scholarly journals A Novel Data Prediction Model using Data Weights and Neural Network based on R for Meaning Analysis between Data

2015 ◽  
Vol 18 (4) ◽  
pp. 524-532 ◽  
Author(s):  
Se Hoon Jung ◽  
Jong Chan Kim ◽  
Chun Bo Sim
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Data Prediction ◽  
Using Data ◽  
Meaning Analysis

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.

scholarly journals Development of missing data prediction model for carbon monoxide

2019 ◽  
Vol 15 (1) ◽  
pp. 13-17
Author(s):  
Nurul Latiffah Abd Rani ◽  
Azman Azid ◽  
Muhamad Shirwan Abdullah Sani ◽  
Mohd Saiful Samsudin ◽  
Ku Mohd Kalkausar Ku Yusof ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Carbon Monoxide ◽  
Missing Data ◽  
Prediction Model ◽  
Optimum Number ◽  
Data Prediction ◽  
Artificial Neural ◽  
Input Variables ◽  
Missing Data Prediction

Carbon monoxide (CO) is one of the most important pollutants since it is selected for API calculation. Therefore, it is paramount to ensure that there is no missing data of CO during the analysis. There are numbers of occurrences that may contribute to the missing data problems such as inability of the instrument to record certain parameters. In view of this fact, a CO prediction model needs to be developed to address this problem. A dataset of meteorological and air pollutants value was obtained from the Air Quality Division, Department of Environment Malaysia (DOE). A total of 113112 datasets were used to develop the model using sensitivity analysis (SA) through artificial neural network (ANN). SA showed particulate matter (PM10) and ozone (O3) were the most significant input variables for missing data prediction model of CO. Three hidden nodes were the optimum number to develop the ANN model with the value of R2 equal to 0.5311. Both models (artificial neural network-carbon monoxide-all parameters (ANN-CO-AP) and artificial neural network-carbon monoxide-leave out (ANN-CO-LO)) showed high value of R2 (0.7639 and 0.5311) and low value of RMSE (0.2482 and 0.3506), respectively. These values indicated that the models might only employ the most significant input variables to represent the CO rather than using all input variables.

Optimized recurrent neural network with fuzzy classifier for data prediction using hybrid optimization algorithm: Scope towards diverse applications

2020 ◽  
pp. 2050074
Author(s):  
A. S. Prakaash ◽  
K. Sivakumar
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Prediction Model ◽  
Recurrent Neural Network ◽  
Optimization Algorithm ◽  
Fuzzy Regression ◽  
Hybrid Optimization ◽  
Hybrid Optimization Algorithm ◽  
Data Prediction ◽  
Fuzzy Regression Model

Today, data processing has become a challenging task due to the significant increase in the amount of data collected using various sensors. To put up knowledge and forecast the data, the existing data mining techniques compute all numerical attributes in the memory simultaneously. However, the over-abundance of entire factors in the data makes accurate prediction infeasible. This paper attempts to implement a new data prediction model using an optimized machine learning algorithm. The proposed data prediction model involves four main phases: (a) data acquisition, (b) feature extraction, (c) data normalization, and (d) prediction. Initially, few data from the UCI repository like Bike Sharing Dataset, Carbon Nanotubes, Concrete Compressive Strength, Electrical Grid Stability Simulated Data, and SkillCraft-1 Master Table are collected. Further, the feature extraction process extracts the first-order statistics like mean, median, standard deviation, the maximum value of entire data, and the minimum value of entire data, and the second-order statistics like kurtosis, skewness, energy, and entropy. Next, the data or feature normalization is done to arrange the data within a certain limit. The normalized features are then subjected to a hybrid prediction system by integrating the Recurrent Neural Network (RNN) and Fuzzy Regression model. As a modification, the number of hidden neurons in the RNN and membership limits of the Fuzzy Regression model are optimized by a hybrid optimization algorithm by merging the concepts of Whale Optimization Algorithm (WOA) and Cat Swarm Optimization (CSO), which is called the Whale Updated Seek Mode-based CSO (WS-CSO) algorithm. Then, the efficiency of the optimized hybrid classifier for all-time prediction of data in different applications is confirmed based on its valuable performance and comparative analysis.

scholarly journals Data Prediction Model Based on LSTM Neural Network in Bridge Health Monitoring System

2021 ◽  
Vol 668 (1) ◽  
pp. 012068
Author(s):  
Shihui Xiao ◽  
Shengfang Qiao ◽  
Hang Chen ◽  
Xi Liu ◽  
Yue Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Monitoring System ◽  
Health Monitoring ◽  
Health Monitoring System ◽  
Bridge Health Monitoring ◽  
Model Based ◽  
Data Prediction
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