An Oracle Normalized Least Mean Square (NLMS) Algorithm and a Simple Bayesian Detection NLMS Algorithm Robust to Impulse Noise by Hadi Zayyani, Yaser Attar

“…Initially, to have a fast algorithm, an optimization problem is introduced and then an oracle NLMS algorithm is devised. It has the largest reduction in misalignment error at each iteration with respect to the previous iteration. …”
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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. …”
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Reduced-Order Models and Conditional Expectation: Analysing Parametric Low-Order Approximations by Hermann G. Matthies

“…Similarly, in the field of machine learning, a function mapping the parameter set to the image space of the machine learning model is learned from a training set of samples, typically minimising the mean square error. This set may be seen as a sample from some probability distribution, and thus the training is an approximate computation of the expectation, giving an approximation of the conditional expectation—a special case of Bayesian updating, where the Bayesian loss function is the mean square error. …”
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A Hybrid Smoothed Finite Element Method for Predicting the Sound Field in the Enclosure with High Wave Numbers by Haitao Wang, Xiangyang Zeng, Ye Lei

“…This method employs the smoothing technique to realize the reduction of the numerical dispersion. By constructing a type of mixed smoothing domain, the traditional node-based and face-based smoothing techniques are mixed in the hybrid SFEM to give a more accurate stiffness matrix, which is widely believed to be the ultimate cause for the numerical dispersion error. …”
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Low Overhead Qutrit Magic State Distillation by Shiroman Prakash, Tanay Saha

“…We show that using qutrits rather than qubits leads to a substantial reduction in the overhead cost associated with an approach to fault-tolerant quantum computing known as magic state distillation. …”
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Hourly and Day Ahead Power Prediction of Building Integrated Semitransparent Photovoltaic System by S. Kaliappan, R. Saravanakumar, Alagar Karthick, P. Marish Kumar, V. Venkatesh, V. Mohanavel, S. Rajkumar

“…The performance metrics of the errors are analysed such as the root mean square error (RMSE), mean absolute percentage error (MAPE), and mean square root (MSE). …”
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Forecasting Forex EUR/USD Closing Prices Using a Dual-Input Deep Learning Model with Technical and Fundamental Indicators by Abolfazl Saghafi, Maryam Bagherian, Farhad Shokoohi

“…The model outperforms the second-best model, achieving a 29% reduction in mean absolute error (MAE) and root mean squared error (RMSE) in the training set and reductions of 24% and 23% in MAE and RMSE, respectively, in the test set. …”
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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. …”
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High-precision position control of hydraulic support pushing system based on quasi-sliding mode by GAO Yuhao, SUN Xing, LI Yang, LIU Wei, LI Jingyan

“…In the sinusoidal response, stable tracking was achieved within 0.2 s, with a peak error of about 0.001 m, representing a reduction of approximately 94.7%, and it exhibited broader bandwidth characteristics. …”
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Deep Learning Model for Real‐Time Flood Forecasting in Fast‐Flowing Watershed by Fan Wang, Jie Mu, Cheng Zhang, Weiqi Wang, Wuxia Bi, Wenqing Lin, Dawei Zhang

“…This leads to an average increase in Nash efficiency of approximately 7.86% and a reduction in the interquartile range of relative peak error by about 30.7%. …”
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End-to-end neural automatic speech recognition system for low resource languages by Sami Dhahbi, Nasir Saleem, Sami Bourouis, Mouhebeddine Berrima, Elena Verdú

“…Using synthetic speech and data augmentation techniques can enhance E2E-ASR performance for low-resource languages, reducing word error rates (WERs) and character error rates (CERs). …”
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Advancing smart communities with a deep learning framework for sustainable resource management. by Yongyan Zhao

“…Predictive models received assessment based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². …”
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A Physics‐Informed Deep Learning Framework for Estimating Thermal Stratification in a Large Deep Reservoir by Yuan He, Xiaofan Yang

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A SAR wave-enhanced method combining denoising and texture enhancement for bathymetric inversion by Aijun Cui, Yi Ma, Jingyu Zhang, Ruifu Wang

“…The proposed method improved bathymetric accuracy, reducing mean absolute error (MAE) by up to 4.69 m and mean relative error (MRE) by up to 18 %. …”
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Texture image segmentation based on geometric classification and assessment density of contour elements by H. M. Alzakki, V. Yu. Tsviatkou

“…The proposed method in comparison with the method based on energy maps, providing a reduction in the error of localization of textural regions by taking into account the geometric characteristics of the elements.…”
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Enhanced Next Generation Millimeter-Wave Multicarrier System with Generalized Frequency Division Multiplexing by Hidekazu Shimodaira, Joongheon Kim, Ali S. Sadri

“…This paper studies the performance improvements in terms of PAPR reduction for GFDM. Based on the performance results, the optimal numbers of subcarriers and subsymbols are calculated for PAPR reduction while minimizing the Bit Error Rate (BER) performance degradation. …”
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Accurate Wideband RCS Estimation from Limited Field Data Using Infinitesimal Dipole Modeling with Compressive Sensing by Jeong-Wan Lee, Ye Chan Jung, Sung-Jun Yang

“…Furthermore, compared to approaches without compressive sensing, the method shows a 55.1% and a 75.5% reduction in error in averaged RCS for VV-pol and HH-pol, respectively. …”
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Classical and Quantum Algorithms for Characters of the Symmetric Group by Sergey Bravyi, David Gosset, Vojtech Havlicek, Louis Schatzki

“…To assess classical hardness of these problems, we present a general reduction from strong simulation (computing a given probability) to weak simulation (sampling with a small error). …”
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Improving stock price forecasting with M-A-BiLSTM: a novel approach by Zihan Liu

“…Evaluated on stock datasets from Apple, ExxonMobil, Tesla, and Snapchat, our model outperforms existing deep learning methods, achieving a 15.91% reduction in Mean Squared Error (MSE) for Tesla and a 5.95% increase in R-squared (R2) for Apple. …”
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Investigation on the Role of Artificial Intelligence in Measurement System by P. A. Rezvy, Venkata Lakshmi Narayana Komanapalli

“…Soft computation using artificial neural networks and deep learning for linearization, compensation and error reduction, machine learning for estimation levaraging different algorithms like levenberg marquardt, scaled conjugate gradient, bayesian regularization, are assessed for training, testing and validation in real time and simulation. …”
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