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Journal articles on the topic 'SVR Neural Network'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Jayadianti, Herlina, Tedy Agung Cahyadi, Nur Ali Amri, and Muhammad Fathurrahman Pitayandanu. "METODE KOMPARASI ARTIFICIAL NEURAL NETWORK PADA PREDIKSI CURAH HUJAN - LITERATURE REVIEW." Jurnal Tekno Insentif 14, no. 2 (2020): 48–53. http://dx.doi.org/10.36787/jti.v14i2.150.

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Abstrak - Penelitian untuk mencari model prediksi curah hujan yang akurat di berbagai bidang sudah banyak dilakukan, maka dilakukan di-review kembali guna membantu proses penyaliran dalam perusahaan tambang. Review dilakukan dengan membandingkan hasil dari setiap model yang telah dilakukan pada beberapa penelitian sebelumnya. Penelitian ini menggunakan metode kuantitatif. Model yang dibandingkan pada penelitian di antaranya yaitu model Fuzzy, Fast Fourier Transformation (FFT), Emotional Artificial Neural Network (EANN), Artificial Neural Network (ANN), Adaptive Ensemble Empirical Mode Decompos

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Guo, Kong Hui, and Xian Yun Wang. "Comparisons of Support Vector Regression and Neural Network in Modelling the Hydraulic Damper." Advanced Materials Research 403-408 (November 2011): 3805–12. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3805.

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Nonparametric models of hydraulic damper based on support vector regression (SVR) are developed. Then these models are compared with two kinds neural network models. One is backpropagation neural network (BPNN) model; another is radial basis function neural network (RBFNN) model. Comparisons are carried out both on virtual damper and actual damper. The force-velocity relation of a virtual damper is obtained based on a rheological model. Then these data are used to identify the characteristics of the virtual damper. The dynamometer measurements of an actual displacement-dependent damper are obt

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Edy, Fradinata, Suthummanon Sakesun, and Suntiamorntut Wannarat. "Initial Optimal Parameters of Artificial Neural Network and Support Vector Regression." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 3341–48. https://doi.org/10.11591/ijece.v8i5.pp3341-3348.

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This paper presents architecture of backpropagation Artificial Neural Network (ANN) and Support Vector Regression (SVR) models in supervised learning process for cement demand dataset. This study aims to identify the effectiveness of each parameter of mean square error (MSE) indicators for time series dataset. The study varies different random sample in each demand parameter in the network of ANN and support vector function as well. The variations of percent datasets from activation function, learning rate of sigmoid and purelin, hidden layer, neurons, and training function should be applied f

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Lin, Kuo Ping. "Application of Least-Squares Support Vector Regression with PSO for CPU Performance Forecasting." Advanced Materials Research 630 (December 2012): 366–71. http://dx.doi.org/10.4028/www.scientific.net/amr.630.366.

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The success of CPU performance prediction will make many benefits. This study adopts the least-squares support vector regression (LS-SVR) with particle swarm optimization (PSO) algorithm to improver accuracy of CPU performance prediction. LS-SVR with PSO, support vector regression (SVR) with PSO, general regression neural network (GRNN), radial basis neural network (RBNN), and linear regression are employed for CPU performance prediction. Empirical results indicate that the LS-SVR (Linear kernel) with PSO has better performance in terms of forecasting accuracy than the other methods. Therefore

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Liu, Jiao, Guoyou Shi, and Kaige Zhu. "Vessel Trajectory Prediction Model Based on AIS Sensor Data and Adaptive Chaos Differential Evolution Support Vector Regression (ACDE-SVR)." Applied Sciences 9, no. 15 (2019): 2983. http://dx.doi.org/10.3390/app9152983.

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There are difficulties in obtaining accurate modeling of ship trajectories with traditional prediction methods. For example, neural networks are prone to falling into local optima and there are a small number of Automatic Identification System (AIS) information samples regarding target ships acquired in real time at sea. In order to improve the accuracy of ship trajectory predictions and solve these problems, a trajectory prediction model based on support vector regression (SVR) is proposed. Ship speed, course, time stamp, longitude and latitude from AIS data were selected as sample features a

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Jiang, Lei, Da Teng, and Yue Zhao. "A Soft Measurement Method for the Tail Diameter in the Growing Process of Czochralski Silicon Single Crystals." Applied Sciences 14, no. 4 (2024): 1569. http://dx.doi.org/10.3390/app14041569.

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In the Czochralski silicon single crystal growth process, the tail diameter is a key parameter that cannot be directly measured. In this paper, we propose a real-time soft measurement method that combines a deep belief network (DBN) and a support vector regression (SVR) network based on system identification to accurately predict the crystal diameter. The main steps of the proposed method are as follows: First, we address the delay problem of the effects of the temperature and crystal pulling speed on the tail diameter growth by using a back propagation (BP) neural network based on the mean im

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Fradinata, Edy, Sakesun Suthummanon, and Wannarat Suntiamorntut. "Initial Optimal Parameters of Artificial Neural Network and Support Vector Regression." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 3341. http://dx.doi.org/10.11591/ijece.v8i5.pp3341-3348.

