Due to the benefits of spectrum and energy efficiency, intelligent reflecting surfaces (IRSs) are regarded as a promising technology for future networks. In this work, we consider single cellular network where multiple IRSs deployed assist downlink transmissions from base station (BS) user equipment (UE). Hence, aim jointly optimize configuration BS active beamforming reflection that meet UE’s ...

As particulate organic carbon (POC) from lakes plays an important role in lake ecosystem sustainability and cycle, the estimation of its concentration using satellite remote sensing is great interest. However, high complexity variability water composition pose major challenges to algorithm POC Class II water. This study aimed formulate a machine-learning predict compare their modeling performan...

Data privacy has become an increasingly important issue in Machine Learning (ML), where many approaches have been developed to tackle this challenge, e.g. cryptography (Homomorphic Encryption (HE), Differential Privacy (DP), etc.) and collaborative training (Secure Multi-Party Computation (MPC), Distributed Federated (FL)). These techniques a particular focus on data encryption or secure local ...

Abstract In this paper, general regression neural network (GRNN) with the input feature of Mel-frequency cepstrum coefficient (MFCC) is employed to automatically recognize calls leopard, ross, and weddell seals widely overlapping living areas. As a feedforward network, GRNN has only one parameter, i.e., spread factor. The recognition performance can be greatly improved by determining factor bas...

Wood density is a key indicator for tree functionality and end utilization. Appropriate chemometric methods play an important role in the successful prediction of wood by visible near infrared (Vis-NIR) spectroscopy. The objective this study was to select appropriate pre-processing, variable selection multivariate calibration techniques improve accuracy Chinese white poplar (Populus tomentosa c...

In this paper, researchers estimated the stock price of activated companies in Tehran (Iran) stock exchange. It is used Linear Regression and Artificial Neural Network methods and compared these two methods. In Artificial Neural Network, of General Regression Neural Network method (GRNN) for architecture is used. In this paper, first, researchers considered 10 macro economic variables and 30 fi...

Data Mining is study of how to determine underlying patterns in the data. Data mining techniques like machine learning, alongside the conventional methods are deployed. Different Data mining techniques like GRNN, MLP, NNARX, CART, RBF, ARIMA and so on are used for the prediction of Rainfall. In this paper, analysis of various algorithms of data mining is used for rainfall prediction model. It i...

To the shortcoming of general particle filter, an improved algorithm based on neural network is proposed and is shown to be more efficient than the general algorithm in the same sample size. The improved algorithm has mainly optimized the choice of importance density. After receiving the samples drawn from prior density, and then adjust the samples with general regression neural network (GRNN),...

In this work, we present the Grounded Recurrent Neural Network (GRNN), a recurrent neural network architecture for multi-label prediction which explicitly ties labels to specific dimensions of the recurrent hidden state (we call this process “grounding”). The approach is particularly well-suited for extracting large numbers of concepts from text. We apply the new model to address an important p...