scholarly journals Artificial Neural Network Context-Aware Reasoning Engine Model for Predicting User-Ranked Activities

2021 ◽  
Vol 5 (6) ◽  
pp. 36-42
Author(s):  
Samuel King Opoku
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Machine Learning Techniques ◽  
Context Aware ◽  
Back Propagation Algorithm ◽  
Engine Model ◽  
Context Reasoning ◽  
Stochastic Learning ◽  
Artificial Neural ◽  
Reasoning Engine

The choice of users’ activity in a context-aware environment depends on users’ preferences and background. Users tend to rank concurrent activities and select their preferred activity. Researchers and developers of context-aware applications have sought various mechanisms to implement context reasoning engines. Recent implementations use Artificial Neural Networks (ANN) and other machine learning techniques to develop a context-aware reasoning engine to predict users’ activities. However, the complexities of these mechanisms overwhelm the processing capabilities and storage capacity of mobile devices. The study models a context-aware reasoning engine using a multi-layered perceptron with a gradient descent back-propagation algorithm to predict activity from user-ranked activities using a stochastic learning mode with a constant learning rate. The work deduced that working with specific rules in training a neural network is not always applicable. Training a network without approximation of neuron’s output to the nearest whole number increases the accuracy level of the network at the end of the training.

scholarly journals Recognition of Text CAPTCHA by using Back Propagation Algorithm of Artificial Neural network

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1448-1459
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Storage System ◽  
Back Propagation ◽  
Machine Learning Techniques ◽  
Security Level ◽  
Simple Type ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Artificial Neural

Text-based CAPTCHA is a very simple type of CAPTCHA which are most widely used. It uses only a group of characters. In this paper, we focus on how Text based CAPTCHA is recognized by machine learning techniques. This paper proposed a method based on Back Propagation algorithm to identify the Text based CAPTCHA. The proposed technique improves the security level of Text-based CAPTCHA storage system by using the Back-propagation method of Artificial Neural Network. We used NNToolbox to train the network in MATLAB software

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

2010 ◽  
Vol 39 ◽  
pp. 555-561 ◽  
Author(s):  
Qing Hua Luan ◽  
Yao Cheng ◽  
Zha Xin Ima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Control ◽  
Back Propagation ◽  
Forecast Model ◽  
Flood Forecast ◽  
Back Propagation Algorithm ◽  
Nonlinear Method ◽  
Flood Prediction ◽  
Artificial Neural

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

scholarly journals An Intelligent Artificial Neural Network Modeling of a Magnetorheological Elastomer Isolator

Algorithms ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 195
Author(s):  
Shiping Zhao ◽  
Yong Ma ◽  
Dingxin Leng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vibration Control ◽  
Back Propagation ◽  
Structural Vibration ◽  
Neural Network Modeling ◽  
Back Propagation Algorithm ◽  
Magnetorheological Elastomer ◽  
Power Cost ◽  
Artificial Neural

Recently, magnetorheological elastomer (MRE) has been paid increasingly attention for vibration mitigation devices with the benefits of low power cost, fail safe performances, and fast responses. To make full use of the striking advantages of MRE device, a highly precise model should be developed to predict its dynamic performances. In the work, an MRE isolator in shear–squeeze mixed mode is developed and tested under dynamic loadings. The nonlinear performances in various displacement amplitude and currents are shown. An artificial neural network model with a back-propagation algorithm is proposed to characterize the nonlinear hysteresis of MRE isolator for its implementation in vibration control applications. This model utilized the displacement, velocity, and applied current as inputs and output force as output. The results show that the proposed model has high modeling accuracy and can well portray the complicated behaviors of MRE isolator with different excitations, which shows a fundamental basis for structural vibration control.

Predictive modeling of geometric shapes of different objects using image processing and an artificial neural network

2016 ◽  
Vol 231 (6) ◽  
pp. 1206-1216 ◽  
Author(s):  
Mustafa Ayyıldız ◽  
Kerim Çetinkaya
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Conjugate Gradient ◽  
Back Propagation ◽  
Neural Network Modeling ◽  
Back Propagation Algorithm ◽  
Geometric Shapes ◽  
Testing Data ◽  
Artificial Neural

In this study, an artificial neural network model was developed to predict the geometric shapes of different objects using image processing. These objects with various sizes and shapes (circle, square, triangle, and rectangle) were used for the experimental process. In order to extract the features of these geometric shapes, morphological features, including the area, perimeter, compactness, elongation, rectangularity, and roundness, were applied. For the artificial neural network modeling, the standard back-propagation algorithm was found to be the optimum choice for training the model. In the building of the network structure, five different learning algorithms were used: the Levenberg–Marquardt, the quasi-Newton back propagation, the scaled conjugate gradient, the resilient back propagation, and the conjugate gradient back propagation. The best result was obtained by 6-5-1 network architectures with single hidden layers for the geometric shapes. After artificial neural network training, the correlation coefficients ( R2) of the geometric shape values for training and testing data were very close to 1. Similarly, the root-mean-square error and mean error percentage values for the training and testing data were less than 0.9% and 0.004%, respectively. These results demonstrated that the artificial neural network is an admissible model for the estimation of geometric shapes using image processing.

scholarly journals Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

2017 ◽  
Vol 43 (4) ◽  
pp. 26-32 ◽  
Author(s):  
Sinan Mehmet Turp
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Data Sets ◽  
Adsorption Efficiency ◽  
Back Propagation Algorithm ◽  
Artificial Neural

AbstractThis study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.

scholarly journals Optimization of Load Forecasting in Smartgrid using Artificial Neural Network based NFTOOL and NNTOOL