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This paper presents architecture of backpropagation Artificial Neural Network (ANN) and Support Vector Regression (SVR) models in supervised learning process for cement demand dataset. This study aims to identify the effectiveness of each parameter of mean square error (MSE) indicators for time series dataset. The study varies different random sample in each demand parameter in the network of ANN and support vector function as well. The variations of percent datasets from activation function, learning rate of sigmoid and purelin, hidden layer, neurons, and training function should be applied f

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8

Pyo, JongCheol, Hongtao Duan, Mayzonee Ligaray, et al. "An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery." Remote Sensing 12, no. 7 (2020): 1073. http://dx.doi.org/10.3390/rs12071073.

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Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neura

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Fu, Jiake, Huijing Tian, Lingguang Song, Mingchao Li, Shuo Bai, and Qiubing Ren. "Productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data." Engineering, Construction and Architectural Management 28, no. 7 (2021): 2023–41. http://dx.doi.org/10.1108/ecam-05-2020-0357.

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PurposeThis paper presents a new approach of productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data.Design/methodology/approachThe paper used big data, data mining and machine learning techniques to extract features of cutter suction dredgers (CSD) for predicting its productivity. ElasticNet-SVR (Elastic Net-Support Vector Machine) method is used to filter the original monitoring data. Along with the actual working conditions of CSD, 15 features were selected. Then, a box plot was used to clean the corresponding data by filtering ou

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Raheja, Supriya, and Sahil Malik. "Prediction of Air Quality Using LSTM Recurrent Neural Network." International Journal of Software Innovation 10, no. 1 (2022): 1–16. http://dx.doi.org/10.4018/ijsi.297982.

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Rapid increase of Industrialization and Urbanization significantly draws the interest of researchers towards the prediction of air quality. Efficient modelling of air quality parameters using deep learning methods can facilitate the imminent implications of air pollution. However, existing methods weakens at consideration of long-term dependencies for multiple parameters. The present study aims prediction of air quality of New Delhi based on concentration of multiple parameters namely PM2.5, PM10, CO, O3, NO2 and SO2. The study uses long short-term memory (LSTM) approach due to its efficiency

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Hu, Min, Wei Li, Ke Yan, Zhiwei Ji, and Haigen Hu. "Modern Machine Learning Techniques for Univariate Tunnel Settlement Forecasting: A Comparative Study." Mathematical Problems in Engineering 2019 (April 9, 2019): 1–12. http://dx.doi.org/10.1155/2019/7057612.

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Tunnel settlement commonly occurs during the tunnel construction processes in large cities. Existing forecasting methods for tunnel settlements include model-based approaches and artificial intelligence (AI) enhanced approaches. Compared with traditional forecasting methods, artificial neural networks can be easily implemented, with high performance efficiency and forecasting accuracy. In this study, an extended machine learning framework is proposed combining particle swarm optimization (PSO) with support vector regression (SVR), back-propagation neural network (BPNN), and extreme learning ma

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Tan, Qingyan. "English Teaching Evaluation Combined with End-User Computing and Neural Network." Mobile Information Systems 2022 (February 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/4018269.

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A neural network model and English teaching evaluation of university with end-user computing are the focus of this paper. The main research contributions are as follows: (1) Propose an ADA-BP neural network. It applies the adaptive learning rate as well as the momentum term to promote the BP network. Experiments show that the model solves the problems of the existing methods and models such as difficulty in determining weights, prone to overfitting, slow convergence, and prone to local minimums, which verifies the effectiveness in evaluating college English teaching. (2) Propose the DA-SVR net

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Zhai, Zhi Hua, and Ping Li Wu. "Life Prediction for Silicon Pressure Sensor Based on SVR." Applied Mechanics and Materials 187 (June 2012): 241–44. http://dx.doi.org/10.4028/www.scientific.net/amm.187.241.

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In order to improve the reliability of silicon pressure sensor, life prediction for silicon pressure sensor should be performed. Life prediction for silicon pressure sensor based on support vector regression is proposed in the paper. Grid method is used to determine the parameters of support vector regression in the process of training support vector regression model. Life for silicon pressure sensor under the conditions of different pressures is given in the experimental analysis. The comparison of the errors and mean errors between support vector regression and BP neural network indicates th

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Zhou, Chonggang, and Yunfei Ding. "Comparative Analysis of Machine Learning Models for Predicting Contaminant Concentration Distributions in Hospital Wards." Buildings 15, no. 11 (2025): 1828. https://doi.org/10.3390/buildings15111828.

