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Articles de revues sur le sujet « Predicting model »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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1

Siek, M., and D. P. Solomatine. "Nonlinear chaotic model for predicting storm surges." Nonlinear Processes in Geophysics 17, no. 5 (2010): 405–20. http://dx.doi.org/10.5194/npg-17-405-2010.

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Abstract. This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea,

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Yang, Ke. "Predicting Student Performance Using Artificial Neural Networks." Journal of Arts, Society, and Education Studies 6, no. 1 (2024): 45–77. http://dx.doi.org/10.69610/j.ases.20240515.

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<p class="MsoNormal" style="text-align: justify;"><span style="font-family: Times New Roman;">This paper explores machine learning approaches to predicting student performance using artificial neural networks. By employing educational data mining and predictive modeling techniques, accurate predictions of student outcomes were achieved. The results indicate that artificial neural networks exhibit high accuracy and reliability in forecasting student academic performance. Through comprehensive analysis and empirical testing, this approach significantly enhances the effectiveness of s

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Carlsson, Leo S., Mikael Vejdemo-Johansson, Gunnar Carlsson, and Pär G. Jönsson. "Fibers of Failure: Classifying Errors in Predictive Processes." Algorithms 13, no. 6 (2020): 150. http://dx.doi.org/10.3390/a13060150.

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Predictive models are used in many different fields of science and engineering and are always prone to make faulty predictions. These faulty predictions can be more or less malignant depending on the model application. We describe fibers of failure (FiFa), a method to classify failure modes of predictive processes. Our method uses Mapper, an algorithm from topological data analysis (TDA), to build a graphical model of input data stratified by prediction errors. We demonstrate two ways to use the failure mode groupings: either to produce a correction layer that adjusts predictions by similarity

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Azhagusundari, Dr B., Dr John Grasias S, G. Sudha,, G. Kanimozhi,, and Dr Radhika. "Linear Regression Model in Student Prediction System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42438.

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Logistic Regression is a widely used statistical method for predicting a categorical dependent variable based on a set of independent variables. Recognized for its adaptability and frequent application, logistic regression is particularly effective in modeling binary and multinomial outcomes. This paper provides a clear and detailed exploration of the fundamental concepts of logistic regression and demonstrates its application in predictive analysis using student data. Through this practical example, the paper highlights the method's utility in identifying relationships and making informed pre

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N, Dhivya, and Shalini M. "Predicting Stroke Risk: An Effective Stroke Prediction Model Based On Neural Networks." International Journal of Research Publication and Reviews 6, no. 6 (2025): 2433–37. https://doi.org/10.55248/gengpi.6.0625.2055.

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Zhou, Nuo-Ya, and Bing Hu. "Preoperative gastric retention in endoscopic retrograde cholangiopancreatography patients: Assessing risks and optimizing outcomes." World Journal of Gastrointestinal Surgery 16, no. 12 (2024): 3655–57. http://dx.doi.org/10.4240/wjgs.v16.i12.3655.

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This article is a comment on the article by Jia et al , aiming at establishing a predictive model to predict the occurrence of preoperative gastric retention in endoscopic retrograde cholangiopancreatography preparation. We share our perspectives on this predictive model. First, further differentiation in predicting the severity of gastric retention could enhance clinical outcomes. Second, we ponder whether this predictive model can be generalized to predictions of gastric retention before various endoscopic procedures. Third, large datasets and prospective clinical validation are needed to im

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Watson-Daniels, Jamelle, David C. Parkes, and Berk Ustun. "Predictive Multiplicity in Probabilistic Classification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10306–14. http://dx.doi.org/10.1609/aaai.v37i9.26227.

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Machine learning models are often used to inform real world risk assessment tasks: predicting consumer default risk, predicting whether a person suffers from a serious illness, or predicting a person's risk to appear in court. Given multiple models that perform almost equally well for a prediction task, to what extent do predictions vary across these models? If predictions are relatively consistent for similar models, then the standard approach of choosing the model that optimizes a penalized loss suffices. But what if predictions vary significantly for similar models? In machine learning, thi

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Aditya, K. Shastry, M. Mohan, and K. Deepthi. "Hybrid Stacked Ensemble Regression Model for Predicting Parkinson's Progression on Protein Data." CommIT (Communication and Information Technology) Journal 19, no. 1 (2025): 15–27. https://doi.org/10.21512/commit.v19i1.12079.