2022 ◽  
Vol 2161 (1) ◽  
pp. 012068
Author(s):  
Sthitprajna Mishra ◽  
Bibhu Prasad Ganthia ◽  
Abel Sridharan ◽  
P Rajakumar ◽  
D. Padmapriya ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Smart Grid ◽  
User Interfaces ◽  
Load Forecasting ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Load Prediction ◽  
Artificial Neural ◽  
Optimal Reserve

Abstract The motivation behind the research is the requirement of error-free load prediction for the power industries in India to assist the planners for making important decisions on unit commitments, energy trading, system security & reliability and optimal reserve capacity. The objective is to produce a desktop version of personal computer based complete expert system which can be used to forecast the future load of a smart grid. Using MATLAB, we can provide adequate user interfaces in graphical user interfaces. This paper devotes study of load forecasting in smart grid, detailed study of architecture and configuration of Artificial Neural Network(ANN), Mathematical modeling and implementation of ANN using MATLAB and Detailed study of load forecasting using back propagation algorithm.

scholarly journals OPTIMIZATION RAINFALL-RUNOFF MODELING FOR CIUJUNG RIVER USING BACK PROPAGATION METHOD

SINERGI ◽  
2018 ◽  
Vol 22 (3) ◽  
pp. 193
Author(s):  
Ika Sari Damayanthi Sebayang ◽  
Agus Suroso ◽  
Alnis Gustin Laoli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Urban Areas ◽  
Back Propagation ◽  
Water Volume ◽  
Rainfall Runoff ◽  
Back Propagation Algorithm ◽  
Runoff Modeling ◽  
Artificial Neural ◽  
The Relationship

The rainfall-runoff model is required to ascertain the relationship between rainfall and runoff. Hydrologists are often confronted with problems of prediction and estimation of runoff using the rainfall date. In actual fact the relationship of rainfall-runoff is known to be highly non-linear and complex. The spatial and temporal precipitation patterns and the variability of watershed characteristics create a more complex hydrologic phenomenon. Runoff is part of the rain water that enters and flows and enters the river body. Rainfall-runoff modeling in this study using Artificial Neural Network, back propagation method and sigmoid binary activation function. This model is used to simulate single or long-term continuous events, water volume, making it very appropriate for urban areas. Back propagation is an inherited learning algorithm and is commonly used by perceptron with multiple layers to change the weights associated with neurons in the hidden layer. Back propagation algorithm uses output error to change the values of its weight in the backward direction. The location of the review is the Ciujung River Basin (DAS), the data used are rainfall and debit data of Ciujung River from 2011-2017. Based on training and simulation results, obtained R2 value: 2012 = 0,85102; 2013 = 0,78661; 2014 = 0,81188; 2015 = 0,77902; 2016 = 0,7279. on model 2 = 0,8724. On model 3 R2:  January = 0,96937; February = 0,92984; March = 0,90666; April = 0,92566; May = 0,9128; June = 0,87975; July= 0,85292; August = 0,95943; September = 0,88229; October = 0,90537; November = 0,93522; December = 0,9111. with MSE (Mean Squared Error) of 0,0018479. The closer value of MSE to 0 and the value of R2 close to 1 then the better designed artificial neural network. If the data used for training more, the artificial neural network will produce a larger R2 value.

scholarly journals DESIGN OF ARTIFICIAL NEURAL NETWORK SOFTWARE FOR PREDICTING THE HEALTH GRADE IN A TELEPHONE EXCHANGE

SAINTEKBU ◽  
2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Wiratmoko Yuwono ◽  
Yodik Iwan Herlambang ◽  
Mauridhi Hery Purnomo ◽  
Prima Kristalina
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Telephone Exchange ◽  
Propagation Algorithm ◽  
Artificial Neural ◽  
Network Software

Application of artificial neural network software ( ANN ) has been implemented forpredicting many thing and replace the conventional ways of predicting method using linearregression. Back Propagation algorithm can be used to reach the result of the program thatcan predict the telephone exchange health grade according to the data that has beenrecorded before. By predicting each parameter that has correlation to the telephoneexchange health grade, we can predict the telephone exchange health grade in the nextperiod.Kata kunci : jaringan syaraf tiruan, propagasi balik, nilai kesehatan sentral.

scholarly journals Determining the Yield of the Crop using Artificial Neural Network Method

2019 ◽  
Vol 9 (1) ◽  
pp. 2959-2965
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Crop Yield ◽  
Crop Yields ◽  
Back Propagation ◽  
Large Data ◽  
Machine Learning Techniques ◽  
Agricultural System ◽  
Factors Affecting ◽  
Artificial Neural

The agricultural system is complex and comprehend since it deals with large data that comes from several factors. Lot of techniques and have been used to identify any interactions between factors affecting yields with crop performance. The major objective of this paper is to help us predict the yield of a particular crop before even cultivating it for its production. We are using artificial neural networks for forwarding and implementing a system that will help the farmers in finding their crop yields according to their given data as input in the system and the system will give output based on previous data. The method used in this crop yield system is an artificial neural network and the algorithm used is feed forward and back propagation. Provide the input of data sets and the desired outcome of the system. Compute the error between the actual and desired outcome of the system. Amendment of the weight associated with different inputs and different functions. Compare the errors and the tolerance ratio of the output. Various machine learning techniques have been used in the past for calculating the crop yield using remote data. However, these methods are less useful and effective for predicting the yield of maize and for some other crops, which is cultivated at different times in various fields.The major application of this crop yield system is that it will help us to predict the yield before even cultivating it by studying the previous data collected such as soil fertility, pH level.

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