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Journal articles on the topic 'Dynaic prediction'

Author: Grafiati

Published: 25 January 2025

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1

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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Lin, Huan, Weiye Yu, and Zhan Lian. "Influence of Ocean Current Features on the Performance of Machine Learning and Dynamic Tracking Methods in Predicting Marine Drifter Trajectories." Journal of Marine Science and Engineering 12, no. 11 (2024): 1933. http://dx.doi.org/10.3390/jmse12111933.

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Accurately and rapidly predicting marine drifter trajectories under conditions of information scarcity is critical for addressing maritime emergencies and conducting marine surveys with resource-limited unmanned vessels. Machine learning-based tracking methods, such as Long Short-Term Memory networks (LSTM), offer a promising approach for trajectory prediction in such scenarios. This study combines satellite observations and idealized simulations to compare the predictive performance of LSTM with a resource-dependent dynamic tracking method (DT). The results indicate that when driven solely by

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Stoodley, Catherine J., and Peter T. Tsai. "Adaptive Prediction for Social Contexts: The Cerebellar Contribution to Typical and Atypical Social Behaviors." Annual Review of Neuroscience 44, no. 1 (2021): 475–93. http://dx.doi.org/10.1146/annurev-neuro-100120-092143.

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Social interactions involve processes ranging from face recognition to understanding others’ intentions. To guide appropriate behavior in a given context, social interactions rely on accurately predicting the outcomes of one's actions and the thoughts of others. Because social interactions are inherently dynamic, these predictions must be continuously adapted. The neural correlates of social processing have largely focused on emotion, mentalizing, and reward networks, without integration of systems involved in prediction. The cerebellum forms predictive models to calibrate movements and adapt

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Oh, Cheol, Stephen G. Ritchie, and Jun-Seok Oh. "Exploring the Relationship between Data Aggregation and Predictability to Provide Better Predictive Traffic Information." Transportation Research Record: Journal of the Transportation Research Board 1935, no. 1 (2005): 28–36. http://dx.doi.org/10.1177/0361198105193500104.

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Providing reliable predictive traffic information is a crucial element for successful operation of intelligent transportation systems. However, there are difficulties in providing accurate predictions mainly because of limitations in processing data associated with existing traffic surveillance systems and the lack of suitable prediction techniques. This study examines different aggregation intervals to characterize various levels of traffic dynamic representations and to investigate their effects on prediction accuracy. The relationship between data aggregation and predictability is explored

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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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Prasanna, Christopher, Jonathan Realmuto, Anthony Anderson, Eric Rombokas, and Glenn Klute. "Using Deep Learning Models to Predict Prosthetic Ankle Torque." Sensors 23, no. 18 (2023): 7712. http://dx.doi.org/10.3390/s23187712.

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Inverse dynamics from motion capture is the most common technique for acquiring biomechanical kinetic data. However, this method is time-intensive, limited to a gait laboratory setting, and requires a large array of reflective markers to be attached to the body. A practical alternative must be developed to provide biomechanical information to high-bandwidth prosthesis control systems to enable predictive controllers. In this study, we applied deep learning to build dynamical system models capable of accurately estimating and predicting prosthetic ankle torque from inverse dynamics using only s

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Bisola Oluwafadekemi Adegoke, Tolulope Odugbose, and Christiana Adeyemi. "Data analytics for predicting disease outbreaks: A review of models and tools." International Journal of Life Science Research Updates 2, no. 2 (2024): 001–9. http://dx.doi.org/10.53430/ijlsru.2024.2.2.0023.

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The burgeoning field of data analytics has emerged as a pivotal force in the realm of public health, particularly in the context of predicting and mitigating disease outbreaks. This comprehensive review delves into the diverse landscape of models and tools employed in data analytics for disease outbreak prediction. With a focus on synthesizing existing knowledge, the paper aims to provide a nuanced understanding of the strengths, limitations, and future directions within this dynamic field. The review begins with an exploration of various models utilized for disease outbreak prediction, rangin

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Zhang, Xiaopeng. "Paris House Rental Price Index Prediction-A Classical Statistical Model Approach." Highlights in Science, Engineering and Technology 88 (March 29, 2024): 294–99. http://dx.doi.org/10.54097/q6kz2d72.