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Parkinson’s Disease (PD) is a progressive neurological disorder marked by both motor and nonmotor symptoms. Accurate prediction of disease progression is critical for effective patient management. The research presents a Hybrid Stacked Ensemble Regression (HSER) model for predicting PD progression using protein and peptide data measurements, leveraging the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDSUPDRS) scores. The researchers integrate three datasets: clinical data, protein data, and peptide data into a comprehensive feature-engineered d

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Asiah, Mat, Khidzir Nik Zulkarnaen, Deris Safaai, Mat Yaacob Nik Nurul Hafzan, Mohamad Mohd Saberi, and Safaai Siti Syuhaida. "A Review on Predictive Modeling Technique for Student Academic Performance Monitoring." MATEC Web of Conferences 255 (2019): 03004. http://dx.doi.org/10.1051/matecconf/201925503004.

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Despite of providing high quality of education, demand on predicting student academic performance become more critical to improve the quality and assisting students to achieve a great performance in their studies. The lack of existing an efficiency and accurate prediction model is one of the major issues. Predictive analytics can provide institution with intuitive and better decision making. The objective of this paper is to review current research activities related to academic analytics focusing on predicting student academic performance. Various methods have been proposed by previous resear

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Widhi, Oktavandi, Maria Safitri, Usman Usman, and Amalia Nur Chasanah. "Comparation Of Bankruptcy Prediction At Retail Companies In Indonesia Using Altman, Zmijewski and Springate Methods." Finance : International Journal of Management Finance 2, no. 2 (2024): 1–10. https://doi.org/10.62017/finance.v2i2.55.

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The Indonesian retail sector, a significant contributor to the nation's GDP, faces challenges due to digital transformation, shifting consumer behavior, and increased competition from e-commerce platforms, leading to potential bankruptcy risks among traditional retailers. This study aims to compare the Altman, Zmijewski, and Springate models in predicting bankruptcy of retail companies listed on the Indonesia Stock Exchange from 2019 to 2023. Using financial data from 11 retail companies, the study calculated bankruptcy predictions using the three models and performed statistical tests (Kolmog

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Na, Myung Hwan, Wanhyun Cho, Sora Kang, and Inseop Na. "Comparative Analysis of Statistical Regression Models for Prediction of Live Weight of Korean Cattle during Growth." Agriculture 13, no. 10 (2023): 1895. http://dx.doi.org/10.3390/agriculture13101895.

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Measuring weight during cattle growth is essential for determining their status and adjusting the feed amount. Cattle must be weighed on a scale, which is laborious and stressful and could hinder growth. Therefore, automatically predicting cattle weight could reduce stress on cattle and farm laborers. This study proposes a prediction system to measure the change in weight automatically during growth using three regression models, using environmental factors, feed intake, and weight during the period. The Bayesian inference and likelihood estimation principles estimate parameters that determine

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Rather, Akhter Mohiuddin. "A Hybrid Intelligent Method of Predicting Stock Returns." Advances in Artificial Neural Systems 2014 (September 7, 2014): 1–7. http://dx.doi.org/10.1155/2014/246487.

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This paper proposes a novel method for predicting stock returns by means of a hybrid intelligent model. Initially predictions are obtained by a linear model, and thereby prediction errors are collected and fed into a recurrent neural network which is actually an autoregressive moving reference neural network. Recurrent neural network results in minimized prediction errors because of nonlinear processing and also because of its configuration. These prediction errors are used to obtain final predictions by summation method as well as by multiplication method. The proposed model is thus hybrid of

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Mtimkulu, Zimele, and Mfowabo Maphosa. "Flight Delay Prediction Using Machine Learning: A Comparative Study of Ensemble Techniques." International Conference on Artificial Intelligence and its Applications 2023 (November 9, 2023): 212–18. http://dx.doi.org/10.59200/icarti.2023.030.

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Machine learning is a promising tool for predicting flight delays. Accurately predicting flight delays in aviation enhances operational efficiency and passenger contentment. Accurate predictions are critical to improving operational efficiency and passenger satisfaction. The study aims to develop a robust predictive model for domestic flights and identify key variables affecting delays. This investigation transcends the confines of traditional prediction methodologies by embracing the potency of ensemble techniques, thereby imbuing the model with the capacity to capture intricate patterns and

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Фокина, Элла, and Георгий Елизарьев. "M&A Prediction Model-Based Investment Strategies." Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438 17, no. 2 (2023): 5–26. http://dx.doi.org/10.17323/j.jcfr.2073-0438.17.2.2023.5-26.

