Integrating Copula-Based Random Forest and Deep Learning Approaches for Analyzing Heterogeneous Treatment Effects in Survival Analysis by Jong-Min Kim

“…Model performance is evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Concordance statistic (C-statistic), Average Treatment Effect (ATE), and Conditional Average Treatment Effect (CATE) by race. …”
Get full text

Enhanced Inversion of Sound Speed Profile Based on a Physics-Inspired Self-Organizing Map by Guojun Xu, Ke Qu, Zhanglong Li, Zixuan Zhang, Pan Xu, Dongbao Gao, Xudong Dai

“…The PISOM has an SSP reconstruction error of less than 2 m/s in 25% of cases, while the SOM method has none. …”
Get full text

Approximate Analytic Solutions of Transient Nonlinear Heat Conduction with Temperature-Dependent Thermal Diffusivity by M. T. Mustafa, A. F. M. Arif, Khalid Masood

“…It is based on an effective combination of Lie symmetry method, homotopy perturbation method, finite element method, and simulation based error reduction techniques. …”
Get full text

Fault Detection Algorithm for Gaussian Mixture Noises: An Application in Lidar/IMU Integrated Localization Systems by Penggao Yan, Zhengdao Li, Feng Huang, Weisong Wen, Li-Ta Hsu

“…Experimental results in a simulated urban environment show that the proposed method exhibits a 30% improvement in the detection rate and a 17%–23% reduction in the detection delay, compared with the conventional method with Gaussian noise modeling.…”
Get full text

The Gust Factor Models Involving Wind Speed and Temperature Profiles for Wind Gust Estimation by Haichuan Hu, Chuanhai Qian, Shibo Gao

“…The GF-L method reduced the root mean square error by 5.1% on average compared to the GF method, while the GF-M method achieved a 9.2% reduction. …”
Get full text

Source localization based on time delay estimation in complex environment by Da-wei ZHANG, Chang-chun BAO, Bing-yin XIA

“…In order to improve the performance of source localization in noisy and reverberant environments,a novel time delay estimation (TDE) method was proposed.This method is called acoustical transfer function ratio based on statistical model (ATFR-SM).In the proposed algorithm,the noise reduction method based on the statistical model was adopted to reduce the effect of noise on acoustical transfer Function (ATF).In the ATF method,the power spectral density (PSD) was smoothed and whitened to reduce the effect of reverberations.voice activity detection (VAD) was used to distinguish the speech period from the noise period,and the TDE was performed in the speech period to improve the estimation accuracy.Moreover,the proposed TDE method and the linear closed-form method for source localization were combined to constitute a source localization system.The results of performance evaluation show that,in both the noisy and reverberant conditions,the lower percentage of abnormal points (PAP) and lower root mean square error (RMSE) can be achieved by the proposed TDE method than those of the reference methods.Meanwhile,the source localization has higher accuracy than the reference methods.…”
Get full text

Extending measurement range for three-dimensional structured light imaging with digital exponential fringe pattern by Abel, Kamagara

“…Experimental simulations and results show that within the desired region of defocus or at an extended measurement range, the proposed method exhibits a 45% comparative reduction in root-mean-square phase error hence improvement in final measurement result.…”
Get full text

Non-Contact Oxygen Saturation Estimation Using Deep Learning Ensemble Models and Bayesian Optimization by Andrés Escobedo-Gordillo, Jorge Brieva, Ernesto Moya-Albor

“…Thus, by leveraging Bayesian optimization for hyperparameter tuning and integrating a Bagging Ensemble, we achieved a significant reduction in the training error (bias), achieving a better generalization over the test set, and reducing the variance in comparison with the baseline model for SpO₂ estimation.…”
Get full text

Improving prediction accuracy in agricultural markets through the CIMA-AttGRU model. by Yankun Jiang, Jinhui Liu, Xiaotuan Li

“…Our empirical results demonstrate a significant improvement in forecasting precision, with the CIMA-AttGRU model achieving a Mean Absolute Error (MAE) reduction of 15% and a Mean Squared Error (MSE) reduction of 20% compared to conventional models. …”
Get full text

Enhancing Streamflow Prediction Accuracy: A Comprehensive Analysis of Hybrid Neural Network Models with Runge–Kutta with Aquila Optimizer by Rana Muhammad Adnan, Wang Mo, Ahmed A. Ewees, Salim Heddam, Ozgur Kisi, Mohammad Zounemat-Kermani

“…Results show that the LSTM-RUNAO model outperformed conventional ANN methods, achieving a 28.7% reduction in root mean square error (RMSE) and a 20.3% reduction in mean absolute error (MAE) compared to standard ANN models. …”
Get full text

