scholarly journals Incremental Approach of Neural Network in Back Propagation Algorithms for Web Data Mining

2017 ◽  
Vol 6 (2) ◽  
pp. 74
Author(s):  
A. P. Tawdar ◽  
M. S. Bewoor ◽  
S. H. Patil
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Text Categorization ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Classification Task ◽  
Text Documents ◽  
Training Time ◽  
Text Document ◽  
Content Mining

Text Classification is also called as Text Categorization (TC), is the task of classifying a set of text documents automatically into different categories from a predefined set. If a text document relates to exactly one of the categories, then it is called as single-label classification task; otherwise, it is called as multi-label classification task. For Information Retrieval (IR) and Machine Learning (ML), TC uses several tools and has received much attention in the last decades. In this paper, first classifies the text documents using MLP based machine learning approach (BPP) and then return the most relevant documents. And also describes a proposed back propagation neural network classifier that performs cross validation for original Neural Network. In order to optimize the classification accuracy, training time. Proposed web content mining methodology in the exploration with the aid of BPP. The main objective of this investigation is web document extraction and utilizing different grouping algorithm. This work extricates the data from the web URL.

scholarly journals A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a MOS-Based E-Nose

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4499 ◽  
Author(s):  
Hao Wei ◽  
Yu Gu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Economic Value ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Machine Learning Algorithms ◽  
Oxide Semiconductor ◽  
Learning Machine

The brown core is an internal disorder that significantly affects the palatability and economic value of Chinese pears. In this study, a framework that includes a back-propagation neural network (BPNN) and extreme learning machine (ELM) (BP-ELMNN) was proposed for the detection of brown core in the Chinese pear variety Huangguan. The odor data of pear were collected using a metal oxide semiconductor (MOS) electronic nose (E-nose). Principal component analysis was used to analyze the complexity of the odor emitted by pears with brown cores. The performances of several machine learning algorithms, i.e., radial basis function neural network (RBFNN), BPNN, and ELM, were compared with that of the BP-ELMNN. The experimental results showed that the proposed framework provided the best results for the test samples, with an accuracy of 0.9683, a macro-precision of 0.9688, a macro-recall of 0.9683, and a macro-F1 score of 0.9685. The results demonstrate that the use of machine learning algorithms for the analysis of E-nose data is a feasible and non-destructive method to detect brown core in pears.

Neural Network-Based Geometry Classification for Navigation Satellite Selection

2003 ◽  
Vol 56 (2) ◽  
pp. 291-304 ◽  
Author(s):  
Dah-Jing Jwo ◽  
Chien-Cheng Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Classification Performance ◽  
Navigation Satellite ◽  
Training Time ◽  
Fast Learning ◽  
Satellite Geometry

The neural networks (NN)-based geometry classification for good or acceptable navigation satellite subset selection is presented. The approach is based on classifying the values of satellite Geometry Dilution of Precision (GDOP) utilizing the classification-type NNs. Unlike some of the NNs that approximate the function, such as the back-propagation neural network (BPNN), the NNs here are employed as classifiers. Although BPNN can also be employed as a classifier, it takes a long training time. Two other methods that feature a fast learning speed will be implemented, including Optimal Interpolative (OI) Net and Probabilistic Neural Network (PNN). Simulation results from these three neural networks are presented. The classification performance and computational expense of neural network-based GDOP classification are explored.

Machine learning for reparameterization of four-site water models: TIP4P-BG and TIP4P-BGT

2021 ◽  
Author(s):  
Hong-fei Ye ◽  
Jian Wang ◽  
Yong-gang Zheng ◽  
Hong-wu Zhang ◽  
Zhen Chen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
High Precision ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Site Water ◽  
Water Models

Two high-precision water models are established based on the combination of a back-propagation neural network and genetic algorithm.

scholarly journals Modify the Accuracy of MODIS PWV in China: A Performance Comparison Using Random Forest, Generalized Regression Neural Network and Back−Propagation Neural Network

2021 ◽  
Vol 13 (11) ◽  
pp. 2215
Author(s):  
Zhaohui Xiong ◽  
Xiaogong Sun ◽  
Jizhang Sang ◽  
Xiaomin Wei
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Water Vapor ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Generalized Regression Neural Network ◽  
Learning Methods ◽  
Machine Learning Methods