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In this paper, we study the development of investment strategies by predicting M&A deals using a logistic model with the financial and non-financial indicators of public companies. A random sample of 1510 acquired and non-acquired companies in Germany, the United Kingdom, France, Sweden, and Russia over the period 2000-2021 was used to design an M&A logit prediction model with high predictive power. The use of interaction variables significantly improved the model’s predictive power and allowed it to obtain more than 70% of correct out-of-sample predictions. Then the model’s ability to

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Vlaović-Begović, Sanja, Stevan Tomašević, and Dajana Ercegovac. "Selection of variables in the function of improving the bankruptcy prediction model." Ekonomika 68, no. 3 (2022): 45–59. http://dx.doi.org/10.5937/ekonomika2203045v.

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The significance of early disclosure of the probability of launching a bankruptcy proceeding leads the authors to develop a model of high prediction power. In this way, the authors use different variables and statistical tools, and techniques. The impact of the economic environment and data availability limits the introduction of certain variables in bankruptcy prediction models. The paper aims to explore attitudes in existing literature regarding the selection of variables used to develop models for predicting bankruptcy, their characteristics, limitations, and impact on the power of predicti

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Krstić, Marija, and Lazar Krstić. "A logistic regression-based model for predicting heart failure mortality." Journal of Engineering Management and Competitiveness 15, no. 1 (2025): 57–64. https://doi.org/10.5937/jemc2501057k.

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Recent trends in evaluating World Wide Web data include the use of traditional data mining techniques, such as regression, clustering, and classification. This paper aims to develop a model for predicting heart failure mortality based on a publicly available online dataset containing medical records of 299 patients. Since the prediction outcome can have only one of two possible values, the binary logistic regression technique was applied. Research shows that the predictive model created using logistic regression can accurately predict patient mortality based on their clinical characteristics a

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Mudzramer A. Hayudini, Datu Ansaruddin K. Kiram, Mharcelyn M. Kiram, Abdulkamal H. Abduljalil, Nureeza J. Latorre, and Fahra B. Sahibad. "Predictive Modeling in Cardiovascular Disease: An Investigation of Random Forests." Natural Sciences Engineering and Technology Journal 5, no. 1 (2024): 393–404. https://doi.org/10.37275/nasetjournal.v5i1.60.

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Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Early detection and intervention are crucial for improving patient outcomes. Machine learning (ML) offers promising tools for CVD prediction, with random forests (RF) emerging as a robust and versatile algorithm. This study investigates the application of RF in predicting blood pressure categories, a crucial indicator of cardiovascular health, using a comprehensive dataset of patient metrics. This study investigated the application of RF in predicting blood pressure categories, a crucial indicator of cardiovascular health.

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Yujiao, Zhang, Ling Weay Ang, Shi Shaomin, and Sellappan Palaniappan. "Dropout Prediction Model for College Students in MOOCs Based on Weighted Multi-feature and SVM." Journal of Informatics and Web Engineering 2, no. 2 (2023): 29–42. http://dx.doi.org/10.33093/jiwe.2023.2.2.3.

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Due to the COVID -19 pandemic, MOOCs have become a popular form of learning for college students. However, unlike traditional face-to-face courses, MOOCs offer little faculty supervision, which may result in students being insufficiently motivated to continue learning, ultimately leading to a high dropout rate. Consequently, the problem of high dropout rates in MOOCs requires urgent attention in MOOC research. Predicting dropout rates is the first step to address this problem, and MOOCs have a large amount of behavioral data that can be used for such predictions. Most existing models for predi

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Muhammad, Yazid Abdul Mutalib, Zainol Zuraini, Nor Ellyza Nohuddin Puteri, and Fahri Abdul Rauf Ummul. "Development of MyCGPA for Early Predicting Students' Academic Performance." Malaysia Journal of Invention and Innovation 4, no. 1 (2025): 15–25. https://doi.org/10.5281/zenodo.13370554.

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One of the primary concerns in higher education is the early identification of underperforming students. To address this issue, the current study proposes the development of a system that would assist academic advisers and faculty management to identify students at risk of low academic performance at an early stage. This system utilises a prediction model based on a dataset of academic and demographic data  from the UPNM’s Computer Science students. The dataset contains information from 97 students and 21 characteristics. We developed a prediction model for Cumulative Grade Point Av

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Supitriyani, Supitriyani, Astuti Astuti, and Khairul Azwar. "Implementation of Springate, Altman, Grover and Zmijewski Models in Measuring Financial Distress." INTERNATIONAL JOURNAL OF TRENDS IN ACCOUNTING RESEARCH 3, no. 1 (2022): 001–8. http://dx.doi.org/10.54951/ijtar.v3i1.169.

