scholarly journals Method for Measuring the Similarity of Multiple Metrological Sequences in the Key Phenological Phase of Rice-based on Dynamic Time

2021 ◽  
Vol 36 (2) ◽  
pp. 22-37
Author(s):  
Z.A. Khan ◽  
Mousa Mohammed Khubrani ◽  
Shadab Alam ◽  
S.J. Hui ◽  
Y. Wang
Keyword(s):  
Classification Accuracy ◽  
Sequence Data ◽  
Meteorological Data ◽  
Automatic Classification ◽  
Dynamic Programming Method ◽  
Accuracy Rate ◽  
Cluster Number ◽  
Dynamic Time ◽  
Heading Stage

The automatic classification of historical data of myriad diverse meteorological sequences in the annual period can help to find the climate differences through key phenology of rice. In this paper, a hybrid gradients-shape dynamic time warping (HGSDTW) algorithm is proposed to measure the similarity of meteorological data during the diverse rice growth period at various locations. The weighting calculation of Euclidean distance uses the form factor in the rice jointing and heading stage. The distance matrix constructs first & second-level gradient single-factor transformation sequences during the period. The dynamic programming method obtains the similarity distances of single and multiple meteorological factors. The results show that the classification accuracy rate from HGSDTW of the heading & jointing stage is higher than that of other similar algorithms. Furthermore, it can observe that the clustering number increases the classification accuracy, and the HGSDTW algorithm maintains the accuracy of 14% for varieties of rice at diverse locations to multiple years of jointing. Besides, the automatic classification experiment of sequence period shows that the classification accuracy of this method is higher than that of another similarity measure. The classification accuracy rate of the heading stage sequence is 10%~14% higher than that of a similar previous standard measurement algorithm, and the jointing period is 1%~9% higher. In this case, the cluster number increasing the classification accuracy, and the HGSDTW maintain the overall accuracy of 14%. Thus, this method can be effectively combined with the classification algorithm to improve the efficiency of the automatic classification of multi-weather sequence data in key phenological periods of rice.

Research of Orange Quality Classification Technology Based on BP Neural Network Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1821-1824
Author(s):  
Chun Hua Qian ◽  
He Qun Qiang ◽  
Sheng Rong Gong
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Classification Accuracy ◽  
Bp Algorithm ◽  
Shape Features ◽  
Accuracy Rate ◽  
Network Algorithm ◽  
Quality Classification ◽  
Neural Network Algorithm

BP algorithm is a classical neural network algorithm. We analyzed the deficiency of traditional BP neural network algorithm, designed new S function and momentum method strategy, optimized the algorithm parameters. We use the new algorithm in the classification of orange images, take color and shape features as input value, the experimental results proved that our algorithm is faster and the classification accuracy rate reaches to 90%

Classification of Mammogram Abnormalities Using Legendre Moments

2020 ◽  
pp. 2150010
Author(s):  
Rodrigo Dalvit C. Silva ◽  
Thomas R. Jenkyn
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Nearest Neighbors ◽  
Accuracy Rate ◽  
Feature Extractor ◽  
Analysis Society ◽  
Mammogram Image ◽  
Legendre Moments

In this paper, the issue of classifying mammogram abnormalities using images from an mammogram image analysis society (MIAS) database is discussed. We compare a feature extractor based on Legendre moments (LMs) with six other feature extractors. To determine the best feature extractor, the performance of each was compared in terms of classification accuracy rate and extraction time using a [Formula: see text]-nearest neighbors ([Formula: see text]-NN) classifier. This study shows that feature extraction using LMs performed best with an accuracy rate over 84% and requiring relatively little time for feature extraction, on average only 1[Formula: see text]s.

scholarly journals An Automatic System for Atrial Fibrillation by Using a CNN-LSTM Model

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Fengying Ma ◽  
Jingyao Zhang ◽  
Wei Chen ◽  
Wei Liang ◽  
Wenjia Yang
Keyword(s):  
Atrial Fibrillation ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Sequence Data ◽  
Detection System ◽  
Heart Rhythm ◽  
Classification Performance ◽  
Ecg Signals ◽  
Electrocardiogram Ecg

Atrial fibrillation (AF) is a common abnormal heart rhythm disease. Therefore, the development of an AF detection system is of great significance to detect critical illnesses. In this paper, we proposed an automatic recognition method named CNN-LSTM to automatically detect the AF heartbeats based on deep learning. The model combines convolutional neural networks (CNN) to extract local correlation features and uses long short-term memory networks (LSTM) to capture the front-to-back dependencies of electrocardiogram (ECG) sequence data. The CNN-LSTM is feeded by processed data to automatically detect AF signals. Our study uses the MIT-BIH Atrial Fibrillation Database to verify the validity of the model. We achieved a high classification accuracy for the heartbeat data of the test set, with an overall classification accuracy rate of 97.21%, sensitivity of 97.34%, and specificity of 97.08%. The experimental results show that our model can robustly detect the onset of AF through ECG signals and achieve stable classification performance, thereby providing a suitable candidate for the automatic classification of AF.

scholarly journals Feature extraction and classification of heart sound using 1D convolutional neural networks

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Fen Li ◽  
Ming Liu ◽  
Yuejin Zhao ◽  
Lingqin Kong ◽  
Liquan Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Heart Sound ◽  
Accuracy Rate ◽  
Denoising Autoencoder ◽  
Mel Frequency Cepstral Coefficients ◽  
One Dimensional ◽  
Input Feature ◽  
Bp Model

