Comparative Study Of Neural Networks Research Articles

A Comparative Study of Neural Networks for Emergency Vehicle Detection in Traffic

This study explores the application of Neural Networks for efficient emergency vehicle identification. Utilizing a dataset of emergency and non-emergency vehicle images, the model demonstrates high accuracy in classification. A noteworthy use case involves real-time integration with traffic systems, where the identification of an emergency vehicle triggers an automatic change of traffic lights to green, facilitating swift and unimpeded passage.

Arabic Fake News Detection: Comparative Study of Neural Networks and Transformer‐Based Approaches

Fake news detection (FND) involves predicting the likelihood that a particular news article (news report, editorial, expose, etc.) is intentionally deceptive. Arabic FND started to receive more attention in the last decade, and many detection approaches demonstrated some ability to detect fake news on multiple datasets. However, most existing approaches do not consider recent advances in natural language processing, i.e., the use of neural networks and transformers. This paper presents a comprehensive comparative study of neural network and transformer‐based language models used for Arabic FND. We examine the use of neural networks and transformer‐based language models for Arabic FND and show their performance compared to each other. We also conduct an extensive analysis of the possible reasons for the difference in performance results obtained by different approaches. The results demonstrate that transformer‐based models outperform the neural network‐based solutions, which led to an increase in the F1 score from 0.83 (best neural network‐based model, GRU) to 0.95 (best transformer‐based model, QARiB), and it boosted the accuracy by 16% compared to the best in neural network‐based solutions. Finally, we highlight the main gaps in Arabic FND research and suggest future research directions.

Comparative study of neural networks for dynamic nonlinear systems identification

In this paper, a comparative study is performed to test the approximation ability of different neural network structures. It involves three neural networks multilayer feedforward neural network (MLFFNN), diagonal recurrent neural network (DRNN), and nonlinear autoregressive with exogenous inputs (NARX) neural network. Their robustness is also tested and compared when the system is subjected to parameter variations and disturbance signals. Further, dynamic back-propagation algorithm is used to update the parameters associated with these neural networks. Four dynamical systems of different complexities including motor-driven robotic link are considered on which the comparative study is performed. The simulation results show the superior performance of DRNN identification model over NARX and MLFFNN identification models.

Comparative Study of Neural Networks for Control of Nonlinear Dynamical Systems with Lyapunov Stability-Based Adaptive Learning Rates

This paper performs the comparative study of two feed-forward neural networks: radial basis function network (RBFN), multilayer feed-forward neural network (MLFFNN) and a recurrent neural network: nonlinear auto-regressive with exogenous inputs (NARX) neural network for their ability to provide an adaptive control of nonlinear systems. Dynamic back-propagation algorithm is used to derive parameter update equations. To ensure stability and faster convergence, an adaptive learning rate is developed in the sense of discrete Lyapunov stability method. Both parameter variation and disturbance signal cases are considered for checking and comparing the robustness of controller. Three simulation examples are considered for carrying out this study. The results so obtained reveal that RBFN-based controller is performing better than that of NARX- and MLFFNN-based controllers.

Comparative study of neural networks in path planning for catering robots

Neural Networks (NN) have been the forefront of growth in recent years due to their variety, the opportunities they provide and most importantly their dynamic nature. A control system for catering robots for path planning is proposed with the help of neural networks as a comparative study. Various parameters such as training time, performance of the network, forecasted distance are considered after iterating to obtain the optimal dataset using Probabilistic Roadmap (PRM) algorithm. Approximately 36% improvement in forecasted distance was obtained using neural networks when compared to the traditional PRM algorithm.

Recognition of CAPTCHA Characters by Supervised Machine Learning Algorithms

The focus of this paper is to compare several common machine learning classification algorithms for Optical Character Recognition of CAPTCHA codes. The main part of a research focuses on the comparative study of Neural Networks, k-Nearest Neighbour, Support Vector Machines and Decision Trees implemented in MATLAB Computing environment. Achieved success rates of all analyzed algorithms overcome 89%. The main difference in results of used algorithms is within the learning times. Based on the data found, it is possible to choose the right algorithm for the particular task.

A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games

The main purpose of this study was to develop and apply a neural network (NN) approach and an adaptive neuro-fuzzy inference system (ANFIS) model for forecasting the attendance rates at soccer games. The models were designed based on the characteristics of the problem. Past real data was used. Training data was used for training the models, and the testing data was used for evaluating the performance of the forecasting models. The obtained forecasting results were compared to the actual data and to each other. To evaluate the performance of the models, two statistical indicators, Mean Absolute Deviation (MAD) and mean absolute percent error (MAPE), were used. Based on the results, the proposed neural network approach and the ANFIS model were shown to be effective in forecasting attendance at soccer games. The neural network approach performed better than the ANFIS model. The main contribution of this study is to introduce two effective techniques for estimating attendance at sports games. This is the first attempt to use an ANFIS model for that purpose.