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Companies that have improved company performance will have good prospects in the future. In addition, the company also needs some good strategy and planning to stay afloat in running its business. One of the strategies carried out by the company is to avoid the occurrence of a level of financial difficulties (financial distress). The goal of the study was to determine bankruptcy predictions and find out the most accurate methods for measuring bankruptcy among Springate, Altman, Grover and Zmijewski models. The data collection techniques used are documentation techniques while the data analysis

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Zhao, Wenbo, and Ling Fan. "Short-Term Load Forecasting Method for Industrial Buildings Based on Signal Decomposition and Composite Prediction Model." Sustainability 16, no. 6 (2024): 2522. http://dx.doi.org/10.3390/su16062522.

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Accurately predicting the cold load of industrial buildings is a crucial step in establishing an energy consumption management system for industrial constructions, which plays a significant role in advancing sustainable development. However, due to diverse influencing factors and the complex nonlinear patterns exhibited by cold load data in industrial buildings, predicting these loads poses significant challenges. This study proposes a hybrid prediction approach combining the Improved Snake Optimization Algorithm (ISOA), Variational Mode Decomposition (VMD), random forest (RF), and BiLSTM-atte

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Mujahidin, Irfan, and Fikri Arif Rakhman. "Application of Optimization Algorithm to Machine Learning Model for Solar Panel Output Power Prediction: A Review." Jurnal Informatika: Jurnal Pengembangan IT 9, no. 2 (2024): 180–87. http://dx.doi.org/10.30591/jpit.v9i2.7051.

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Solar panels have become a popular source of renewable energy due to their sustainability and environmental friendliness. Accurate predictions of solar panel output are crucial for various applications, such as energy system optimization, power grid management, and economic planning. Many important factors pose challenges in predicting the output of solar panels, such as weather conditions that can change at any time, geographical factors, data quality, and the duration of data collection. Machine learning (ML) models show promising performance in this prediction; there are many types of machi

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Rinella, Matthew J., and Roger L. Sheley. "A model for predicting invasive weed and grass dynamics. I. Model development." Weed Science 53, no. 5 (2005): 586–93. http://dx.doi.org/10.1614/ws-04-190r2.1.

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Invasive weed managers are presented with a complicated and ever-enlarging set of management alternatives. Identifying the optimal weed management strategy for a given set of conditions requires predicting how candidate strategies will affect plant community composition. Although field experiments have advanced our ability to predict postmanagement composition, extrapolation problems limit the prediction accuracy achieved by interpreting treatment means as predictions. Examples of extrapolation problems include nonlinear relationships between competing plants, site-to-site variation in plant p

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Yoo, Jang, Jaeho Lee, Miju Cheon, et al. "Predictive Value of 18F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer." Cancers 14, no. 8 (2022): 1987. http://dx.doi.org/10.3390/cancers14081987.

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We investigated predictions from 18F-FDG PET/CT using machine learning (ML) to assess the neoadjuvant CCRT response of patients with stage III non-small cell lung cancer (NSCLC) and compared them with predictions from conventional PET parameters and from physicians. A retrospective study was conducted of 430 patients. They underwent 18F-FDG PET/CT before initial treatment and after neoadjuvant CCRT followed by curative surgery. We analyzed texture features from segmented tumors and reviewed the pathologic response. The ML model employed a random forest and was used to classify the binary outco

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Huang, Yanyan, Huijun Wang, and Ke Fan. "Improving the Prediction of the Summer Asian–Pacific Oscillation Using the Interannual Increment Approach." Journal of Climate 27, no. 21 (2014): 8126–34. http://dx.doi.org/10.1175/jcli-d-14-00209.1.

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Abstract The summer Asian–Pacific oscillation (APO) is a dominant teleconnection pattern over the extratropical Northern Hemisphere that links the large-scale atmospheric circulation anomalies over the Asian–North Pacific Ocean sector. In this study, the direct Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) model outputs from 1960 to 2001, which are limited in predicting the interannual variability of the summer Asian upper-tropospheric temperature and the decadal variations, are applied using the interannual increment approach to improve

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Yang, Jiapeng. "Goldman Sachs’s Price Forecast Based on ARIMA and LSTM." Highlights in Business, Economics and Management 24 (January 22, 2024): 2194–201. http://dx.doi.org/10.54097/zk7c4c90.

