Classification of Road Damage from Digital Image Using Backpropagation Neural Network

Sutikno, Sutikno; Wibawa, Helmie Arif; Budiarto, Prima Yusuf

doi:10.11591/ijai.v6.i4.pp159-165

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…The next important step to extract information from an image is to suppress or handle unwanted distortion or noise in images for further processing [13]. Hence, [13][14][15][16][17][18] used enhancement in image processing to remove noise in their image data. This enhancement intensifies the quality of images which reduce noise and blur, adjust the contrast and enlightening the image details.…”

Section: Image Processingmentioning

confidence: 99%

Classification of white rice grain quality using ANN: a review

Hamzah¹,

Mohamed

2020

IJ-AI

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Exploring the new method of using technology for classifying rice grain quality is pertinent for rice producers in order to provide quality grains and protect consumers from any contamination exist. This is even more important when in today’s market we can see that rice with low quality is traded without stringent quality control which at the end will affect consumer’s health. This paper will review classification methods in determining quality white rice grain. Although there are many researchers developing new process to do rice classification by using different technique, there are still more advanced technique that can be used to do classification. This paper will focus on classifying rice grain quality using artificial neural network (ANN) approach with the help of image processing to identify the impurities contained in the rice grains. The findings show ANN using BPNN has the highest accuracy of 96%, it is also noted that other methods provide equally better performance. This review indicate hybrid method in ANN should be explored next for future work.

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Section: Image Processingmentioning

confidence: 99%

Classification of white rice grain quality using ANN: a review

Hamzah¹,

Mohamed

2020

IJ-AI

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“…Artificial Neural Networks were generally utilized in a wide range of fields, for example, digital image [22], fault detection [23], gold price forecasting [24] and many more. It has picked up the enthusiasm since rediscovery and popularization of the backpropagation algorithm by Rumelhart and McClelland in 1986.…”

Section: Introductionmentioning

confidence: 99%

Forecasting accuracy: a comparative study between artificial neural network and autoregressive model for streamflow

Zurey

Ismail

2020

IJ-AI

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Estimating the reliability of potential prediction is very crucial as our life depended heavily on it. Thus, a simulation that concerned hydrological factors such as streamflow must be enhanced. In this study, Autoregressive (AR) and Artificial Neural Networks (ANN) were used. The forecasting result for each model was assessed by using various performance measurements such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE) and Nash-Sutcliffe Model Efficiency Coefficient (CE). The experimental results showed the forecast performance of Durian Tunggal reservoir datasets by using ANN Model 7 with 7 hidden neurons has better forecast performance compared to AR (4). The ANN model has the smallest MAE (0.0116 m3/s), RMSE (0.0607 m3/s), MAPE (1.8214% m3/s), MFE (0.0058 m3/s) and largest CE (0.9957 m3/s) which show the capability of fitting to a nonlinear dataset. In conclusion, high predictive precision is an advantage as a proactive or precautionary measure that can be inferred in advance in order to avoid certain negative effects.

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“…A methodology in which the vehicle count is determined using Image processing technique and a learning model is designed which predicts the duration of traffic signal for the next iteration based on the available data of current iteration, is presented in [5]. Also, [6] and [7] proposes alternative techniques to overcome traffic congestion and traffic accidents.…”

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confidence: 99%

Autonomous Traffic Signal Control using Decision Tree

Rithesh¹,

Vignesh²,

Anala³

2018

IJECE

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The objective of this paper is to introduce an effective and efficient way of traffic signal light control to optimize the traffic signal duration across each lanes and thereby, to minimize or completely eliminate traffic congestion. This paper introduces a new approach to resolve the traffic congestion problem at junctions by making use of decision trees. The vehicle count in the real time traffic video is determined by Image Processing technique. This information is fed to the decision tree based on which the decision is made regarding the status of traffic signal lights of each lane at the junction at any given instant of time.

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Classification of Road Damage from Digital Image Using Backpropagation Neural Network

Cited by 4 publications

References 17 publications

Classification of white rice grain quality using ANN: a review

Classification of white rice grain quality using ANN: a review

Forecasting accuracy: a comparative study between artificial neural network and autoregressive model for streamflow

Autonomous Traffic Signal Control using Decision Tree

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