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The study focuses on predicting rental prices in Paris and aims to contribute to urban economics and data analytics. It analyzes a wide range of data sources, including historical rental prices, economic indicators, demographics, and regulations. The goal is to compare classical statistical models' prediction accuracy of these three models: ARIMA, dynamic regression, and random forest. The results reveal that the ARIMA model performs best, offering more accurate predictions. ARIMA relies on time series analysis, capturing complex patterns in rental prices, making it well-suited for dynamic rea

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Nik Nurul Hafzan, Mat Yaacob, Deris Safaai, Mat Asiah, Mohamad Mohd Saberi, and Safaai Siti Syuhaida. "Review on Predictive Modelling Techniques for Identifying Students at Risk in University Environment." MATEC Web of Conferences 255 (2019): 03002. http://dx.doi.org/10.1051/matecconf/201925503002.

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Predictive analytics including statistical techniques, predictive modelling, machine learning, and data mining that analyse current and historical facts to make predictions about future or otherwise unknown events. Higher education institutions nowadays are under increasing pressure to respond to national and global economic, political and social changes such as the growing need to increase the proportion of students in certain disciplines, embedding workplace graduate attributes and ensuring that the quality of learning programs are both nationally and globally relevant. However, in higher ed

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Kim, Jeonghun, and Ohbyung Kwon. "A Model for Rapid Selection and COVID-19 Prediction with Dynamic and Imbalanced Data." Sustainability 13, no. 6 (2021): 3099. http://dx.doi.org/10.3390/su13063099.

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The COVID-19 pandemic is threatening our quality of life and economic sustainability. The rapid spread of COVID-19 around the world requires each country or region to establish appropriate anti-proliferation policies in a timely manner. It is important, in making COVID-19-related health policy decisions, to predict the number of confirmed COVID-19 patients as accurately and quickly as possible. Predictions are already being made using several traditional models such as the susceptible, infected, and recovered (SIR) and susceptible, exposed, infected, and resistant (SEIR) frameworks, but these

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Liu, Liujun. "A Comparative Examination of Stock Market Prediction: Evaluating Traditional Time Series Analysis Against Deep Learning Approaches." Advances in Economics, Management and Political Sciences 55, no. 1 (2023): 196–204. http://dx.doi.org/10.54254/2754-1169/55/20231008.

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The contemporary financial landscape is characterized by dynamic market behavior. Accurate predictions of stock price movements are not only of paramount importance for financial decision-makers but also pose a significant challenge due to the inherent complexities of financial markets. This research study delves into the realm of stock market prediction by employing a comprehensive approach that combines time series analysis and machine learning techniques. The main goal is to assess different models in predicting price trends, potentially reshaping stock market forecasts and emphasizing the

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Judijanto, Loso, and Fristi Riandari. "Fuzzy logic framework for financial distress prediction: Enhancing corporate decision-making under uncertainty." International Journal of Basic and Applied Science 13, no. 1 (2024): 1–13. http://dx.doi.org/10.35335/ijobas.v13i1.474.

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This research aims to develop an enhanced Fuzzy Logic Framework for Financial Distress Prediction to improve corporate decision-making under uncertainty. The primary objective is to address limitations in traditional fuzzy logic models, such as static rule bases and lack of adaptability to dynamic financial conditions. To achieve this, a time-dependent fuzzy logic system is proposed, incorporating real-time financial data and adaptive learning mechanisms to improve predictive accuracy over time. The research design involves creating a dynamic fuzzy rule base, assigning weights to rules based o

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Yuan, Yihong, and Andrew Grayson Wylie. "Comparing Machine Learning and Time Series Approaches in Predictive Modeling of Urban Fire Incidents: A Case Study of Austin, Texas." ISPRS International Journal of Geo-Information 13, no. 5 (2024): 149. http://dx.doi.org/10.3390/ijgi13050149.

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This study examines urban fire incidents in Austin, Texas using machine learning (Random Forest) and time series (Autoregressive integrated moving average, ARIMA) methods for predictive modeling. Based on a dataset from the City of Austin Fire Department, it addresses the effectiveness of these models in predicting fire occurrences and the influence of fire types and urban district characteristics on predictions. The findings indicate that ARIMA models generally excel in predicting most fire types, except for auto fires. Additionally, the results highlight the significant differences in model

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Lyu, Xiaozhong, Cuiqing Jiang, Yong Ding, Zhao Wang, and Yao Liu. "Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions." Sustainability 11, no. 3 (2019): 913. http://dx.doi.org/10.3390/su11030913.