Water vapor plays an important role in climate change and water cycling, but there are few water vapor products with both high spatial resolution and high accuracy that effectively monitor the change of water vapor. The high precision Global Navigation Satellite System (GNSS) Precipitable Water Vapor (PWV) is often used to calibrate the high spatial resolution Moderate−resolution Imaging Spectroradiometer (MODIS) PWV to produce new PWV product with high accuracy and high spatial resolution. In addition, the machine learning method has a good performance in modifying the accuracy of MODIS PWV. However, the accuracy improvement of different machine learning methods and different modeling timescale is different. In this article, we use three machine learning methods, namely, the Random Forest (RF), Generalized Regression Neural Network (GRNN), and Back−propagation Neural Network (BPNN) to calibrate MODIS PWV in 2019, at annual and monthly timescales. We also use the Multiple Linear Regression (MLR) method for comparison. The root mean squares (RMSs) at the annual timescale with the three machine learning methods are 4.1 mm (BPNN), 3.3 mm (RF), and 3.9 mm (GRNN), and the average RMSs become 2.9 mm (BPNN), 2.8 mm (RF), and 2.5 mm (GRNN) at the monthly timescale. Those results are all better than the MLR method (5.0 mm at the annual timescale and 4.6 mm at the monthly timescale). When there is an obvious variation pattern in the training sample, the RF method can capture the pattern to achieve the best results since the RF achieves the best performance at the annual timescale. Dividing such samples into several sub−samples each having higher internal consistency could further improve the performance of machine learning methods, especially for the GRNN, since GRNN achieves the best performance at the monthly timescale, and the performance of those three machine learning methods at the monthly timescale is better than that of annual timescale. The spatial and temporal variation patterns of the RMS values are significantly weakened after the modeling by machine learning methods for both three methods.

scholarly journals Big Data Analysis of Facebook Users Personality Reconition using Map Reduce Back Propagation Neural Networks

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Solomon Akinboro ◽  
Isaac K. Ogundoyin ◽  
Ayobami T. Olusesi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Back Propagation ◽  
Threshold Value ◽  
Back Propagation Neural Network ◽  
Classification Model ◽  
Map Reduce ◽  
Support Vector ◽  
Computationally Efficient ◽  
Personality Recognition

Machine learning has been an effective tool to connect networks of enormous information for predicting personality. Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in most research efforts. This research modeled user personality based on set of features extracted from the Facebook data using Map-Reduce Back Propagation Neural Network (MRBPNN). The performance of the MRBPNN classification model was evaluated in terms of five basic personality dimensions: Extraversion (EXT), Agreeableness (AGR), Conscientiousness (CON), Neuroticism (NEU), and Openness to Experience (OPN) using True positive, False Positive, accuracy, precision and F-measure as metrics at the threshold value of 0.32. The experimental results reveal that MRBPNN model has accuracy of 91.40%, 93.89%, 91.33%, 90.43% and 89.13% CON, OPN, EXT, NEU and AGR respectively for personality recognition which is more computationally efficient than Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM). Therefore, personality recognition based on MRBPNN would produce a reliable prediction system for various personality traits with data having a very large instance.  Keywords— Machine learning, Facebook, MRBPNN, Personality Recognition, Neuroticism, Agreeableness.

scholarly journals Machine-Learning Prediction of Underwater Shock Loading on Structures

Computation ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 58
Author(s):  
Zhang ◽  
Drikakis ◽  
Li ◽  
Yan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Laplace Transform ◽  
Back Propagation ◽  
Underwater Explosion ◽  
Back Propagation Neural Network ◽  
Inverse Laplace Transform ◽  
Structural Stiffness ◽  
Parallel Plates ◽  
Stiffness And Damping

Due to the complex physics of underwater explosion problems, it is difficult to derive analytical solutions with accurate results. In this study, a machine-learning method to train a back-propagation neural network for parameter prediction is presented for the first time in literature. The specific problem is the response of a structure submerged in water subjected to shock loads produced by an underwater explosion, with the detonation point being far away from the structure so that the loading wave can be regarded as a planar shock wave. Two rigid parallel plates connected by a linear spring and a linear dashpot that simulate structural stiffness and damping respectively, represent the structure. Taking the Laplace transform of the governing equations, solving the resulting equations, and then taking the inverse Laplace transform, the simplified problem is analyzed theoretically. The coupled ordinary differential equations governing the motion of the system are also solved numerically by the fourth order Runge–Kutta method and then verified by a finite element method using Ansys/LSDYNA. The parametric training with the back-propagation neural network algorithm was conducted to delineate the effects of structural stiffness and damping on the attenuation of shock waves, the cavitation time, and the time of maximum momentum transfer. The prediction results agree well with the validation and test sample results.

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