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The prediction of stock prices is a common and crucial problem in trading. Correctly predicting future stock prices enables traders to determine the optimal time to buy and sell stocks, increasing the probability of making profits. This study focuses on predicting the closing price of Goldman Sachs. Initially, an ARIMA (4,1,6) benchmark model was established based on the AIC information criteria for time series prediction. The model was then applied to make forward predictions. Subsequently, a two-layer LSTM model was constructed. The prediction results of both models were visualized, and the

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Ravi, Pandey, Gupta Nandini, and Narendra Patil Mr. "Predicting Sulphur Price Volatility: A Multi-Model Approach for Enhanced Commodity Forecasting." Career Point International Journal of Research(CPIJR) 4, no. 3 (2025): 37–41. https://doi.org/10.5281/zenodo.15057663.

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The volatility in sulphur prices presents significant challenges for industries relying on sulphur as a key raw material, particularly in terms of budgeting, inventory management, and strategic planning. Traditional methods for predicting commodity prices, such as manual analysis, are often time-consuming and error-prone. This project proposes a comprehensive predictive system for sulphur price forecasting by leveraging advanced machine learning and time series techniques. Multiple predictive models, including Linear Regression, Random Forest, AdaBoost, XGBoost, ARIMA, Auto ARIMA, and VAR, are

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Tansitpong, Praowpan. "Probabilistic Model of Patient Classification Using Bayesian Model." International Journal of Reliable and Quality E-Healthcare 13, no. 1 (2024): 1–19. http://dx.doi.org/10.4018/ijrqeh.348579.

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The research emphasizes the effectiveness of Bayesian classification algorithms in predicting patient visits in healthcare settings. Bayesian algorithms examine past patient data to detect intricate patterns in admission dynamics, including demographic, clinical, and temporal factors. Through the use of Bayesian principles, prediction models are able to estimate the probability of certain patient demographics occurring at certain intervals, therefore assisting in the allocation of resources and the management of operations. Probabilities that have been estimated are used to make choices on sta

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Park, Jiwon, Sung Hyup Hong, Sang Hun Yeon, Byeong Mo Seo, and Kwang Ho Lee. "Predictive Model for Solar Insolation Using the Deep Learning Technique." International Journal of Energy Research 2023 (February 3, 2023): 1–17. http://dx.doi.org/10.1155/2023/3525651.

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In this study, prediction performances of a regression model and deep learning-based predictive models were comparatively analyzed for the prediction of hourly insolation in regions located at the temperate climate and microthermal climate with high precipitation. Unlike linear regression models, artificial neural networks (ANN) and long short-term memory- (LSTM-) based models achieved reliable predictive performances with CV(RMSE) of 14.0% and 15.8%, respectively. This study proposed the direction of future research by improving the performance of predicting insolation at 1 hour after the cur

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Xu, Fengyang. "Research on traffic flow prediction method based on LSTM model and PSO-LSTM model." Applied and Computational Engineering 101, no. 1 (2024): 154–63. http://dx.doi.org/10.54254/2755-2721/101/20241003.

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Abstract. With the acceleration of urbanization, the number of cars owned by residents has also significantly increased. The contradiction between the number of cars and the road carrying capacity has become increasingly severe, resulting in very serious congestion. This paper selects road data from 0:00 to 10:00 every morning in Beijing from April 2nd to April 12th, 2016, and uses the average speed of vehicles as a variable to measure road congestion. Based on these data, this article uses LSTM models to predict the speed of vehicles on two roads representing main and non main roads. Research

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Vita, Roberto, Leo Stefan Carlsson, and Peter B. Samuelsson. "Predicting the Liquid Steel End-Point Temperature during the Vacuum Tank Degassing Process Using Machine Learning Modeling." Processes 12, no. 7 (2024): 1414. http://dx.doi.org/10.3390/pr12071414.

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The present work focuses on predicting the steel melt temperature following the vacuum treatment step in a vacuum tank degasser (VTD). The primary objective is to establish a comprehensive methodology for developing and validating machine learning (ML) models within this context. Another objective is to evaluate the model by analyzing the alignment of the SHAP values with metallurgical domain expectations, thereby validating the model’s predictions from a metallurgical perspective. The proposed methodology employs a Random Forest model, incorporating a grid search with domain-informed variable

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Yao, Chun-Qiao, Hong-Bin An, Song-Ran Cao, and Elton J. Chen. "On the Generalization of an Attention-based GRU Model for Shield Attitude and Position Prediction." Journal of Physics: Conference Series 2890, no. 1 (2024): 012033. http://dx.doi.org/10.1088/1742-6596/2890/1/012033.