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Online word-of-mouth (eWOM) disseminated on social media contains a considerable amount of important information that can predict sales. However, the accuracy of sales prediction models using big data on eWOM is still unsatisfactory. We argue that eWOM contains the heat and sentiments of product dimensions, which can improve the accuracy of prediction models based on multiattribute attitude theory. In this paper, we propose a dynamic topic analysis (DTA) framework to extract the heat and sentiments of product dimensions from big data on eWOM. Ultimately, we propose an autoregressive heat-senti

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Korbmacher, Raphael, and Antoine Tordeux. "Toward Better Pedestrian Trajectory Predictions: The Role of Density and Time-to-Collision in Hybrid Deep-Learning Algorithms." Sensors 24, no. 7 (2024): 2356. http://dx.doi.org/10.3390/s24072356.

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Predicting human trajectories poses a significant challenge due to the complex interplay of pedestrian behavior, which is influenced by environmental layout and interpersonal dynamics. This complexity is further compounded by variations in scene density. To address this, we introduce a novel dataset from the Festival of Lights in Lyon 2022, characterized by a wide range of densities (0.2–2.2 ped/m2). Our analysis demonstrates that density-based classification of data can significantly enhance the accuracy of predictive algorithms. We propose an innovative two-stage processing approach, surpass

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Halabi, Susan, Cai Li, and Sheng Luo. "Developing and Validating Risk Assessment Models of Clinical Outcomes in Modern Oncology." JCO Precision Oncology, no. 3 (December 2019): 1–12. http://dx.doi.org/10.1200/po.19.00068.

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The identification of prognostic factors and building of risk assessment prognostic models will continue to play a major role in 21st century medicine in patient management and decision making. Investigators often are interested in examining the relationship among host, tumor-related, and environmental variables in predicting clinical outcomes. We distinguish between static and dynamic prediction models. In static prediction modeling, variables collected at baseline typically are used in building models. On the other hand, dynamic predictive models leverage the longitudinal data of covariates

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Jiang, Linxing Preston, and Rajesh P. N. Rao. "Dynamic predictive coding: A model of hierarchical sequence learning and prediction in the neocortex." PLOS Computational Biology 20, no. 2 (2024): e1011801. http://dx.doi.org/10.1371/journal.pcbi.1011801.

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We introduce dynamic predictive coding, a hierarchical model of spatiotemporal prediction and sequence learning in the neocortex. The model assumes that higher cortical levels modulate the temporal dynamics of lower levels, correcting their predictions of dynamics using prediction errors. As a result, lower levels form representations that encode sequences at shorter timescales (e.g., a single step) while higher levels form representations that encode sequences at longer timescales (e.g., an entire sequence). We tested this model using a two-level neural network, where the top-down modulation

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Mai, Weimin, Junxin Chen, and Xiang Chen. "Time-Evolving Graph Convolutional Recurrent Network for Traffic Prediction." Applied Sciences 12, no. 6 (2022): 2842. http://dx.doi.org/10.3390/app12062842.

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Accurate traffic prediction is crucial to the construction of intelligent transportation systems. This task remains challenging because of the complicated and dynamic spatiotemporal dependency in traffic networks. While various graph-based spatiotemporal networks have been proposed for traffic prediction, most of them rely on predefined graphs from different views or static adaptive matrices. Some implicit dynamics of inter-node dependency may be neglected, which limits the performance of prediction. To address this problem and make more accurate predictions, we propose a traffic prediction mo

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Drisya, G. V., D. C. Kiplangat, K. Asokan, and K. Satheesh Kumar. "Deterministic prediction of surface wind speed variations." Annales Geophysicae 32, no. 11 (2014): 1415–25. http://dx.doi.org/10.5194/angeo-32-1415-2014.

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Abstract. Accurate prediction of wind speed is an important aspect of various tasks related to wind energy management such as wind turbine predictive control and wind power scheduling. The most typical characteristic of wind speed data is its persistent temporal variations. Most of the techniques reported in the literature for prediction of wind speed and power are based on statistical methods or probabilistic distribution of wind speed data. In this paper we demonstrate that deterministic forecasting methods can make accurate short-term predictions of wind speed using past data, at locations

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20

Srinath, M. "Vehicular Traffic Flow Prediction Model Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 109–12. http://dx.doi.org/10.22214/ijraset.2023.54576.