Simulation of incremental update of electronic document information based on big data technology by Zhiyuan Jin, Qi Zhang, Tiejun Pan

“…Abstract Experimental results show that the algorithm converges faster and has a smaller average error compared to the BP algorithm and the attribute reduction algorithm. …”
Get full text

Designing a Neural Observer to Estimate the State Variables of the Dynamical System of a Specific Class of Leukaemia by Yousef Farshidi, Reza Ghasemi, Aminin Sharafian Ardekani

“…The better performance of a neural observer would be apparent in comparison with a classical high gain observer. Applying this method for estimating the state variables of cell dynamics results in a reduction in the number of tests and the required samples, which will consequently reduce costs and prevent wasting leukemic patients’ time.…”
Get full text

Efficient multi-station air quality prediction in Delhi with wavelet and optimization-based models. by Lakshmi Sankar, Krishnamoorthy Arasu

“…The AquaWave-BiLSTM framework demonstrated exceptional predictive accuracy, with a Mean Squared Error (MSE) of 0.00065, a Mean Absolute Error (MAE) of 0.04566, a Root Mean Square Error (RMSE) of 0.02523, and an R² value of 0.9494, surpassing conventional methodologies. …”
Get full text

Improving of the Generation Accuracy Forecasting of Photovoltaic Plants Based on <i>k</i>-Means and <i>k</i>-Nearest Neighbors Algorithms by P. V. Matrenin, A. I. Khalyasmaa, V. V. Gamaley, S. A. Eroshenko, N. A. Papkova, D. A. Sekatski, Y. V. Potachits

“…In this case, unsupervised learning is first performed using the k-means method to form clusters. For this, it is also proposed to use studied the feature space dimensionality reduction algorithm to visualize and estimate the clustering accuracy. …”
Get full text

O-Arm Imaging With Real-Time Control for Organ Motion Tracking: A Feasibility Study by Ashkan Ghorbanian, Mobin Salehi, Mohammad Sajad Sokout, Borhan Beigzadeh

“…Tracking the rigid motion of patients may lead to a reduction of the search area in the autofocus correction method for compensating deformable motion, which can directly impact computational efforts. …”
Get full text

SP-RF-ARIMA: A sparse random forest and ARIMA hybrid model for electric load forecasting by Kamran Hassanpouri Baesmat, Farhad Shokoohi, Zeinab Farrokhi

“…This methodology, termed SP-RF-ARIMA, is evaluated against existing approaches; it demonstrates more than 40% reduction in mean absolute error and root mean square error compared to the second-best method.…”
Get full text

A Hybrid Learning Framework for Enhancing Bridge Damage Prediction by Amal Abdulbaqi Maryoosh, Saeid Pashazadeh, Pedram Salehpour

“…The proposed approach combines pixel-based feature method local binary pattern (LBP) with the mid-level feature bag of visual words (BoVW) for feature extraction, followed by the Apriori algorithm for dimensionality reduction and optimal feature selection. …”
Get full text

Low Power OFDM Receiver Exploiting Data Sparseness and DFT Symmetry by Nikos Petrellis

“…Simulations were performed for two Quadrature Amplitude Modulation orders (16-QAM and 32-QAM), two options for the FFT size (1024 and 4096), two alternative input symbol structures for the inverse FFT (IFFT), and several sparseness levels and samples substitution options. The Symbol Error Rate (SER) and image reconstruction examples are used to show that a full reconstruction or a very low error can be achieved. …”
Get full text

Improving the criteria of electricity consumptionforecasting in petrochemical industrial units based ondeep learning by Ehsan Tavakoli Garmaserh, Mehran Emadi

“…Experimental evaluations using benchmark datasets demonstrate significant improvements, achieving a Root Mean Square Error (RMSE) of 0.0693 and a Mean Absolute Percentage Error (MAPE) reduction of over 15% compared to state-of-the-art methods. …”
Get full text

Evaluating Predictive Accuracy of Regression Models with First-Order Autoregressive Disturbances: A Comparative Approach Using Artificial Neural Networks and Classical Estimators by Rauf I. Rauf, Masad A. Alrasheedi, Rasheedah Sadiq, Abdulrahman M. A. Aldawsari

“…The analysis is structured into three phases: the first phase examines predictive accuracy across methods using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE); the second phase evaluates the efficiency of parameter estimation based on standard errors across methods; and the final phase visually assesses the closeness of predicted values to actual values through scatter plots. …”
Get full text