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As the distribution of indoor contaminants is often heterogeneous, the traditional Wells–Riley equation is inadequate for accurately assessing the infection risk to indoor personnel. In this study, contaminant concentration data from hospital wards were obtained through experimentally validated computational fluid dynamics (CFD) simulations. Four common machine learning models—multiple linear regression (MLR), support vector regression (SVR), backpropagation (BP) neural network, and convolutional neural network (CNN)—were employed to predict the distribution of contaminants within the wards. T

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ZHANG, JIE, JIE LU, and GUANGQUAN ZHANG. "A SEASONAL AUTO-REGRESSIVE MODEL BASED SUPPORT VECTOR REGRESSION PREDICTION METHOD FOR H5N1 AVIAN INFLUENZA ANIMAL EVENTS." International Journal of Computational Intelligence and Applications 10, no. 02 (2011): 199–230. http://dx.doi.org/10.1142/s1469026811003069.

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The time series prediction of avian influenza epidemics is a complex issue, because avian influenza has latent seasonality which is difficult to identify. Although researchers have applied a neural network (NN) model and the Box-Jenkins model for the seasonal epidemic series research area, the results are limited. In this study, we develop a new prediction seasonal auto-regressive-based support vector regression (SAR-SVR) model which combines the seasonal auto-regressive (SAR) model with a support vector regression (SVR) model to address this prediction problem to overcome existing limitations

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Wang, Ji, Jian Zhou, Wen-An Mo, Chao Liang, Li-Jun Sun, and Chun-Bo Wen. "Tool life prediction based on multi-source feature PSO-SVR neural network." Journal of Physics: Conference Series 2366, no. 1 (2022): 012049. http://dx.doi.org/10.1088/1742-6596/2366/1/012049.

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Abstract With the continuous improvement of modern manufacturing automation and process intensification, the complexity of tools and machined parts has greatly increased, and their performance directly affects the quality of workpieces and production efficiency. Accurate prediction of tool life is conducive to improving production efficiency and reducing enterprise costs. In this paper, a tool life prediction method based on multi-source feature PSO-SVR neural network is proposed. By monitoring and collecting the current and vibration signals of the tool during the machining process of CNC mac

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Hu, Li, Tianhong Liang, Gaofang Yin, and Nanjing Zhao. "Quantitative Representation of Water Quality Biotoxicity by Algal Photosynthetic Inhibition." Toxics 11, no. 6 (2023): 493. http://dx.doi.org/10.3390/toxics11060493.

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The method based on the photosynthetic inhibition effect of algae offers the advantages of swift response and straightforward measurement. Nonetheless, this effect is influenced by both the environment and the state of the algae themselves. Additionally, a single parameter is vulnerable to uncertainties, rendering the measurement accuracy and stability inadequate. This paper employed currently utilized photosynthetic fluorescence parameters, including Fv/Fm(maximum photochemical quantum yield), Performance Indicator (PIabs), Comprehensive Parameter Index (CPI) and Performance Index of Comprehe

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Duan, Lingyun, Ziyuan Liu, Wen Yu, et al. "Modeling Analysis and Comparision of Neural Network Simulation Based on ECM and LSTM." Journal of Physics: Conference Series 2068, no. 1 (2021): 012041. http://dx.doi.org/10.1088/1742-6596/2068/1/012041.

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Abstract Comparing the prediction effects of traditional econometric algorithm model and deep learning algorithm model, taking regional GDP as an example, two prediction models of ARMA-ECM and LSTM-SVR are established for prediction, and the prediction results of different models are compared and analyzed. The results show that there are some deviations in the prediction results of the two models, but the prediction trends are the same. The prediction accuracy of LSTM-SVR model will decrease significantly with the reduction of time series data samples, while ARMA-ECM model is not so sensitive.

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Martinez, Elodie, Anouar Brini, Thomas Gorgues, et al. "Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean." Remote Sensing 12, no. 24 (2020): 4156. http://dx.doi.org/10.3390/rs12244156.

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Phytoplankton plays a key role in the carbon cycle and supports the oceanic food web. While its seasonal and interannual cycles are rather well characterized owing to the modern satellite ocean color era, its longer time variability remains largely unknown due to the short time-period covered by observations on a global scale. With the aim of reconstructing this longer-term phytoplankton variability, a support vector regression (SVR) approach was recently considered to derive surface Chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) from physical oceanic model outputs and atm

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Niu, Jian Guang, Chun Yan Gao, and Xiu Qing Xing. "Quality Cost Forecast of the Construction Enterprise Based on SVR Model." Advanced Materials Research 594-597 (November 2012): 3011–14. http://dx.doi.org/10.4028/www.scientific.net/amr.594-597.3011.