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Abstract: Efficient traffic flow prediction is crucial for effective traffic management and congestion reduction in urban areas. However, traditional statistical models often struggle to accurately capture the intricate dynamics of vehicular traffic flow, particularly under dynamic conditions. In this research project, we propose a novel approach that leverages deep learning techniques, specifically Long Short-Term Memory (LSTM) neural networks, AdaBoost, and gradient descent, to enhance the accuracy of traffic flow predictions .By harnessing historical traffic data, our model generates precis

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Williams-Riquer, Francisco, Alexander Chmelnizkij, Diaa Alkateeb, and Jürgen Grabe. "Prediction of induced soil vibration during pile vibrodriving using Dynamic Mode Decomposition (DMD)." Journal of Physics: Conference Series 2909, no. 1 (2024): 012002. https://doi.org/10.1088/1742-6596/2909/1/012002.

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Abstract This study investigates using the Dynamic Mode Decomposition (DMD) algorithm to perform approximations and time-ahead prediction of soil vibrations during the vibrodriving process. Geotechnical applications face challenges in modeling and predicting soil vibrations due to the soil’s heterogeneous nature. This study addresses this issue using a purely data-driven approach. Geophone data collected during pile installation using a vibrodriver were used to demonstrate the feasibility of the DMD algorithm. The research reveals that both the standard DMD and augmented DMD, which incorporate

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Appiah, Rita, Alexander Heifetz, Derek Kultgen, Lefteri H. Tsoukalas, and Richard B. Vilim. "Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System Identification." Energies 17, no. 24 (2024): 6257. https://doi.org/10.3390/en17246257.

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This study investigates the dynamic regulation of the sodium cold trap purification temperature at Argonne National Laboratory’s liquid sodium test facility, employing long short-term memory (LSTM) system identification techniques. The investigation introduces an innovative hybrid approach by integrating model predictive control (MPC) based on first principles dynamic models with a multi-step time–frequency LSTM model in predicting the temperature profiles of a sodium cold trap purification system. The long short-term memory–model predictive controller (LSTM-MPC) model employs a sliding window

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Zhuang, Qian, and Lianghua Chen. "Dynamic Prediction of Financial Distress Based on Kalman Filtering." Discrete Dynamics in Nature and Society 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/370280.

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The widely used discriminant models currently for financial distress prediction have deficiencies in dynamics. Based on the dynamic nature of corporate financial distress, dynamic prediction models consisting of a process model and a discriminant model, which are used to describe the dynamic process and discriminant rules of financial distress, respectively, is established. The operation of the dynamic prediction is achieved by Kalman filtering algorithm. And a generaln-step-ahead prediction algorithm based on Kalman filtering is deduced in order for prospective prediction. An empirical study

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Xu, Ziqi, Jingwen Zhang, Jacob Greenberg, et al. "Predicting Multi-dimensional Surgical Outcomes with Multi-modal Mobile Sensing." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, no. 2 (2024): 1–30. http://dx.doi.org/10.1145/3659628.

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Pre-operative prediction of post-surgical recovery for patients is vital for clinical decision-making and personalized treatments, especially with lumbar spine surgery, where patients exhibit highly heterogeneous outcomes. Existing predictive tools mainly rely on traditional Patient-Reported Outcome Measures (PROMs), which fail to capture the long-term dynamics of patient conditions before the surgery. Moreover, existing studies focus on predicting a single surgical outcome. However, recovery from spine surgery is multi-dimensional, including multiple distinctive but interrelated outcomes, suc

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G, Mrs Gowri. "Prediction of Air Pollution in Smart Cities Using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 273–77. http://dx.doi.org/10.22214/ijraset.2021.39241.

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Abstract: Air-pollution is one of the main threats for developed societies. According to the World Health Organization (WHO), pollution is the main cause of deaths among children aged under five years. Smart cities are called to play a decisive role to increase such pollution in real-time. The increase in air pollution due to fossil fuel consumption as well as its ill effects on the climate has made air pollution forecasting an important research area in today’s times. Deployment of the Internet of things (IoT) based sensors has considerably changed the dynamics of predicting air quality. pred

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Islam, Md Sariful, and Thomas W. Crawford. "Assessment of Spatio-Temporal Empirical Forecasting Performance of Future Shoreline Positions." Remote Sensing 14, no. 24 (2022): 6364. http://dx.doi.org/10.3390/rs14246364.

