scholarly journals Score Prediction Model of MOOCs Learners Based on Neural Network

2018 ◽  
Vol 13 (10) ◽  
pp. 171
Author(s):  
Yuan Zhang ◽  
Wenbo Jiang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Learning Process ◽  
Clustering Algorithm ◽  
Dropping Out ◽  
Personalized Service ◽  
Learning Efficiency ◽  
Learning Rules

Through analyzing the behavior data of MOOCs learners, a MOOCs learner's score prediction model is constructed based on clustering algorithm and neural network in this paper. By using this model, we can find out the neglected information and hidden learning rules in the MOOCs learning process. The model can provide personalized guidance for each user and improve learning efficiency. The model can provide personalized service to help learners form personalized learn-ing strategies, and it also can alert learners with low grades and risk of dropping out.

The Algorithm of Quadratic Junction Neural Network

2013 ◽  
Vol 462-463 ◽  
pp. 438-442
Author(s):  
Ming Gu
Keyword(s):  
Neural Network ◽  
Clustering Algorithm ◽  
Artificial Data ◽  
Data Set ◽  
Number Of Clusters ◽  
Learning Rules ◽  
The Neural Network ◽  
Hierarchical Clustering Algorithm ◽  
Clustering Data ◽  
Structure Properties

Neural network with quadratic junction was described. Structure, properties and unsupervised learning rules of the neural network were discussed. An ART-based hierarchical clustering algorithm using this kind of neural networks was suggested. The algorithm can determine the number of clusters and clustering data. A 2-D artificial data set is used to illustrate and compare the effectiveness of the proposed algorithm and K-means algorithm.

scholarly journals A 4D Trajectory Prediction Model Based on the BP Neural Network

2019 ◽  
Vol 29 (1) ◽  
pp. 1545-1557 ◽  
Author(s):  
Zhi-Jun Wu ◽  
Shan Tian ◽  
Lan Ma
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Clustering Algorithm ◽  
Spline Interpolation ◽  
Absolute Error ◽  
Maximum Absolute Error ◽  
Trajectory Prediction ◽  
Model Based ◽  
Crossing Point

Abstract To solve the problem that traditional trajectory prediction methods cannot meet the requirements of high-precision, multi-dimensional and real-time prediction, a 4D trajectory prediction model based on the backpropagation (BP) neural network was studied. First, the hierarchical clustering algorithm and the k-means clustering algorithm were adopted to analyze the total flight time. Then, cubic spline interpolation was used to interpolate the flight position to extract the main trajectory feature. The 4D trajectory prediction model was based on the BP neural network. It was trained by Automatic Dependent Surveillance – Broadcast trajectory from Qingdao to Beijing and used to predict the flight trajectory at future moments. In this paper, the model is evaluated by the common measurement index such as maximum absolute error, mean absolute error and root mean square error. It also gives an analysis and comparison of the predicted over-point time, the predicted over-point altitude, the actual over-point time and the actual over-point altitude. The results indicate that the predicted 4D trajectory is close to the real flight data, and the time error at the crossing point is no more than 1 min and the altitude error at the crossing point is no more than 50 m, which is of high accuracy.

scholarly journals Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS Data

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
He Ma ◽  
Yi Zuo ◽  
Tieshan Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Clustering Algorithm ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Feature Representation ◽  
Trajectory Prediction ◽  
Research Issues ◽  
The Mean ◽  
Cubic Spline Curve

With the increasing application and utility of automatic identification systems (AISs), large volumes of AIS data are collected to record vessel navigation. In recent years, the prediction of vessel trajectories has become one of the hottest research issues. In contrast to existing studies, most researchers have focused on the single-trajectory prediction of vessels. This article proposes a multiple-trajectory prediction model and makes two main contributions. First, we propose a novel method of trajectory feature representation that uses a hierarchical clustering algorithm to analyze and extract the vessel navigation behavior for multiple trajectories. Compared with the classic methods, e.g., Douglas–Peucker (DP) and least-squares cubic spline curve approximation (LCSCA) algorithms, the mean loss of trajectory features extracted by our method is approximately 0.005, and it is reduced by 50% and 30% compared to the DP and LCSCA algorithms, respectively. Second, we design an integrated model for simultaneous prediction of multiple trajectories using the proposed features and employ the long short-term memory (LSTM)-based neural network and recurrent neural network (RNN) to pursue this time series task. Furthermore, the comparative experiments prove that the mean value and standard deviation of root mean squared error (RMSE) using the LSTM are 4% and 14% lower than those using the RNN, respectively.

Thermal error analysis of taurenEDM machine tool based on FCM fuzzy clustering and RBF neural network

2021 ◽  
pp. 1-12
Author(s):  
Jianyong Liu ◽  
Yanhua Cai ◽  
Qinjian Zhang ◽  
Haifeng Zhang ◽  
Hu He ◽  
...  
Keyword(s):  
Neural Network ◽  
Temperature Field ◽  
Prediction Model ◽  
Machine Tool ◽  
Temperature Measurement ◽  
Reference Point ◽  
Clustering Algorithm ◽  
Rbf Neural Network ◽  
Thermal Error ◽  
Thermal Errors

A method that combines temperature field detection, adaptive FCM (Fuzzy c-means) clustering algorithm and RBF (Radial basis function network) neural network model is proposed. This method is used to analyze the thermal error of the spindle reference point of the taurenEDM (Electro-discharge machining) machine tool. The thermal imager is used to obtain the temperature field distribution of the machine tool while the machine tool simulates actual operating conditions. Based on this, the arrangement of temperature measurement points is determined, and the temperature data of the corresponding measurement points are got by temperature sensors. In actual engineering, too many temperature measurement points can cause problems such as too high cost, too much wiring. And normal processing can be affected. In order to establish that the thermal error prediction model of the machine tool spindle reference point can meet the actual engineering needs, the adaptive FCM clustering algorithm is used to optimize the temperature measurement points. While collecting the temperatures of the optimized temperature measurement points, the displacement sensors are used to detect the thermal deformation data in X, Y, Z directions of the spindle reference position. Based on the test data, the RBF neural network thermal errors prediction model of the machine tool spindle reference point is established. Then, the test results are used to verify the accuracy of the thermal errors analysis model. The research method in this paper provides a system solution for thermal error analysis of the taurenEDM machine tool. And this builds a foundation for real-time compensation of the machine tool’s thermal errors.

Stock Market Prediction using Data Mining Techniques

2020 ◽  
pp. 310-313
Author(s):  
Karunesh Makker ◽  
Prince Patel ◽  
Hrishikesh Roy ◽  
Sonali Borse
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Prediction Model ◽  
Efficient Market Hypothesis ◽  
Deterministic System ◽  
Vast Number ◽  
Factors Influencing ◽  
Number Of Factors ◽  
Using Data ◽  
Robust Prediction

Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding Technical indicators will guide the investor to minimize the risk and reap better returns.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

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