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This paper established a relatively good index system of quality cost projections. The quality cost of construction enterprise is predicted by introducing a new mathematical model — Support Vector Regression Model (SVR). SVR is one of the best methods on dealing with small samples, avoiding the defects of neural network that is easy to fall into local minimum, lower accuracy rate, and it verified Unascertained-SVR model is feasible and good accuracy by example.

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Zhao, Xiaoguo, Ding Liu, and Xiaomei Yan. "Diameter Prediction of Silicon Ingots in the Czochralski Process Based on a Hybrid Deep Learning Model." Crystals 13, no. 1 (2022): 36. http://dx.doi.org/10.3390/cryst13010036.

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The diameter prediction of silicon ingots in the Czochralski process is a complex problem because the process is highly nonlinear, time-varying, and time-delay. To address this problem, this paper presents a novel hybrid deep learning model, which combines the deep belief network (DBN), support vector regression (SVR), and the ant lion optimizer (ALO). Continuous restricted Boltzmann machines (CRBMs) are used in DBN for working with continuous industrial data. The feature aggregates the outputs from various DBNs through an SVR model. Additionally, the ALO algorithm is used for the parameter’s

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Sankar Ganesh, S., Pachaiyappan Arulmozhivarman, and Rao Tatavarti. "Forecasting Air Quality Index Using an Ensemble of Artificial Neural Networks and Regression Models." Journal of Intelligent Systems 28, no. 5 (2017): 893–903. http://dx.doi.org/10.1515/jisys-2017-0277.

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Abstract Air is the most essential constituent for the sustenance of life on earth. The air we inhale has a tremendous impact on our health and well-being. Hence, it is always advisable to monitor the quality of air in our environment. To forecast the air quality index (AQI), artificial neural networks (ANNs) trained with conjugate gradient descent (CGD), such as multilayer perceptron (MLP), cascade forward neural network, Elman neural network, radial basis function (RBF) neural network, and nonlinear autoregressive model with exogenous input (NARX) along with regression models such as multipl

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Luo, Peilei, Huichun Ye, Wenjiang Huang, et al. "Enabling Deep-Neural-Network-Integrated Optical and SAR Data to Estimate the Maize Leaf Area Index and Biomass with Limited In Situ Data." Remote Sensing 14, no. 21 (2022): 5624. http://dx.doi.org/10.3390/rs14215624.

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Accurate estimation of the maize leaf area index (LAI) and biomass is of great importance in guiding field management and early yield estimation. Physical models and traditional machine learning methods are commonly used for LAI and biomass estimation. However, these models and methods mostly rely on handcrafted features and theoretical formulas under idealized assumptions, which limits their accuracy. Deep neural networks have demonstrated great superiority in automatic feature extraction and complicated nonlinear approximation, but their application to LAI and biomass estimation has been hin

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Wang, Jiuxin, Yutian Luo, Zhengming Yang, Xinli Zhao, and Zhongkun Niu. "Research on Prediction of Movable Fluid Percentage in Unconventional Reservoir Based on Deep Learning." Applied Sciences 11, no. 8 (2021): 3589. http://dx.doi.org/10.3390/app11083589.

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In order to improve the measurement speed and prediction accuracy of unconventional reservoir parameters, the deep neural network (DNN) is used to predict movable fluid percentage of unconventional reservoirs. The Adam optimizer is used in the DNN model to ensure the stability and accuracy of the model in the gradient descent process, and the prediction effect is compared with the back propagation neural network (BPNN), K-nearest neighbor (KNN), and support vector regression model (SVR). During network training, L2 regularization is used to avoid over-fitting and improve the generalization abi

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Jin, Shousong, Mengyi Cao, Qiancheng Qian, Guo Zhang, and Yaliang Wang. "Study on an Assembly Prediction Method of RV Reducer Based on IGWO Algorithm and SVR Model." Sensors 23, no. 1 (2022): 366. http://dx.doi.org/10.3390/s23010366.

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This paper proposes a new method for predicting rotation error based on improved grey wolf–optimized support vector regression (IGWO-SVR), because the existing rotation error research methods cannot meet the production beat and product quality requirements of enterprises, because of the disadvantages of its being time-consuming and having poor calculation accuracy. First, the grey wolf algorithm is improved based on the optimal Latin hypercube sampling initialization, nonlinear convergence factor, and dynamic weights to improve its accuracy in optimizing the parameters of the support vector re

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Jia, Xiaoli, Lin Zhou, Haibo Huang, Jian Pang, and Liang Yang. "Improving Electric Vehicle Structural-Borne Noise Based on Convolutional Neural Network-Support Vector Regression." Electronics 13, no. 1 (2023): 113. http://dx.doi.org/10.3390/electronics13010113.