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Coasts and coastlines in many parts of the world are highly dynamic in nature, where large changes in the shoreline position can occur due to natural and anthropogenic influences. The prediction of future shoreline positions is of great importance in the better planning and management of coastal areas. With an aim to assess the different methods of prediction, this study investigates the performance of future shoreline position predictions by quantifying how prediction performance varies depending on the time depths of input historical shoreline data and the time horizons of predicted shorelin

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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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Geweke, John, and Gianni Amisano. "Prediction with Misspecified Models." American Economic Review 102, no. 3 (2012): 482–86. http://dx.doi.org/10.1257/aer.102.3.482.

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The assumption that one of a set of prediction models is a literal description of reality formally underlies many formal econometric methods, including Bayesian model averaging and most approaches to model selection. Prediction pooling does not invoke this assumption and leads to predictions that improve on those based on Bayesian model averaging, as assessed by the log predictive score. The paper shows that the improvement is substantial using a pool consisting of a dynamic stochastic general equilibrium model, a vector autoregression, and a dynamic factor model, in conjunction with standard

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Kulkarni, N. M., A. Chandra, and S. S. Jagdale. "A Dynamic Model for End Milling Using Single Point Cutting Theory." Journal of Manufacturing Science and Engineering 118, no. 2 (1996): 272–74. http://dx.doi.org/10.1115/1.2831021.

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The dynamics of a milling process can significantly influence the surface quality and integrity of the finished part. Accordingly, various researchers have investigated the dynamics of milling processes using a hierarchy of models. Tlusty and Smith (1991) provides a review of these models. In recent years, several other researchers (e.g., Armarego and Deshpande, 1989; Montgomery and Altintas, 1991; Nallakatla and Smith, 1992) have also continued to enhance various aspects of such dynamic models. While these dynamic models provide significant insights into the cutting characteristics of a milli

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Kim, Donghyun, Heechan Han, Wonjoon Wang, Yujin Kang, Hoyong Lee, and Hung Soo Kim. "Application of Deep Learning Models and Network Method for Comprehensive Air-Quality Index Prediction." Applied Sciences 12, no. 13 (2022): 6699. http://dx.doi.org/10.3390/app12136699.

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Accurate pollutant prediction is essential in fields such as meteorology, meteorological disasters, and climate change studies. In this study, long short-term memory (LSTM) and deep neural network (DNN) models were applied to six pollutants and comprehensive air-quality index (CAI) predictions from 2015 to 2020 in Korea. In addition, we used the network method to find the best data sources that provide factors affecting comprehensive air-quality index behaviors. This study had two steps: (1) predicting the six pollutants, including fine dust (PM10), fine particulate matter (PM2.5), ozone (O3),

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Abishek, B. Ebenezer, Vijayalakshmi A, Blessy Sharon Gem, and P. Sathish Kumar. "ULTRA WIDE-BAND SYSTEMS WITH ENSEMBLES OF CLASSIFIERS BASED LATENT GRAPH PREDICTOR FM FOR OPTIMAL RESOURCE PREDICTION." ICTACT Journal on Communication Technology 14, no. 4 (2023): 3043–49. http://dx.doi.org/10.21917/ijct.2023.0453.

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The proliferation of Ultra Wide-Band (UWB) systems has introduced new challenges in predicting optimal resource allocation, necessitating advanced methodologies to enhance efficiency. Current resource prediction models for UWB systems often struggle to accurately forecast optimal resource allocation due to the dynamic and complex nature of the communication environment. This study aims to overcome these limitations by introducing a novel framework that integrates machine learning ensembles and latent graph predictor FM to achieve more accurate and reliable resource predictions. While various r

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Villegas Mier, Oscar, Anna Dittmann, Wiebke Herzberg, et al. "Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast." Energies 16, no. 19 (2023): 6980. http://dx.doi.org/10.3390/en16196980.

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Predictive control has great potential in the home energy management domain. However, such controls need reliable predictions of the system dynamics as well as energy consumption and generation, and the actual implementation in the real system is associated with many challenges. This paper presents the implementation of predictive controls for a heat pump with thermal storage in a real single-family house with a photovoltaic rooftop system. The predictive controls make use of a novel cloud camera-based short-term solar energy prediction and an intraday prediction system that includes additiona

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Mo, Hanlin. "Comparative Analysis of Linear Regression, Polynomial Regression, and ARIMA Model for Short-term Stock Price Forecasting." Advances in Economics, Management and Political Sciences 49, no. 1 (2023): 166–75. http://dx.doi.org/10.54254/2754-1169/49/20230509.