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Abstract Misalignment between the shield machine and tunnel design axis during tunneling can result in several issues. Shield attitude parameters are crucial for ensuring stable tunneling and reducing the risk of accidents in shield projects. This study investigates a method for predicting EPB shield attitudes using various algorithms. A reliable model for predicting shield attitudes was constructed by a Gated Recurrent Unit (GRU) and attention mechanism that can prioritize core parameters with higher weights. The Pearson correlation coefficient method was conducted to analyze input parameters

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Liang, Qi. "Research on Software Defect Prediction Model based on Deep Learning." Highlights in Science, Engineering and Technology 122 (December 15, 2024): 23–29. https://doi.org/10.54097/y0w76b47.

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As software systems grow in complexity and scale, detecting and predicting defects has become crucial for ensuring software quality and enhancing development efficiency. Traditional approaches to software defect prediction rely heavily on manual feature extraction and statistical models, which often struggle to handle intricate defect patterns and large-scale datasets. Recently, deep learning has demonstrated significant promise in software defect prediction, primarily due to its ability to automatically extract features and its strong pattern recognition capabilities. To enhance both the accu

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Wei, Chih-Chiang, and Wei-Jen Kao. "Establishing a Real-Time Prediction System for Fine Particulate Matter Concentration Using Machine-Learning Models." Atmosphere 14, no. 12 (2023): 1817. http://dx.doi.org/10.3390/atmos14121817.

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With the rapid urbanization and industrialization in Taiwan, pollutants generated from industrial processes, coal combustion, and vehicle emissions have led to severe air pollution issues. This study focuses on predicting the fine particulate matter (PM2.5) concentration. This enables individuals to be aware of their immediate surroundings in advance, reducing their exposure to high concentrations of fine particulate matter. The research area includes Keelung City and Xizhi District in New Taipei City, located in northern Taiwan. This study establishes five fine prediction models based on mach

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Tang, Li, Ping He Pan, and Yong Yi Yao. "EPAK: A Computational Intelligence Model for 2-level Prediction of Stock Indices." International Journal of Computers Communications & Control 13, no. 2 (2018): 268–79. http://dx.doi.org/10.15837/ijccc.2018.2.3187.

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This paper proposes a new computational intelligence model for predicting univariate time series, called EPAK, and a complex prediction model for stock market index synthesizing all the sector index predictions using EPAK as a kernel. The EPAK model uses a complex nonlinear feature extraction procedure integrating a forward rolling Empirical Mode Decomposition (EMD) for financial time series signal analysis and Principal Component Analysis (PCA) for dimension reduction to generate information-rich features as input to a new two-layer K-Nearest Neighbor (KNN) with Affinity Propagation (AP) clus

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Wang, Chun Sheng. "Information-Entropy-Based Integrated Model for Predicting Burn-Through Point in Lead-Zinc Sintering Process." Advanced Materials Research 396-398 (November 2011): 40–43. http://dx.doi.org/10.4028/www.scientific.net/amr.396-398.40.

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This paper presents an information-entropy-based integrated model for predicting the burn-through point (BTP) in lead-zinc sintering process. First, a fuzzy T-S prediction model for BTP was established to deal with the uncertainty of the vertical burning speed. Considering the BTP is also affected by process parameters, a neural network (NN) prediction model for BTP was then built. Finally, an integrated model for predicting the BTP was constructed by combining the above two models using the recursive entropy algorithm. The practical running results demonstrate the validity of the proposed int

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Motesharei, Arman, Cecile Batailler, Daniele De Massari, Graham Vincent, Antonia F. Chen, and Sébastien Lustig. "Predicting robotic-assisted total knee arthroplasty operating time." Bone & Joint Open 3, no. 5 (2022): 383–89. http://dx.doi.org/10.1302/2633-1462.35.bjo-2022-0014.r1.

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Aims No predictive model has been published to forecast operating time for total knee arthroplasty (TKA). The aims of this study were to design and validate a predictive model to estimate operating time for robotic-assisted TKA based on demographic data, and evaluate the added predictive power of CT scan-based predictors and their impact on the accuracy of the predictive model. Methods A retrospective study was conducted on 1,061 TKAs performed from January 2016 to December 2019 with an image-based robotic-assisted system. Demographic data included age, sex, height, and weight. The femoral and

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Prédhumeau, Manon, Lyuba Mancheva, Julie Dugdale, and Anne Spalanzani. "Agent-Based Modeling for Predicting Pedestrian Trajectories Around an Autonomous Vehicle." Journal of Artificial Intelligence Research 73 (April 19, 2022): 1385–433. http://dx.doi.org/10.1613/jair.1.13425.