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In order to enhance the predictive accuracy and control capabilities pertaining to low- and medium-frequency road noise in automotive contexts, this study introduces a methodology for Structural-borne Road Noise (SRN) prediction and optimization. This approach relies on a multi-level target decomposition and a hybrid model combining Convolutional Neural Network (CNN) and Support Vector Regression (SVR). Initially, a multi-level target analysis method is proposed, grounded in the hierarchical decomposition of vehicle road noise along the chassis parts, delineated layer by layer, in accordance w

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Yan, Haohao, Yiding Han, Xiaoxiao Shan, et al. "Breaking the Fear Barrier: Aberrant Activity of Fear Networks as a Prognostic Biomarker in Patients with Panic Disorder Normalized by Pharmacotherapy." Biomedicines 11, no. 9 (2023): 2420. http://dx.doi.org/10.3390/biomedicines11092420.

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Panic disorder (PD) is a prevalent type of anxiety disorder. Previous studies have reported abnormal brain activity in the fear network of patients with PD. Nonetheless, it remains uncertain whether pharmacotherapy can effectively normalize these abnormalities. This longitudinal resting-state functional magnetic resonance imaging study aimed to investigate the spontaneous neural activity in patients with PD and its changes after pharmacotherapy, with a focus on determining whether it could predict treatment response. The study included 54 drug-naive patients with PD and 54 healthy controls (HC

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Chen, Jialin, and Hanyin Yang. "A CSI 300 Index Prediction Model Based on PSO-SVR-GRNN Hybrid Method." Mobile Information Systems 2022 (August 4, 2022): 1–10. http://dx.doi.org/10.1155/2022/7419920.

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In this article, a PSO-SVR-GRNN nonparametric hybrid model is proposed for the CSI 300 stock index to forecast the problem. Particle Swarm Optimization (PSO) is utilized to optimize the parameters of the SVR model to enhance the prediction ability of the support vector machine's regression model for the original CSI 300 Index time series. The optimized residual sequence prediction results of the General Regression Neural Network (GRNN) are then used to optimize the time series prediction. The outcomes indicate that the PSO- SVR-GRNN model can greatly improve the prediction accuracy of the CSI

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Dong, Luofan, Huaqiang Du, Ning Han, et al. "Application of Convolutional Neural Network on Lei Bamboo Above-Ground-Biomass (AGB) Estimation Using Worldview-2." Remote Sensing 12, no. 6 (2020): 958. http://dx.doi.org/10.3390/rs12060958.

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Above-ground biomass (AGB) directly relates to the productivity of forests. Precisely, AGB mapping for regional forests based on very high resolution (VHR) imagery is widely needed for evaluation of productivity. However, the diversity of variables and algorithms and the difficulties inherent in high resolution optical imagery make it complex. In this paper, we explored the potentials of the state-of-art algorithm convolutional neural networks (CNNs), which are widely used for its high-level representation, but rarely applied for AGB estimation. Four experiments were carried out to compare the

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Gao, Zhanpeng, and Wenjun Yi. "Prediction of Projectile Interception Point and Interception Time Based on Harris Hawk Optimization–Convolutional Neural Network–Support Vector Regression Algorithm." Mathematics 13, no. 3 (2025): 338. https://doi.org/10.3390/math13030338.

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In modern warfare, the accurate prediction of the intercept time and intercept point of the interceptor is the key to achieving penetration. Aiming at this problem, firstly, a convolutional neural network (CNN) is used to automatically extract high-level features from the data, and then these features are used as the input of support vector regression (SVR) for regression prediction. The Harris Hawk optimization (HHO) is used to optimize the hyperparameters of SVR, and the HHO-CNN-SVR algorithm is proposed. In order to verify the effectiveness of the algorithm for the prediction of the interce

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Bao, Chen, Yongwei Miao, Jiazhou Chen, and Xudong Zhang. "Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions." Applied Sciences 13, no. 18 (2023): 10317. http://dx.doi.org/10.3390/app131810317.

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With the increasing demand for intelligent custom clothing, the development of highly accurate human body dimension prediction tools using artificial neural network technology has become essential to ensuring high-quality, fashionable, and personalized clothing. Although support vector regression (SVR) networks have demonstrated state-of-the-art (SOTA) performances, they still fall short on prediction accuracy and computation efficiency. We propose a novel generalized regression forecasting network (GRFN) that incorporates kernel ridge regression (KRR) within a multi-strategy multi-subswarm pa

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Alfakhri, Rezky, Inggih Permana, Rice Novita, and M. Afdal. "Prediksi Produksi Kelapa Sawit Menggunakan Algoritma Support Vector Regression dan Recurrent Neural Network." Building of Informatics, Technology and Science (BITS) 6, no. 3 (2024): 2102–10. https://doi.org/10.47065/bits.v6i3.6441.