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This research investigates the effectiveness of three prominent stock price prediction methodologies: Linear Regression, Polynomial Regression, and AutoRegressive Integrated Moving Average (ARIMA) model. The study leverages one and a half years of historical data from Apple, Tesla, Amazon, and Nike stocks to predict average prices over the ensuing 14 days. The predictive efficacy of each model is tested against actual data, revealing their respective strengths and limitations. Linear Regression offers an overview of stock trends with limited intricacy, while Polynomial Regression delivers a mo

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Kačur, Ján, Patrik Flegner, Milan Durdán, and Marek Laciak. "Prediction of Temperature and Carbon Concentration in Oxygen Steelmaking by Machine Learning: A Comparative Study." Applied Sciences 12, no. 15 (2022): 7757. http://dx.doi.org/10.3390/app12157757.

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The basic oxygen steelmaking process (BOS) faces the issue of the absence of information about the melt temperature and the carbon concentration in the melt. Although deterministic models for predicting steelmaking process variables are being developed in metallurgical research, machine-learning models can model the nonlinearities of process variables and provide a good estimate of the target process variables. In this paper, five machine learning methods were applied to predict the temperature and carbon concentration in the melt at the endpoint of BOS. Multivariate adaptive regression spline

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Lu, Ying, Xiaopeng Fan, Zhipan Zhao, and Xuepeng Jiang. "Dynamic Fire Risk Classification Prediction of Stadiums: Multi-Dimensional Machine Learning Analysis Based on Intelligent Perception." Applied Sciences 12, no. 13 (2022): 6607. http://dx.doi.org/10.3390/app12136607.

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Stadium fires can easily cause massive casualties and property damage. The early risk prediction of stadiums will be able to reduce the incidence of fires by making corresponding fire safety management and decision making in an early and targeted manner. In the field of building fires, some studies apply data mining techniques and machine learning algorithms to the collected risk hazard data for fire risk prediction. However, most of these studies use all attributes in the dataset, which may degrade the performance of predictive models due to data redundancy. Furthermore, machine learning algo

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Ma, Junwei, Xiaoxu Niu, Huiming Tang, Yankun Wang, Tao Wen, and Junrong Zhang. "Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach." Complexity 2020 (January 28, 2020): 1–15. http://dx.doi.org/10.1155/2020/2624547.

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Displacement prediction of reservoir landslide remains inherently uncertain since a complete understanding of the complex nonlinear, dynamic landslide system is still lacking. An appropriate quantification of predictive uncertainties is a key underpinning of displacement prediction and mitigation of reservoir landslide. A density prediction, offering a full estimation of the probability density for future outputs, is promising for quantification of the uncertainty of landslide displacement. In the present study, a hybrid computational intelligence approach is proposed to build a density predic

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Zhang, Shaohu, Jianxiao Ma, Boshuo Geng, and Hanbin Wang. "Traffic flow prediction with a multi-dimensional feature input: A new method based on attention mechanisms." Electronic Research Archive 32, no. 2 (2024): 979–1002. http://dx.doi.org/10.3934/era.2024048.

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<abstract> <p>Accurately predicting traffic flow is an essential component of intelligent transportation systems. The advancements in traffic data collection technology have broadened the range of features that affect and represent traffic flow variations. However, solely inputting gathered features into the model without analysis might overlook valuable information, hindering the improvement of predictive performance. Furthermore, intricate dynamic relationships among various feature inputs could constrain the model's potential for further enhancement in predictive accuracy. Conse

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Zeng, Lingchao, Cheng Zhang, Pengfei Qin, Yejun Zhou, and Yaxing Cai. "One Method for Predicting Satellite Communication Terminal Service Demands Based on Artificial Intelligence Algorithms." Applied Sciences 14, no. 14 (2024): 6019. http://dx.doi.org/10.3390/app14146019.

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This paper presents a traffic demand prediction method based on deep learning algorithms, aiming to address the dynamic traffic demands in satellite communication and enhance resource management efficiency. Integrating Seq2Seq and LSTM networks, the method improves prediction accuracy and applicability, especially for mobile terminals such as aviation and maritime ones. Unlike traditional approaches, it does not require extensive statistical data and can be generalized to real-world systems, providing stable long-term traffic demand predictions. This study utilizes real-world flight data mappe

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Zhang, Fuhao, Wenbo Shi, Jian Zhang, Min Zeng, Min Li, and Lukasz Kurgan. "PROBselect: accurate prediction of protein-binding residues from proteins sequences via dynamic predictor selection." Bioinformatics 36, Supplement_2 (2020): i735—i744. http://dx.doi.org/10.1093/bioinformatics/btaa806.