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This paper addresses modeling and simulating pedestrian trajectories when interacting with an autonomous vehicle in a shared space. Most pedestrian–vehicle interaction models are not suitable for predicting individual trajectories. Data-driven models yield accurate predictions but lack generalizability to new scenarios, usually do not run in real time and produce results that are poorly explainable. Current expert models do not deal with the diversity of possible pedestrian interactions with the vehicle in a shared space and lack microscopic validation. We propose an expert pedestrian model th

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Tong, Tingting, and Zhen Li. "Predicting learning achievement using ensemble learning with result explanation." PLOS ONE 20, no. 1 (2025): e0312124. https://doi.org/10.1371/journal.pone.0312124.

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Predicting learning achievement is a crucial strategy to address high dropout rates. However, existing prediction models often exhibit biases, limiting their accuracy. Moreover, the lack of interpretability in current machine learning methods restricts their practical application in education. To overcome these challenges, this research combines the strengths of various machine learning algorithms to design a robust model that performs well across multiple metrics, and uses interpretability analysis to elucidate the prediction results. This study introduces a predictive framework for learning

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Kim, Jisun, Jaewoong Kim, Changmin Pyo, and Kwangsan Chun. "Bead Geometry Prediction Model for 9% Nickel Laser Weldment, Part 1: Global Regression Model vs. Modified Regression Model." Processes 9, no. 5 (2021): 793. http://dx.doi.org/10.3390/pr9050793.

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Due to its excellent toughness and stiffness in cryogenic conditions, 9% nickel steel is applied to LNG storage facilities, and its usage is increasing as a result of changes in environmental regulations. A study was conducted on the development of a predictive model to optimize the laser welding process of 9% nickel steel, and two prediction models were developed using one hundred data points obtained through experiments. A global regression model used as a general prediction model and a modified regression model using the p-value of the analysis of variance were developed, and their predicti

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He, Ningjing, and Jiaqi Wang. "Price-Predicting Model: Predictions for All Sailboats." Journal of Innovations in Economics & Management 5, no. 4 (2024): 127–51. http://dx.doi.org/10.69610/j.jiem.20241001.

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Sailboat pricing is a nuanced field, significantly influenced by vessel age, geographical location, and brand reputation. To address this complexity, we leveraged the XGBoost algorithm, a powerful machine learning tool, and refined it through Bayesian optimization to create a highly accurate model for predicting sailboat prices. Our analysis revealed significant regional disparities, with sailboats from Europe, the USA, and the Caribbean commanding varying market valuations.&nbsp;When applying this optimized model to the Hong Kong used sailboat market, we were able to generate forecasts wi

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Kaftanov, A. N., A. E. Andreychenko, A. D. Ermak, D. V. Gavrilov, A. V. Gusev, and R. E. Novitskiy. "A model for predicting death in adult patients within 10 years." Public Health 5, no. 2 (2025): 4–16. https://doi.org/10.21045/2782-1676-2025-5-2-4-16.

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Introduction. The identification of risk factors and the prediction of mortality from various causes are important issues in medicine. From a preventive perspective, it is crucial to identify patients at high risk of death, as early detection and treatment of diseases effectively increase life expectancy. The purpose of the study: to develop a universal model for predicting death in adult patients within 10 years and to compare the predictive ability of predicting death in a large contemporary cohort of the machine learning model (decision trees) with a Cox regression. Materials and methods. T

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Su, Te-Jen, Feng-Chun Lee, and Shih-Ming Wang. "Building Statistical Model for Predicting Risk of Diabetes." International Journal of Clinical Medicine and Bioengineering 2, no. 2 (2022): 35–40. http://dx.doi.org/10.35745/ijcmb2022v02.02.0004.

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In recent years, diabetes has become one of the most common human diseases in the world, and is even the main cause of high mortality and economic losses, while timely diagnosis and prediction provide patients with appropriate methods for prevention and treatment. By using a logistic regression model, we tried to predict type 2 diabetes. The statistical analysis was conducted with SPSS for descriptive analysis of data, a chi-square test, and logistic regression analysis to predict the risk factor of diabetes. As the result, five main predictive factors were identified: waist circumference, fam

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Carton, Quinten, Bart Merema, and Hilde Breesch. "Recommendations for model identification for MPC of an all-Air HVAC system." E3S Web of Conferences 246 (2021): 11006. http://dx.doi.org/10.1051/e3sconf/202124611006.

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Rule-based control (RBC) strategies are often unable to execute the optimal control action, which leads to unnecessary energy consumption and suboptimal comfort. Model predictive control (MPC) is a dynamic control strategy for heating, ventilation and air-conditioning (HVAC) systems that is mostly more capable of performing optimal control actions. The identification process of predictive models is an essential aspect of MPC. However, this model identification process remains time consuming due to the large variation in buildings and systems. The aim of this paper is to determine guidelines to

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Waqas, Hamid, and Rohani Md-Rus. "Predicting financial distress: Applicability of O-score model for Pakistani firms." Business and Economic Horizons 14, no. 2 (2018): 389–401. https://doi.org/10.15208/beh.2018.28.