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Oil palm is one of the important plantation crops and a leading commodity in Indonesia. PT. XYZ is a company engaged in receiving Fresh Fruit Bunches (FFB) to be processed into Crude Palm Oil (CPO) and Palm Kernel (PK). So far, the company has conducted statistical analysis with a correction value of 5% - 12% on the production results each month in targeting production results. However, this method is still lacking, because it uses manual calculations and considers estimates from personal experience. Therefore, this research proposes a data mining technique with Support Vector Regression (SVR)

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Ampountolas, Apostolos. "Forecasting Orange Juice Futures: LSTM, ConvLSTM, and Traditional Models Across Trading Horizons." Journal of Risk and Financial Management 17, no. 11 (2024): 475. http://dx.doi.org/10.3390/jrfm17110475.

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This study evaluated the forecasting accuracy of various models over 5-day and 10-day trading horizons to predict the prices of orange juice futures (OJ = F). The analysis included traditional models like Autoregressive Integrated Moving Average (ARIMA) and advanced neural network models such as Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), and Convolutional Long Short-Term Memory (ConvLSTM), incorporating factors like the Commodities Index and the S&P500 Index. We employed loss function metrics and va

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Ghose, Bikramjeet, Pramit Pandit, Chiranjit Mazumder, Kanchan Sinha, and Pradip Kumar Sahu. "Comparative Study of EMD based Modelling Techniques for Improved Agricultural Price Forecasting." Journal of the Indian Society of Agricultural Statistics 78, no. 1 (2024): 53–62. http://dx.doi.org/10.56093/jisas.v78i1.7.

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Forecasting agricultural commodity prices is regarded as a challenging task due to its non-linear and non-stationary nature. As agriculture productionis highly reliant on various biological and agro-meteorological factors, traditional smoothing techniques as well as statistical models often fail tomodel such series satisfactorily. To capture such complex patterns effectively, different data-driven and self-adaptive techniques have been developedtime-to-time. Against this backdrop, in this paper, we have assessed the suitability of empirical mode decomposition (EMD)-based neural network andsupp

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Belayneh, A., and J. Adamowski. "Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/794061.

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Drought forecasts can be an effective tool for mitigating some of the more adverse consequences of drought. Data-driven models are suitable forecasting tools due to their rapid development times, as well as minimal information requirements compared to the information required for physically based models. This study compares the effectiveness of three data-driven models for forecasting drought conditions in the Awash River Basin of Ethiopia. The Standard Precipitation Index (SPI) is forecast and compared using artificial neural networks (ANNs), support vector regression (SVR), and wavelet neura

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Singh, Abhilash, J. Amutha, Jaiprakash Nagar, Sandeep Sharma, and Cheng-Chi Lee. "LT-FS-ID: Log-Transformed Feature Learning and Feature-Scaling-Based Machine Learning Algorithms to Predict the k-Barriers for Intrusion Detection Using Wireless Sensor Network." Sensors 22, no. 3 (2022): 1070. http://dx.doi.org/10.3390/s22031070.

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The dramatic increase in the computational facilities integrated with the explainable machine learning algorithms allows us to do fast intrusion detection and prevention at border areas using Wireless Sensor Networks (WSNs). This study proposed a novel approach to accurately predict the number of barriers required for fast intrusion detection and prevention. To do so, we extracted four features through Monte Carlo simulation: area of the Region of Interest (RoI), sensing range of the sensors, transmission range of the sensor, and the number of sensors. We evaluated feature importance and featu

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Shi, Zhi Biao, and Ying Miao. "Prediction Research on the Failure of Steam Turbine Based on Fruit Fly Optimization Algorithm Support Vector Regression." Advanced Materials Research 614-615 (December 2012): 409–13. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.409.

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In order to solve the blindness of the parameter selection in the Support Vector Regression (SVR) algorithm, we use the Fruit Fly Optimization Algorithm (FOA) to optimize the parameters in SVR, and then propose the optimization algorithm on the parameters in SVR based on FOA to fitting and simulate the experimental data of the turbine’s failures. This algorithm could optimize the parameters in SVR automatically, and achieve ideal global optimal solution. By comparing with the commonly used methods such as Support Vector Regression and Radial Basis Function neural network, it can be shown that

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Jaddi, Hajar, Abdellah El-Hmaidi, Habiba Ousmana, et al. "Predicting Nitrate Levels in the Saïss Water Table: A Comparative Study of Machine Learning Methods." BIO Web of Conferences 115 (2024): 03001. http://dx.doi.org/10.1051/bioconf/202411503001.

