Energy Consumption Prediction of Building Heating Systems Based on Model Identification Methods by 曲明璐, 杜尚赫, 张欣林, 于 震, 李 怀

“…During deployment, the predicted R2 value and total energy consumption error were 0.87 and 5.18%, respectively. …”
Get full text

Artificial neural networks employment in the prediction of evapotranspiration of greenhouse-grown sweet pepper by Héliton Pandorfi, Alan C. Bezerra, Roberto T. Atarassi, Frederico M. C. Vieira, José A. D. Barbosa Filho, Cristiane Guiselini

“…ABSTRACT This study aimed to investigate the applicability of artificial neural networks (ANNs) in the prediction of evapotranspiration of sweet pepper cultivated in a greenhouse. …”
Get full text

Prognosis method to predict small-sized breast cancer affected by fibrocystic disease by S. A. Velichko, E. M. Slonimskaya, I. G. Frolova, D. G. Bukharin, A. V. Doroshenko

Get full text

A Large-Scale Assessment of Nucleic Acids Binding Site Prediction Programs. by Zhichao Miao, Eric Westhof

“…Several strategies have been proposed, but the state-of-the-art approaches display a great diversity in i) the definition of nucleic acid binding sites; ii) the training and test datasets; iii) the algorithmic methods for the prediction strategies; iv) the performance measures and v) the distribution and availability of the prediction programs. …”
Get full text

Dynamic feature selection for silicon content prediction in blast furnace using BOSVRRFE by Junyi Duan

“…Abstract Accurate prediction of silicon content in blast furnace ironmaking is essential for optimizing furnace temperature control and production efficiency. …”
Get full text

Singular Spectrum Analysis With Conditional Predictions for Real‐Time State Estimation and Forecasting by H. Reed Ogrosky, Samuel N. Stechmann, Nan Chen, Andrew J. Majda

“…A modified version of the traditional SSA algorithm, referred to as SSA with conditional predictions (SSA‐CP), is presented to address these issues. …”
Get full text

Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism by Yang Chen, Zeyang Tang, Yibo Cui, Wei Rao, Yiwen Li

“…The accurate estimation and prediction of charging demand are crucial for the planning of charging infrastructure, grid layout, and the efficient operation of charging networks. …”
Get full text

Exploring the Limitations of Peripheral Blood Transcriptional Biomarkers in Predicting Influenza Vaccine Responsiveness by Luca Marchetti, Emilio Siena, Mario Lauria, Denise Maffione, Nicola Pacchiani, Corrado Priami, Duccio Medini

“…Particular attention has been paid to the identification of early signatures capable of predicting vaccine immunogenicity. Building from previous studies, we employed a recently established algorithm for signature-based clustering of expression profiles, SCUDO, to provide new insights into why blood-derived transcriptome biomarkers often fail to predict the seroresponse to the influenza virus vaccination. …”
Get full text

Improving parking availability prediction in smart cities with IoT and ensemble-based model by Stéphane Cédric Koumetio Tekouabou, El Arbi Abdellaoui Alaoui, Walid Cherif, Hassan Silkan

“…We propose in this paper a new system that integrates the IoT and a predictive model based on ensemble methods to optimize the prediction of the availability of parking spaces in smart parking. …”
Get full text

Prediction model for oil seal performance parameters based on PSO-MLP-KAN by Weixing Yan, Mingshuo Shi, Pengbo Xiao, Kui Zhang, Xin Wu

“…To accurately, efficiently, and stably predict the sealing performance of oil seals, this study proposes a prediction method based on a combination of the Kolmogorov–Arnold network (KAN) and multi-layer perceptron (MLP) optimized by particle swarm optimization (PSO) algorithm. …”
Get full text

Vehicle Position Updating Strategy Based on Kalman Filter Prediction in VANET Environment by Yuanfu Mo, Dexin Yu, Jun Song, Kun Zheng, Yajuan Guo

“…This paper proposes vehicle position data updating strategy with packet repetition based on Kalman filter predicting. Firstly, we design a position data updating model based on Kalman filter difference predicting equations. …”
Get full text

