• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.403-413
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• 2022

Classification and analysis are improved factors for the realtime automation system. In the field of agriculture, the cultivation of different paddy crop depends on the atmosphere and the soil nature. We need to analyze the moisture level in the area to predict the type of paddy that can be cultivated. For this process, Ensemble Modulation Pattern system and Block Probability Neural Network based classification models are used to analyze the moisture and temperature of land area. The dataset consists of the collections of moisture and temperature at various data samples for a land. The Ensemble Modulation Pattern based feature analysis method, the extract of the moisture and temperature in various day patterns are analyzed and framed as the pattern for given dataset. Then from that, an improved neural network architecture based on the block probability analysis are used to classify the data pattern to predict the class of paddy crop according to the features of dataset. From that classification result, the measurement of data represents the type of paddy according to the weather condition and other features. This type of classification model assists where to plant the crop and also prevents the damage to crop due to the excess of water or excess of temperature. The result analysis presents the comparison result of proposed work with the other state-of-art methods of data classification.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.785-787
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• 2014

본 논문에서는 화산 분출 초저음파의 식별을 위해서 FSPNNC(Frequency Sub-bands Parallel Neural NetworkClassification)을 선택한다. FSPNNC 는 각기 다른 주파수 영역에서 독립적으로 추출한 특징벡터를 병렬 구조의 신경망에 학습하는 구조를 가지며 하나의 신경망은 하나의 분류 및 하나의 주파수 부대역만을 학습하고 다른 신경망들은 해당 특징벡터를 분류하지 않도록 학습된다. 실험은 단일 신경망 및 PNNCB(Parallel Neural Network Classifier Bank)와의 비교실험을 통하여 식별 성능을 제시한다.

• Journal of Korea Multimedia Society
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• v.21 no.10
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• pp.1195-1202
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• 2018

Artificial neural network is widely used for its excellent performance and implementability. However, traditional neural network needs to learn the system from scratch, with the addition of new input data, the variation of the observation environment, or the change in the form of input/output data. To resolve such a problem, the technique of transfer learning has been proposed. Transfer learning constructs a newly developed target system partially updating existing system and hence provides much more efficient learning process. Until now, transfer learning is mainly studied in the field of image processing and is not yet widely employed in acoustic data processing. In this paper, focusing on the scalability of transfer learning, we apply the concept of transfer learning to the problem of guitar chord classification and evaluate its performance. For this purpose, we build a target system of convolutional neutral network (CNN) based 48 guitar chords classification system by applying the concept of transfer learning to a source system of CNN based 24 guitar chords classification system. We show that the system with transfer learning has performance similar to that of conventional system, but it requires only half the learning time.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.6 no.1
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• pp.67-73
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• 2012

In this paper, Artificial Neural Network(ANN) based motion classification algorithm is proposed to classify wrist motions using surface electromyograms(sEMG). surface EMGs are obtained from two electrodes placed on the flexor carpi ulnaris muscle and extensor carpi ulnaris muscle of 26 subjects under no strain condition during wrist motions and used to recognize wrist motions such as up, down, left, right, and rest. Feature is extracted from obtained EMG signals in time domain for fast processing and used to classify wrist motions using ANN. DAMV, DASDV, MAV, and RMS were used as features and accuracies of motion classification based on ANN were 98.03% for DAMV, 97.97% for DASDV, 96.95% for MAV, 96.82% for RMS.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.234-242
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• 2024

We propose a passive sonar signal classification algorithm using Graph Neural Network (GNN). The proposed algorithm segments spectrograms into image patches and represents graphs through connections between adjacent image patches. Subsequently, Graph Convolutional Network (GCN) is trained using the represented graphs to classify signals. In experiments with publicly available underwater acoustic data, the proposed algorithm represents the line frequency features of spectrograms in graph form, achieving an impressive classification accuracy of 92.50 %. This result demonstrates a 8.15 % higher classification accuracy compared to conventional Convolutional Neural Network (CNN).