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The main goal of this study is to predict nitrate (NO3-) levels in the Saiss basin water table as a function of various physicochemical parameters. To accomplish this, three machine learning approaches were utilized: multiple linear regression (MLR), super vector regression (SVR), and artificial neural networks (ANN). The independent variables were composed of six water quality parameters, including Ca2+, Na2+, EC, Cl-, HCO3-, and SO42-. The study utilized a dataset of 389 water samples collected between 1991 and 2017. The artificial neural network (ANN) was trained using the Levenberg-Marquar

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Tang, J. L., C. Z. Cai, X. J. Zhu, G. L. Wang, and D. F. Cao. "Modeling and Predicting Tensile Strength of Tungsten Alloy by Using PSO-SVR." Advanced Materials Research 455-456 (January 2012): 1497–503. http://dx.doi.org/10.4028/www.scientific.net/amr.455-456.1497.

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In this paper, the support vecstor regression (SVR) approach combined with particle swarm optimization (PSO) is proposed to establish a model for predicting tungsten tensile strength base on the tension experimental data of tungsten alloy under two influential factors, including tungsten content and deformation magnitude. Comparing the prediction result of PSO-SVR model with that of back propagation neural network (BPNN) model, it is shown that the prediction precision of SVR model is higher evaluated by identical training and test samples. The mean absolute error (MAE), mean absolute percenta

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Hasan, Khairul Kamarudin, Muhammad Asraf Hairuddin, Rijalul Fahmi Mustapa, Siti Aminah Nordin, and Nur Dalila Khirul Ashar. "Machine Learning Approach of Optimal Frequency Tuning for Capacitive Wireless Power Transfer System." International Journal of Emerging Technology and Advanced Engineering 12, no. 11 (2022): 65–71. http://dx.doi.org/10.46338/ijetae1122_07.

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Wireless power transmission has become a remarkable research topic due to its enormous application potential. Recent advances in machine learning have been shown to be the most promising approach that offers significant capabilities in the wireless power transfer system (WPTS) for selecting the optimal frequency tuning to achieve high efficiency performance. However, developing an automated frequency-tuned system remains a challenge. In this study, a novel frequency-tuned method is presented that utilises machine learning-based models such as neural networks (NN), support vector regression (SV

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Chen, Zhao, Zhibin Sun, Huaiqing Zhang, Huacong Zhang, and Hanqing Qiu. "Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data." Remote Sensing 15, no. 24 (2023): 5653. http://dx.doi.org/10.3390/rs15245653.

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Accurate forest biomass estimation serves as the foundation of forest management and holds critical significance for a comprehensive understanding of forest carbon storage and balance. This study aimed to integrate Landsat 8 OLI and Sentinel-1A SAR satellite image data and selected a portion of the Shanxia Experimental Forest in Jiangxi Province as the study area to establish a biomass estimation model by screening influencing factors. Firstly, we extracted spectral information, vegetation indices, principal component features, and texture features within 3 × 3-pixel neighborhoods from Landsat

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Zhang, Qisong, Lei Yan, Runbo Hu, Yingqiu Li, and Li Hou. "Regional Economic Prediction Model Using Backpropagation Integrated with Bayesian Vector Neural Network in Big Data Analytics." Computational Intelligence and Neuroscience 2022 (February 16, 2022): 1–10. http://dx.doi.org/10.1155/2022/1438648.

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Forecasting economic growth is critical for formulating national economic development policies. Neural Networks are a type of artificial intelligence that may be used to model complex target functions. ANN (Artificial Neural Networks) are one of the most effective learning approaches now available for specific sorts of tasks, such as learning to understand complex real-world sensor data. This paper proposes the regional economic prediction model based on neural networks techniques. Bayesian vector neural network (BVNN) is integrated with backpropagation (BP) model. The database has been collec

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Stępień, Igor, Rafał Obuchowicz, Adam Piórkowski, and Mariusz Oszust. "Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment." Sensors 21, no. 4 (2021): 1043. http://dx.doi.org/10.3390/s21041043.

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The quality of magnetic resonance images may influence the diagnosis and subsequent treatment. Therefore, in this paper, a novel no-reference (NR) magnetic resonance image quality assessment (MRIQA) method is proposed. In the approach, deep convolutional neural network architectures are fused and jointly trained to better capture the characteristics of MR images. Then, to improve the quality prediction performance, the support vector machine regression (SVR) technique is employed on the features generated by fused networks. In the paper, several promising network architectures are introduced,

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Rezzy, Eko Caraka, Abu Bakar Sakhinah, Tahmid Muhammad, Yasin Hasbi, and Dwi Kurniawan Isma. "Neurocomputing fundamental climate analysis." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 4 (2019): 1818–27. https://doi.org/10.12928/TELKOMNIKA.v17i4.11788.