Prediction of Ground-Level Air Pollution Using Artificial Neural Network in Tehran by Afshin Khoshand, Mahshid Shahbazi Sehrani, Hamidreza Kamalan, Siamak Bodaghpour

“…Training the models was on the basis of Multi-Layer Perceptron (MLP) with the Back Propagation (BP) algorithm using MATLAB program. The results indicated appropriate agreement between the observed and predicted concentrations, as the values of the coefficient of multiple determinations (R2) for all models were more than 0.83. …”
Get full text

Quantitative phase imaging with temporal kinetics predicts hematopoietic stem cell diversity by Takao Yogo, Yuichiro Iwamoto, Hans Jiro Becker, Takaharu Kimura, Reiko Ishida, Ayano Sugiyama-Finnis, Tomomasa Yokomizo, Toshio Suda, Sadao Ota, Satoshi Yamazaki

“…By analyzing the cellular kinetics of individual HSCs, we discovered previously undetectable diversity that snapshot analysis cannot resolve. The QPI-driven algorithm quantitatively evaluates stemness at the single-cell level and leverages temporal information to significantly improve prediction accuracy. …”
Get full text

Stability Prediction of Residual Soil and Rock Slope Using Artificial Neural Network by Mahesh Paliwal, Himkar Goswami, Arunava Ray, Ashutosh Kumar Bharati, Rajesh Rai, Manoj Khandelwal

“…Nonlinear equations have been formulated by coding the artificial neural network algorithm. An android application has also been developed to predict the stability of residual soil and rock slope instantly. …”
Get full text

A novel approach for predicting the standardised precipitation index considering climatic factors by Mustafa A. Alawsi, Salah L. Zubaidi, Laith B. Al-badranee

“…Also, it was found that the PSO algorithm precisely predicts the parameters of the proposed model. …”
Get full text

Passenger Flow Prediction of Integrated Passenger Terminal Based on K-Means–GRNN by Yifan Tan, Haixu Liu, Yun Pu, Xuemei Wu, Yubo Jiao

“…In this paper, the passenger flow GRNN prediction model is proposed, based on the K-means cluster algorithm, and an improved index named BWPs (Between-Within Proportion-Similarity) is proposed to improve the clustering effect of K-means so that the clustering effect of the new index is verified. …”
Get full text

Research on Financial Stock Market Prediction Based on the Hidden Quantum Markov Model by Xingyao Song, Wenyu Chen, Junyi Lu

“…Experimental results demonstrate that the proposed quantum model outperforms classical algorithmic models in handling higher complexity, achieving improved efficiency, reduced computation time, and superior predictive performance. …”
Get full text

Using Hybrid Artificial Intelligence Approaches to Predict the Fracture Energy of Concrete Beams by Qinghua Xiao, Congming Li, Shengxiang Lei, Xiangyu Han, Qiaofeng Chen, Zemin Qiu, Biao Sun

“…In this study, artificial intelligence approaches were tried to seek a feasible way to solve these prediction issues. Firstly, the ridge regression (RR), the classification and regression tree (CART), and the gradient boosting regression tree (GBRT) were selected to construct the predictive models. …”
Get full text

Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon by Moumita Saha, Arun Chakraborty, Pabitra Mitra

“…In this article, we propose a joint-clustering of monsoon years and predictors for understanding and predicting the monsoon. This is achieved by subspace clustering algorithm. …”
Get full text

Research on Accelerated Degradation Test Design and Life Prediction for Aluminum Electrolytic Capacitor by YANG Tao, WANG Xu, XIAO Jianglin

“…By comparing the degradated capacitance predicted by BP neural network with the measured data of the degradation test and the predicted value of the least squares linear fitting of the experimental data, the results show that the prediction error of capacitance based on the BP neural network is within 3%, while the predicted value of the least squares linear fitting is around 6%, which verifies the superiority of the BP neural network life prediction algorithm, and provides strong support for development and application of the subsequent on-line monitoring technology of board level capacitors.…”
Get full text