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Abstract Motivation Knowledge of protein-binding residues (PBRs) improves our understanding of protein−protein interactions, contributes to the prediction of protein functions and facilitates protein−protein docking calculations. While many sequence-based predictors of PBRs were published, they offer modest levels of predictive performance and most of them cross-predict residues that interact with other partners. One unexplored option to improve the predictive quality is to design consensus predictors that combine results produced by multiple methods. Results We empirically investigate predict

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Li, Jiale, Li Fan, Xuran Wang, Tiejiang Sun, and Mengjie Zhou. "Product Demand Prediction with Spatial Graph Neural Networks." Applied Sciences 14, no. 16 (2024): 6989. http://dx.doi.org/10.3390/app14166989.

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In the rapidly evolving online marketplace, accurately predicting the demand for pre-owned items presents a significant challenge for sellers, impacting pricing strategies, product presentation, and marketing investments. Traditional demand prediction methods, while foundational, often fall short in addressing the dynamic and heterogeneous nature of e-commerce data, which encompasses textual descriptions, visual elements, geographic contexts, and temporal dynamics. This paper introduces a novel approach utilizing the Graph Neural Network (GNN) to enhance demand prediction accuracy by leveragin

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Cao, Ren-Meng, Xiao Fan Liu, and Xiao-Ke Xu. "Why cannot long-term cascade be predicted? Exploring temporal dynamics in information diffusion processes." Royal Society Open Science 8, no. 9 (2021): 202245. http://dx.doi.org/10.1098/rsos.202245.

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Predicting information cascade plays a crucial role in various applications such as advertising campaigns, emergency management and infodemic controlling. However, predicting the scale of an information cascade in the long-term could be difficult. In this study, we take Weibo, a Twitter-like online social platform, as an example, exhaustively extract predictive features from the data, and use a conventional machine learning algorithm to predict the information cascade scales. Specifically, we compare the predictive power (and the loss of it) of different categories of features in short-term an

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Sun, Sihan, Minming Gu, and Tuoqi Liu. "Adaptive Sliding Window–Dynamic Time Warping-Based Fluctuation Series Prediction for the Capacity of Lithium-Ion Batteries." Electronics 13, no. 13 (2024): 2501. http://dx.doi.org/10.3390/electronics13132501.

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Accurately predicting the capacity of lithium-ion batteries is crucial for improving battery reliability and preventing potential incidents. Current prediction models for predicting lithium-ion battery capacity fluctuations encounter challenges like inadequate fitting and suboptimal computational efficiency. This study presents a new approach for fluctuation prediction termed ASW-DTW, which integrates Adaptive Sliding Window (ASW) and Dynamic Time Warping (DTW). Initially, this approach leverages Empirical Mode Decomposition (EMD) to preprocess the raw battery capacity data and extract local f

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AlQahtani, Nasser A., Timothy J. Rogers, and Neil D. Sims. "Towards nonlinear model predictive control of flexible structures using Gaussian Processes." Journal of Physics: Conference Series 2909, no. 1 (2024): 012004. https://doi.org/10.1088/1742-6596/2909/1/012004.

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Abstract In recent years, there is a growing interest of using and implementing data driven control in structural dynamics. This study considers applying Nonlinear Model Predictive Control (NMPC) to flexible structures by utilising recent developments in models which have been learnt from example data, i.e. machine learning approaches. The Gaussian process (GP) is a Bayesian machine learning algorithm identified for use as a black-box model in NMPC; it provides both the prediction of the system output and the associated confidence. In a control context, a GP can be utilised as a discrepancy mo

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Wu, Tengtao. "High throughput screening of thermal interface materials by machine learning." Applied and Computational Engineering 61, no. 1 (2024): 77–86. http://dx.doi.org/10.54254/2755-2721/61/20240930.

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Till now, it remains a challenge for effective prediction and screening of novel materials with high thermal conductivity, as well as further optimization of the interface thermal resistance. Normally, people have to spend long time on tedious calculations when predicting and screening these materials. In this paper, I combined machine learning with molecular dynamics simulations to investigate the thermal conductive properties of materials with the aim of significantly reducing computational consumption. I first applied molecular dynamics simulations to obtain the relevant properties of mater

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Zhang, Junling, Min Mei, Jun Wang, et al. "The Construction and Application of a Deep Learning-Based Primary Support Deformation Prediction Model for Large Cross-Section Tunnels." Applied Sciences 14, no. 2 (2024): 912. http://dx.doi.org/10.3390/app14020912.

