Infant's cry sound classification using Mel-Frequency Cepstrum Coefficients feature extraction and Backpropagation Neural Network

2016 ◽  
Author(s):  
Yesy Diah Rosita ◽  
Hartarto Junaedi
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Backpropagation Neural Network ◽  
Sound Classification

scholarly journals Deteksi Api Pada Video Menggunakan Metode Artificial Neural Network

2021 ◽  
Vol 3 (1) ◽  
pp. 96-107
Author(s):  
Budiman Rabbani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Extraction Method ◽  
Gray Level ◽  
Backpropagation Neural Network ◽  
Validation Process ◽  
Feature Extraction Method ◽  
Time Value ◽  
Occurrence Matrix

Abstract The camera is one of the tools used to collect images. Cameras are often used for the safety of homes, highways and others. Then in this study camera captures are used to support fire objects because fire is one of the causes of safety that can be controlled. Therefore, by utilizing a capture camera will see the best model of backpropagation neural network by combining the local binary patern (LBP) feature extraction method and the Gray Level Co-occurrence Matrix (GLCM) to access the fire. Then to evaluate the performance of the model created using three parameters that contain accuracy, recall, precision. The data in this study consisted of videos with variations of fire and non-fire videos. Based on the final results of the study, accuracy, remember, the best precision obtained simultaneously 96%, 97%, 97%. Then the validation process was done using 30 videos with a variation of 15 fire videos and 15 non-fire videos and obtained an accuracy of 86.6% with an average time value of 6.029 minutes.

scholarly journals Diagonal Based Feature Extraction and Backpropagation Neural Network in Handwritten Batak Toba Characters Recognition

2021 ◽  
Author(s):  
Elviawaty Muisa Zamzami ◽  
Septi Hayanti ◽  
Erna Budhiarti Nababan
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Character Recognition ◽  
Complex Problem ◽  
Backpropagation Neural Network ◽  
Training Process ◽  
Handwritten Character Recognition ◽  
Handwritten Character

Handwritten character recognition is considered a complex problem since one’s handwritten character has its characteristics.  Data used for this research was a photo of handwritten or scanned handwritten.  In this research, Backpropagation Neural Network (BPNN) was used to recognize handwritten Batak Toba character, wherein preprocessing stage feature extraction was done using Diagonal Based Feature Extraction (DBFE) to obtain feature value.  Furthermore, the feature value will be used as an input to BPNN. The total number of data used was190 data, where 114 data was used for the training process and another 76 data was used for testing. From the testing process carried out, the accuracy obtained was 87,19 %.

scholarly journals Identification of Plant Types by Leaf Textures Based on the Backpropagation Neural Network

2018 ◽  
Vol 8 (6) ◽  
pp. 5389 ◽  
Author(s):  
Taufik Hidayat ◽  
Asyaroh Ramadona Nilawati
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Plant Species ◽  
Recognition Accuracy ◽  
Backpropagation Neural Network ◽  
Training Set ◽  
Plant Resources ◽  
Plant Leaf ◽  
Tropical Plant ◽  
Texture Characteristics

The number of species of plants or flora in Indonesia is abundant. The wealth of Indonesia's flora species is not to be doubted. Almost every region in Indonesia has one or some distinctive plant(s) which may not exist in other countries. In enhancing the potential diversity of tropical plant resources, good management and utilization of biodiversity is required. Based on such diversity, plant classification becomes a challenge to do. The most common way to recognize between one plant and another is to identify the leaf of each plant. Leaf-based classification is an alternative and the most effective way to do because leaves will exist all the time, while fruits and flowers may only exist at any given time. In this study, the researchers will identify plants based on the textures of the leaves. Leaf feature extraction is done by calculating the area value, perimeter, and additional features of leaf images such as shape roundness and slenderness. The results of the extraction will then be selected for training by using the backpropagation neural network. The result of the training (the formation of the training set) will be calculated to produce the value of recognition accuracy with which the feature value of the dataset of the leaf images is then to be matched. The result of the identification of plant species based on leaf texture characteristics is expected to accelerate the process of plant classification based on the characteristics of the leaves.

scholarly journals Pengenalan Plat Kendaraan Bermotor Menggunakan Metode Gradien Karakter dan BPNN (Backpropagation Neural Network)

2020 ◽  
Vol 4 (2) ◽  
pp. 169-178
Author(s):  
Muhammad Ridwan Ali ◽  
Ario Yudo Husodo
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Digital Image ◽  
Gradient Method ◽  
License Plate ◽  
Backpropagation Neural Network ◽  
Network Feature ◽  
Testing Performance ◽  
Hidden Layer ◽  
Performance Rate

License plates are a unique feature to identify a vehicle in the combination between letters and numbers. Feature extraction needed to identify each letter and number in a digital image. There are several methods in feature extraction, one of them uses a gradient feature extraction. In this research, an application program to identify the license plate is a character gradient method and backpropagation neural network (BNN). First, the digital image is cropped to get a license plate then segmented to generate each character. The next step is the extraction feature using Character gradient to get a particular feature from each character. Backpropagation neural networks are used as data classification. This research consist of two types of testing: performance analysis based on hidden layers and feature quantity in training datasets and license plate data. From the result, we can conclude that the quantity of features affects the system performance. The highest performance rate in the first scenario test is feature 48 with 60 hidden layers, and in the second scenario, the highest is feature 108 with 60 hidden layers. The lowest performance rate is shown in feature 12 with 20 hidden layers. Keywords: License plate, character gradient method, backpropagation neural network, feature, hidden layer.

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.

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