scholarly journals CLASSIFICATION OF VEHICLE TYPES USING BACKPROPAGATION NEURAL NETWORKS WITH METRIC AND ECENTRICITY PARAMETERS

2021 ◽  
Vol 4 (1) ◽  
pp. 65-70
Author(s):  
Hendra Mayatopani ◽  
Rohmat Indra Borman ◽  
Wahyu Tisno Atmojo ◽  
Arisantoso Arisantoso
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Vehicle Classification ◽  
Test Results ◽  
Backpropagation Neural Network ◽  
Vehicle Type ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Wheeled Vehicles

One of the efforts to break down traffic jams is to establish special lanes that can be passed by two, four or more wheeled vehicles. By being able to recognize the type of vehicle can reduce congestion. Citran based vehicle classification helps in providing information about the vehicle type. This study aims to classify the type of vehicle using a backpropagation neural network algorithm. The vehicle image can be recognized based on its shape, then the backpropagation neural network algorithm will be supported by metric and eccentricity parameters to perform feature extraction. Then from the results of feature extraction with metric parameters and eccentricity, the object will be classified using a backpropagation neural network algorithm. The test results show an accuracy of 87.5%. This shows the algorithm can perform classification well.

scholarly journals Classification of Tropical Rain Forest East Kalimantan Using Image Processing and Backpropagation Neural Network Algorithm

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2586-2589
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Rain Forest ◽  
Tropical Rain Forest ◽  
Backpropagation Neural Network ◽  
East Kalimantan ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
High Level

Tropical Rain Forest located in East Kalimantan has a high level of biodiversity, with a high level of biodiversity in east kalimantan then it needs a method to classify the existing plants there. In the research, the researchers tried to classify 5 plants found in tropical rainforests, namely ShoreaBalangeran, Dryobalanopsbeccarii Dyer, Eusideroxylonzwageri, Duriokutejensis, Cerberamanghas. Classification is done by using backpropagation neural network algorithm combined with image processing, where the image used is the image of plant leaf. The result of this research is the classification of 5 species of this plant with precision value above 90% in order to become a supporter of botanical decision in determining the type of plant and become alternative reference to classify plants in tropical rain forest area.

scholarly journals EQUIPMENT PRICE FORECAST BASED ON T-S FUZZY NEURAL NETWORK

2018 ◽  
Vol 48 (4) ◽  
pp. 305-309
Author(s):  
G. P. JIANG ◽  
L. XIE ◽  
S. X. SUN
Keyword(s):  
Neural Network ◽  
Wavelet Neural Network ◽  
Test Results ◽  
Factors Affecting ◽  
Price Forecast ◽  
Network Algorithm ◽  
Auction Price ◽  
Variable Weight ◽  
Fuzzy Neural ◽  
Neural Network Algorithm

As we all know, the factors affecting the price of equipment are more complicated, but these factors still have a great correlation. How can we accurately predict the price of equipment? Based on the study of the tight support and smoothness of wavelet function, this paper proposes a correlation variable weight wavelet neural network algorithm to predict the price of 162 devices. The test results show that if the weight is not reduced, the predicted price is 0, and the error is still large. However, by arranging the data from small to large, the variable weighted wavelet neural network algorithm is used to predict the result closer to the auction price, which overcomes the incompatibility of the algorithm iteration and provides a reference for accurately predicting the price of the device.

Classification of Batik Authenticity Using Convolutional Neural Network Algorithm with Transfer Learning Method

2021 ◽  
Author(s):  
Farrel Athaillah Putra ◽  
Dwi Anggun Cahyati Jamil ◽  
Briliantino Abhista Prabandanu ◽  
Suhaili Faruq ◽  
Firsta Adi Pradana ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Method ◽  
Network Algorithm ◽  
Neural Network Algorithm

scholarly journals Research on Sentiment Classification Algorithms on Online Review

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Ruixia Yan ◽  
Zhijie Xia ◽  
Yanxi Xie ◽  
Xiaoli Wang ◽  
Zukang Song
Keyword(s):  
Neural Network ◽  
Sentiment Classification ◽  
Online Review ◽  
Support Vector ◽  
Classification Algorithms ◽  
Evaluation Indexes ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Reinforcement Learning Models

The product online review text contains a large number of opinions and emotions. In order to identify the public’s emotional and tendentious information, we present reinforcement learning models in which sentiment classification algorithms of product online review corpus are discussed in this paper. In order to explore the classification effect of different sentiment classification algorithms, we conducted a research on Naive Bayesian algorithm, support vector machine algorithm, and neural network algorithm and carried out some comparison using a concrete example. The evaluation indexes and the three algorithms are compared in different lengths of sentence and word vector dimensions. The results present that neural network algorithm is effective in the sentiment classification of product online review corpus.

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