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2573-2575
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• 2003

In this paper, we discuss a fuzzy neural network classifier with immune algorithm. The fuzzy neural network classifier is constructed with the fuzzy classifier and the neural network classifier based on fuzzy rules. To maximize performance of classifier, the immune algorithm and the back propagation algorithm are used. For the generalized classification ability, the simulation results from the iris data demonstrate superiority of the proposed classifier in comparison with other classifier.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.2 s.16
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• pp.47-60
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• 2000

On this study, Among the classification methods of land cover using satellite imagery, we compared the classification accuracy of Neural Network Classifier and that of Maximum Likelihood Classifier which has the characteristics of parametric and non-parametric classification method. In the assessment of classification accuracy, we analyzed the classification accuracy about testing area as well as training area that many analysts use generally when assess the classification accuracy. As a result, Neural Network Classifier is superior to Maximum Likelihood Classifier as much as 3% in the classification of training data. When ground reference data is used, we could get poor result from both of classification methods, but we could reach conclusion that the classification result of Neural Network Classifier is superior to the classification result of Maximum Likelihood Classifier as much as 10%.

• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.592-599
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• 2020

This paper proposes a Convolutional Recurrent Neural Net (CRNN) structure that can simultaneously reflect both static and dynamic characteristics of seismic waveforms for various earthquake events classification. Addressing various earthquake events, including not only micro-earthquakes and artificial-earthquakes but also macro-earthquakes, requires both effective feature extraction and a classifier that can discriminate seismic waveform under noisy environment. First, we extract the static characteristics of seismic waveform through an attention-based convolution layer. Then, the extracted feature-map is sequentially injected as input to a multi-input single-output Long Short-Term Memory (LSTM) network structure to extract the dynamic characteristic for various seismic event classifications. Subsequently, we perform earthquake events classification through two fully connected layers and softmax function. Representative experimental results using domestic and foreign earthquake database show that the proposed model provides an effective structure for various earthquake events classification.

• Journal of Information Management
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• v.39 no.4
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• pp.47-66
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• 2008

This study introduced the machine learning algorithms to overcome the many different limitations involved with manual classification and to provide the users with faster and more accurate classification service. The experiments objects of the study were consisted of 100 literature titles for each of the eight subject categories in MeSH. The algorithms used to the experiments included Neural network, C5.0, CHAID and KNN. As results, the combination of the neural network and C5.0 technique recorded classification accuracy of 83.75%, which was 2.5% and 3.75% higher than that of the neural network alone and C5.0 alone, respectively. The number represented the highest accuracy rates among the four classification experiments. Thus the use of the neural network and C5.0 technique together will result in higher accuracy rates than the techniques individually.

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.21-32
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• 2022

Early-stage diagnosis of Alzheimer's Disease (AD) from Cognitively Normal (CN) patients is crucial because treatment at an early stage of AD can prevent further progress in the AD's severity in the future. Recently, computer-aided diagnosis using magnetic resonance image (MRI) has shown better performance in the classification of AD. However, these methods use a traditional machine learning algorithm that requires supervision and uses a combination of many complicated processes. In recent research, the performance of deep neural networks has outperformed the traditional machine learning algorithms. The ability to learn from the data and extract features on its own makes the neural networks less prone to errors. In this paper, a dense neural network is designed for binary classification of Alzheimer's disease. To create a classifier with better results, we studied result of different activation functions in the prediction. We obtained results from 5-folds validations with combinations of different activation functions and compared with each other, and the one with the best validation score is used to classify the test data. In this experiment, features used to train the model are obtained from the ADNI database after processing them using FreeSurfer software. For 5-folds validation, two groups: AD and CN are classified. The proposed DNN obtained better accuracy than the traditional machine learning algorithms and the compared previous studies for AD vs. CN, AD vs. Mild Cognitive Impairment (MCI), and MCI vs. CN classifications, respectively. This neural network is robust and better.