AbstractWe proposed a one-dimensional convolutional neural network (CNN) model, which divides heart sound signals into normal and abnormal directly independent of ECG. The deep features of heart sounds were extracted by the denoising autoencoder (DAE) algorithm as the input feature of 1D CNN. The experimental results showed that the model using deep features has stronger anti-interference ability than using mel-frequency cepstral coefficients, and the proposed 1D CNN model has higher classification accuracy precision, higher F-score, and better classification ability than backpropagation neural network (BP) model. In addition, the improved 1D CNN has a classification accuracy rate of 99.01%.

scholarly journals Classification of Musical Instruments using SVM and KNN

2020 ◽  
Vol 9 (7) ◽  
pp. 1186-1190
Keyword(s):  
Automatic Classification ◽  
High Accuracy ◽  
Musical Instruments ◽  
Acoustic Features ◽  
Accuracy Rate ◽  
Digital World ◽  
Knn Classifier ◽  
Modeling Techniques ◽  
Accuracy Level

Automatic classification of musical instruments is a challenging task. Music data classification has become a very popular research in the digital world. Classification of the musical instruments required a huge manual process. This system classifies the musical instruments from a several acoustic features that includes MFCC, Sonogram and MFCC combined with Sonogram. SVM and kNN are two modeling techniques used to classify the features. In this paper, to simply musical instruments classifications based on its features which are extracted from various instruments using recent algorithms. The proposed work compares the performance of kNN with SVM. Identifying the musical instruments and computing its accuracy is performed with the help of SVM and kNN classifier, using the combination of MFCC and Sonogram with SVM a high accuracy rate of 98% achieve in classifying musical instruments. The system tested sixteen musical instruments to find out the accuracy level using SVM and kNN

scholarly journals Peningkatan Akurasi Klasifikasi Ketidaktepatan Waktu Kelulusan Mahasiswa Menggunakan Metode Boosting Neural Network

Jurnal Varian ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 95-102
Author(s):  
I Ketut Putu Suniantara ◽  
Gede Suwardika ◽  
Siti Soraya
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Feedforward Neural Network ◽  
Classification Model ◽  
Classification Problems ◽  
Accuracy Rate ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
And Training

Supervised learning in Machine learning is used to overcome classification problems with the Artificial Neural Network (ANN) approach. ANN has a few weaknesses in the operation and training process if the amount of data is large, resulting in poor classification accuracy. The results of the classification accuracy of Artificial Neural Networks will be better by using boosting. This study aims to develop a Boosting Feedforward Neural Network (FANN) classification model that can be implemented and used as a form of classification model that results in better accuracy, especially in the classification of the inaccuracy of Terbuka University students. The results showed the level of accuracy produced by the Feedforward Neural Network (FFNN) method had an accuracy rate of 72.93%. The application of boosting on FFN produces the best level of accuracy which is 74.44% at 500 iterations

scholarly journals Flower Identification and Classification using Computer Vision and Machine Learning Techniques

2019 ◽  
Vol 8 (6) ◽  
pp. 277-285
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Classification Accuracy ◽  
Machine Learning Techniques ◽  
Accuracy Rate ◽  
Disk Flower ◽  
Meaningful Information ◽  
Learning Techniques ◽  
Hybrid Methodology

Computer vision techniques plays an important role in extracting meaningful information from images. A process of extraction, analysis, and understanding of information from images may accomplished by an automated process using computer vision and machine learning techniques. The paper proposed a hybrid methodology using MKL – SVM with multi-label classification that is experimented on a dataset contained 25000 flower images of 102 different spices. Basic and morphology features including color, size, texture, petal type, petal count, disk flower, corona, aestivation of flower and flower class are extracted to increase the classification accuracy. Various classifiers are applied on extracted feature set and their performance are discussed. The result of MKL – SVM with multi-label classification is very promising with 76.92% as an accuracy rate. In brief, this paper attempts to explore a novel morphology for feature extraction and the applicability of symbolic representation schemes along with different classification strategies for effective multi-label classification of flower spices

scholarly journals Unsupervised Learning Method for Events Identification in φ-OTDR

2021 ◽  
Author(s):  
Jie Zhang ◽  
Xiaoting Zhao ◽  
Yiming Zhao ◽  
Xiang Zhong ◽  
Yidan Wang ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Classification Accuracy ◽  
Fiber Optic ◽  
Vibration Sensor ◽  
Learning Method ◽  
Clustering Method ◽  
Interesting Application ◽  
Cluster Number

Abstract In this paper, an unsupervised-learning method for events-identification in φ-OTDR fiber-optic distributed vibration sensor is proposed. The different vibration-events including blowing, raining, direct and indirect hitting, and noise-induced false vibration are clustered by the k-means algorithm. The equivalent classification accuracy of 99.4% has been obtained, compared with the actual classes of vibration-events in the experiment. With the cluster-number of 3, the maximal Calinski-Harabaz index and Silhouette coefficient are obtained as 2653 and 0.7206, respectively. It is found that our clustering method is effective for the events-identification of φ-OTDR without any prior labels, which provides an interesting application of unsupervised-learning in self-classification of vibration-events for φ-OTDR.

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