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The deformation of tunnel support structures during tunnel construction is influenced by geological factors, geometrical factors, support factors, and construction factors. Accurate prediction of tunnel support structure deformation is crucial for engineering safety and optimizing support parameters. Traditional methods for tunnel deformation prediction have often relied on numerical simulations and model experiments, which may not always meet the time-sensitive requirements. In this study, we propose a fusion deep neural network (FDNN) model that combines multiple algorithms with a complement

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Pipin, Sio Jurnalis, Ronsen Purba, and Heru Kurniawan. "Prediksi Saham Menggunakan Recurrent Neural Network (RNN-LSTM) dengan Optimasi Adaptive Moment Estimation." Journal of Computer System and Informatics (JoSYC) 4, no. 4 (2023): 806–15. http://dx.doi.org/10.47065/josyc.v4i4.4014.

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Predicting stock price movements is a complex challenge in the financial market due to unpredictable price fluctuations and high sensitivity levels. Noise in historical stock price data and temporal dependencies between previous and current prices make recognizing price movement patterns difficult. In a dynamic market environment, the model's ability to generate accurate predictions holds significant implications for more informed investment decision-making. The Recurrent Neural Network - Long Short-Term Memory (RNN-LSTM) model holds great potential for stock price prediction. It captures temp

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Long, Hao, Feng Hu, and Lingjun Kong. "Enhanced Link Prediction and Traffic Load Balancing in Unmanned Aerial Vehicle-Based Cloud-Edge-Local Networks." Drones 8, no. 10 (2024): 528. http://dx.doi.org/10.3390/drones8100528.

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With the advancement of cloud-edge-local computing, Unmanned Aerial Vehicles (UAVs), as flexible mobile nodes, offer novel solutions for dynamic network deployment. However, existing research on UAV networks faces substantial challenges in accurately predicting link dynamics and efficiently managing traffic loads, particularly in highly distributed and rapidly changing environments. These limitations result in inefficient resource allocation and suboptimal network performance. To address these challenges, this paper proposes a UAV-based cloud-edge-local network resource elastic scheduling arch

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Liu, Xiao Kang, Ji Sen Yang, Zhong Hua Gao, and Dong Lin Peng. "Position Predictive Measurement Method for Time Grating CNC Rotary Table." Advanced Materials Research 139-141 (October 2010): 1587–90. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1587.

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Time grating sensor transforms space domain information to time domain and measures spatial displacement with time. To develop high precision time grating CNC rotary table and reduce the dynamic position feedback error of the table, circular position predictive measurement method is proposed for transforming time domain information back to the space domain based on time-space transformation technology. Predicted values are obtained by modeling the measured values with time series theory, and the last prediction error is corrected in real time using the current measured values. Modeling method

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Gevorgian, Aleksandr, Giovanni Pernigotto, and Andrea Gasparella. "Addressing Data Scarcity in Solar Energy Prediction with Machine Learning and Augmentation Techniques." Energies 17, no. 14 (2024): 3365. http://dx.doi.org/10.3390/en17143365.

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The accurate prediction of global horizontal irradiance (GHI) is crucial for optimizing solar power generation systems, particularly in mountainous areas with complex topography and unique microclimates. These regions face significant challenges due to limited reliable data and the dynamic nature of local weather conditions, which complicate accurate GHI measurement. The scarcity of precise data impedes the development of reliable solar energy prediction models, impacting both economic and environmental outcomes. To address these data scarcity challenges in solar energy prediction, this paper

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Nguyen, Hoang, Christopher Bentley, Le Minh Kieu, Yushuai Fu, and Chen Cai. "Deep Learning System for Travel Speed Predictions on Multiple Arterial Road Segments." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 4 (2019): 145–57. http://dx.doi.org/10.1177/0361198119838508.

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Accurate travel speed prediction is a critical tool for incidence response management. The complex dynamics of transport systems render model-based prediction extremely challenging. However, the large amounts of available vehicle speed data contain the complex interdependencies of the target travel speed; the data itself can be used to generate accurate predictions using deep learning methods. In this work, a deep learning methodology involving feature generation, model development, and model deployment is presented. The authors demonstrate the high performance of deep learning methods (relati

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