Prediction of performance and emission characteristics in a biodiesel engine using WCO ester: a comparative study of neural networks

In this study, the applicability of Artificial Neural Networks (ANNs) has been investigated for predicting the performance and emission characteristics of a diesel engine fuelled with Waste cooking oil (WCO). ANN modeling was done using multilayer perception (MLP) and radial basis functions (RBF). In the radial basis functions, centers were initialized by two different methods namely random selection method and using clustering algorithm. In the clustering method, center initialization was done using FCM (Fuzzy$$c$$cmeans) and CDWFCM (cluster dependent weighted fuzzy$$c$$cmeans) algorithms. The networks were trained using the experimental data, wherein load percentage, compression ratio, blend percentage, injection timing and injection pressure were taken as the input parameters and brake thermal efficiency, brake specific energy consumption, exhaust gas temperature and engine emissions were used as the output parameters. The investigation showed that ANN predicted results matched well with the experimental results over a wide range of operating conditions for both models. A comparison was made between ANN models and regression models. ANN performed better than the regression models. Similarly a comparison of MLP and RBF indicated that RBF with CDWFCM performed better than MLP networks with lower Mean Relative Error (MRE) and higher accuracy of prediction.

A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System

The aim of this paper is to compare the neural network and fuzzy modeling approaches on a nonlinear system. We have taken Permanent Magnet Brushless Direct Current (PMBDC) motor data and have generated models using both approaches. The predictive performance of both methods was compared on the data set for model configurations.The paper describes the results of these tests and discusses the effects of changing model parameters on predictive and practical performance. Modeling sensitivity was used to compare for two methods.

A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System

A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System

Comparative study of neural networks and least mean square algorithm applied to the optimization of cosmetic formulations

In this work, a comparative study between two methods to acquire relevant information about a cosmetic formulation has been carried out. A Design of Experiments (DOE) has been applied in two stages to a capillary cosmetic cream: first, a Plackett-Burman (PB) design has been used to reduce the number of variables to be studied; second, a complete factorial design has been implemented. With the experimental data collected from the DOE, a Least Mean Square (LMS) algorithm and Artificial Neural Networks (ANN) have been utilized to obtain an equation (or model) that could explain cream viscosity. Calculations have shown that ANN are the best prediction method to fit a model to experimental data, within the interval of concentrations defined by the whole set of experiments.

A Comparative Study of Neural Networks and Non-Parametric Regression Models for Trend and Seasonal Time Series

A Comparative Study of Neural Networks and Non-Parametric Regression Models for Trend and Seasonal Time Series

A comparative study of neural networks for input resistance computation of electrically thin and thick rectangular microstrip antennas

Multilayered-perceptron-based neural models for calculating the input resistance of electrically thin and thick rectangular microstrip antennas are presented. Eleven learning algorithms—Levenberg-Marquardt, conjugate gradient of Fletcher-Reeves, Bayesian regularization, Broyden-Fletcher-Goldfarb-Shanno, conjugate gradient of Polak-Ribiere, conjugate gradient of Powell-Beale, scaled conjugate gradient, one-step secant, resilient backpropagation, backpropagation with momentum, and backpropagation with adaptive learning rate—are used to train the multilayered perceptrons. The input resistance results obtained with the use of neural models are in very good agreement with the experimental results available in the literature. When the performances of neural models are compared with each other, the best result is obtained from the multilayered perceptrons trained by the Levenberg-Marquardt algorithm.

A Comparative Study of Neural Networks Based Approach for Fault Detection

This paper deals with a comparative study of neural networks used for fault detection and diagnosis (FDD) in a wastewater plant. When a fault occurs the parameters in a non-lineal mathematical model of the process change. A method for detecting and tracking the different values of the parameters is proposed, with tries to be robust with respect to low frequency disturbances and modelling errors, the uncertainties, which can interfere seriously with the FDD performance, increasing the false and missed alarms. The method, using different neural networks, is applied to a wastewater plant, in order to compare and to prove the reliability of the algorithms.

141 Approximate dynamic models for chemical processes: A comparative study of neural networks and nonlinear time series modeling techniques

141 Approximate dynamic models for chemical processes: A comparative study of neural networks and nonlinear time series modeling techniques

Approximate Dynamic Models for Chemical Processes: A Comparative Study of Neural Networks and Nonlinear Time Series Modeling Techniques

Abstract Neural networks and nonlinear time series models provide two paradigms for developing input-output models for nonlinear systems. Methodology for developing neural networks with radial basis functions and nonlinear auto-regressive (NAR) models are described. Dynamic input- output models for a MIMO chemical reactor system are developed. The NAR models are more parsimonious and more accurate in predictions.