Wang, Junwei Han, Qingfei Kong

“…Immunotherapy has revolutionized cancer treatment, but predicting patient response remains challenging. Herein, we present iPRISM (Intelligent Predicting Response to cancer Immunotherapy through Systematic Modeling), which is a novel network‐based model that integrates multiomics data to predict immunotherapy outcomes. …”
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Phenotype augmentation using generative AI for isocitrate dehydrogenase mutation prediction in glioma by Ha Kyung Jung, Changyong Choi, Ji Eun Park, Seo Young Park, Jae Ho Lee, Namkug Kim, Ho Sung Kim

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ML-Driven Alzheimer’s disease prediction: A deep ensemble modeling approach by Mustafa Lateef Fadhil Jumaili, Emrullah Sonuç

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Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field Concrete by Omobolaji Opafola, Abisola Olayiwola, Ositola Osifeko, Adekunle David, Ajibola Oyedejı

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Predicted distribution of Metaparasitylenchus hypothenemi (Tylenchida: Allantonematidae), parasite of the coffee berry borer by Simota-Ruiz M., Castillo A., Cisneros-Hernández J., Carmona-Castro O.

“…Four species distribution models were generated for the Neotropical region with environmental variables for sites with parasite presence data, predicting a range of possible distribution with a high probability of occurrence in southeastern Mexico and southwestern Guatemala and a low probability in areas of Central and South America. …”
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