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Rainfall is a natural phenomenon that needs to be studied more deeply and interesting to be analyzed. It involves numbers of human activities such as aviation, agriculture, fisheries, and also disaster risk reduction. Moreover, the characteristics of rainfall data follows seasonality, fluctuation, not normally distributed and it makes traditional time series challenging to use. Therefore, neurocomputing model can be used as an alternative to extraction information from rainfall data and give high performance also accuracy. In this paper, we give short preview about SST Anomalies in Manado, Nor

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Feng, Wenyan, and Fan Feng. "Research on the Multimodal Digital Teaching Quality Data Evaluation Model Based on Fuzzy BP Neural Network." Computational Intelligence and Neuroscience 2022 (June 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/7893792.

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We propose in this paper a fuzzy BP neural network model and DDAE-SVR deep neural network model to analyze multimodal digital teaching, establish a multimodal digital teaching quality data evaluation model based on a fuzzy BP neural network, and optimize the initial weights and thresholds of BP neural network by using adaptive variation genetic algorithm. Since the BP neural network is highly dependent on the initial weights and points, the improved genetic algorithm is used to optimize the initial weights and thresholds of the BP neural network, reduce the time for the BP neural network to fi

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Guo, Tuo, Fengjie Xu, Jinfang Ma, and Fahuan Ge. "Component Prediction of Antai Pills Based on One-Dimensional Convolutional Neural Network and Near-Infrared Spectroscopy." Journal of Spectroscopy 2022 (December 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/6875022.

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Convolutional neural networks (CNNs) are widely used for image recognition and text analysis and have been suggested for application on one-dimensional data as a way to reduce the need for preprocessing steps. In this study, the performance of one-dimensional convolutional neural network (1DCNN) machine learning algorithm was investigated for regression analysis of Antai pills spectral data. This algorithm was compared with other chemometric methods, including support vector machine regression (SVR) and partial least-square regression (PLSR) methods. The results showed that the 1DCNN model out

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Jondhale, Satish R., Vijay Mohan, Bharat Bhushan Sharma, Jaime Lloret, and Shashikant V. Athawale. "Support Vector Regression for Mobile Target Localization in Indoor Environments." Sensors 22, no. 1 (2022): 358. http://dx.doi.org/10.3390/s22010358.

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Trilateration-based target localization using received signal strength (RSS) in a wireless sensor network (WSN) generally yields inaccurate location estimates due to high fluctuations in RSS measurements in indoor environments. Improving the localization accuracy in RSS-based systems has long been the focus of a substantial amount of research. This paper proposes two range-free algorithms based on RSS measurements, namely support vector regression (SVR) and SVR + Kalman filter (KF). Unlike trilateration, the proposed SVR-based localization scheme can directly estimate target locations using fi

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Song, Shuang, Shugang Li, Tianjun Zhang, Li Ma, Shaobo Pan, and Lu Gao. "Research on a Multi-Parameter Fusion Prediction Model of Pressure Relief Gas Concentration Based on RNN." Energies 14, no. 5 (2021): 1384. http://dx.doi.org/10.3390/en14051384.

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The effective prediction of gas concentration and the reasonable formulation of corresponding safety measures have important significance for improving the level of coal mine safety. To improve the accuracy of gas concentration prediction and enhance the applicability of the models, this paper starts with actual coal mine production monitoring data, improves the accuracy of gas concentration prediction through multi-parameter fusion prediction, and constructs a recurrent neural network (RNN)-based multi-parameter fusion prediction of coal face gas concentration. We determined the performance e

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Zhao, Guoyan, Meng Wang, and Weizhang Liang. "A Comparative Study of SSA-BPNN, SSA-ENN, and SSA-SVR Models for Predicting the Thickness of an Excavation Damaged Zone around the Roadway in Rock." Mathematics 10, no. 8 (2022): 1351. http://dx.doi.org/10.3390/math10081351.

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Due to the disturbance effect of excavation, the original stress is redistributed, resulting in an excavation damaged zone around the roadway. It is significant to predict the thickness of an excavation damaged zone because it directly affects the stability of roadways. This study used a sparrow search algorithm to improve a backpropagation neural network, and an Elman neural network and support vector regression models to predict the thickness of an excavation damaged zone. Firstly, 209 cases with four indicators were collected from 34 mines. Then, the sparrow search algorithm was used to opt

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Liu, Yajun, Shenchao Zhang, and Zhendong Liu. "Machine learning approach to improve vapor recovery: Prediction and frequency converter with a new vapor recovery system." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 236, no. 5 (2021): 2079–88. http://dx.doi.org/10.1177/09544062211027199.

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In practice, the volatile organic compounds (VOCs) pollution can exist when refueling due to the properties of the gasoline, low viscosity and high saturated-vapor pressure. A new gasoline vapor recovery system involving frequency conversion technology and machine learning is developed to cope with this problem. In the proposed system, firstly, the pumping capacity of the vacuum pump is evaluated, and test shows an almost linear relationship between suction volume and frequency. Then, the Multi-Layer Perception (MLP) neural network and the support vector regression (SVR) are employed to predic

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