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Predicting financial distress have significant importance in corporate finance as it serves as an effective early warning system for the related stakeholders. The study applies the most admired financial distress prediction O-score model and compares its predictive accuracy with estimated logit model. The study estimates logit model by including the profitability ratios, liquidity ratios, leverage ratios, and cash flow ratios. This study filled the gap by using the cash flow ratios to predict financial distress for Pakistani listed firms. The sample for the estimation model consists of 290 fir

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Shaik, Alfana, Kodanda Rama Jammalamadaka Sastry, Chandra Prakash Vudatha, and Tirapathi Reddy Burramukku. "Cognitive and academic-based probability models for predicting campus placements." International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1239–51. https://doi.org/10.11591/ijai.v11.i4.pp1239-1251.

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Industrial organizations select the students for placement by conducting tests based on the academic content and targeting students' cognitive levels, such as the problem-solving ability. Educational institutes are mostly dependent on the students' academic performance to judge the likelihood of Employing the students. Cognitive and academic-based models are required to accurately predict the students' employment and assess the areas of improvement required. The interrelationships must be established to achieve coherence between the models. In this paper, three predictive models ha

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Daniele, Mario, and Elisa Raoli. "Early Warning Systems for financial crises prediction in private companies: Evidence from the Italian context." FINANCIAL REPORTING, no. 2 (December 2024): 133–61. https://doi.org/10.3280/fr2024-002006.

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Purpose: This study compares models for predicting business financial crises, fo-cusing on which are most effective. In light of the new European Directive on business failure, it highlights a trade-off between predictive accuracy and timeli-ness in static models and offers an alternative approach. Design/methodology/approach: This study examines the Italian early warning system (EWS), testing static alert indicators' predictive ability on a large sample of private companies. It then proposes a dynamic version of the EWS. Findings: The results show a trade-off between predictive ability and ti

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Huang, Junzhang, and Wencai Liu. "Comparison of machine learning models for predicting stroke risk in hypertensive patients: Lasso regression model, random forest model, Boruta algorithm model, and Boruta algorithm combined with Lasso regression model." Medicine 104, no. 22 (2025): e42690. https://doi.org/10.1097/md.0000000000042690.

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The aim of this study was to compare the performance of 4 machine learning models—Lasso regression model, random forest model, Boruta algorithm model, and the Boruta algorithm combined with Lasso regression—in predicting stroke risk among hypertensive patients. The study evaluated the strengths and weaknesses of each model to provide a more clinically valuable prediction model for stroke risk. The study included 3472 hypertensive patients, of which 312 had experienced a stroke, and 3160 had not. Various health indicators were analyzed using Lasso regression, random forest, Boruta algorithm, an

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Lee, Hyungah, Woojin Cho, Jong-hyeok Park, and Jae-hoi Gu. "Predicting Liquid Natural Gas Consumption via the Multilayer Perceptron Algorithm Using Bayesian Hyperparameter Autotuning." Energies 17, no. 10 (2024): 2290. http://dx.doi.org/10.3390/en17102290.

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Reductions in energy consumption and greenhouse gas emissions are required globally. Under this background, the Multilayer Perceptron machine-learning algorithm was used to predict liquid natural gas consumption to improve energy consumption efficiency. Setting hyperparameters remains challenging in machine-learning-based prediction. Here, to improve prediction efficiency, hyperparameter autotuning via Bayesian optimization was used to identify the optimal combination of the eight key hyperparameters. The autotuned model was validated by comparing its predictive performance with that of a base

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Li, Hongmei, Tatiana Ilyina, Tammas Loughran, Aaron Spring, and Julia Pongratz. "Reconstructions and predictions of the global carbon budget with an emission-driven Earth system model." Earth System Dynamics 14, no. 1 (2023): 101–19. http://dx.doi.org/10.5194/esd-14-101-2023.

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Abstract. The global carbon budget (GCB) – including fluxes of CO2 between the atmosphere, land, and ocean and its atmospheric growth rate – show large interannual to decadal variations. Reconstructing and predicting the variable GCB is essential for tracing the fate of carbon and understanding the global carbon cycle in a changing climate. We use a novel approach to reconstruct and predict the variations in GCB in the next few years based on our decadal prediction system enhanced with an interactive carbon cycle. By assimilating physical atmospheric and oceanic data products into the Max Plan

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