scholarly journals Applying Data Mining Techniques on Continuous Sensed Data for Daily Living Activity Recognition

2015 ◽  
Vol 738-739 ◽  
pp. 191-196
Author(s):  
Yun Jie Li ◽  
Hui Song
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Tree ◽  
Daily Living ◽  
Tree Model ◽  
Data Set ◽  
Data Mining Techniques ◽  
Daily Living Activity ◽  
The Neural Network ◽  
Better Than

In this paper, several data mining techniques were discussed and analyzed in order to achieve the objective of human daily activities recognition based on a continuous sensing data set. The data mining techniques of decision tree, Naïve Bayes and Neural Network were successfully applied to the data set. The paper also proposed an idea of combining the Neural Network with the Decision Tree, the result shows that it works much better than the typical Neural Network and the typical Decision Tree model.

scholarly journals Comparison and analysis of applications of ID3, CART decision tree models and neural network model in medical diagnosis and prognosis evaluation

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Zeye Liu ◽  
◽  
Xiangbin Pan ◽  
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Network Model ◽  
Neural Network Model ◽  
Data Preprocessing ◽  
Tree Model ◽  
Maximum Increase ◽  
Data Set ◽  
The Neural Network ◽  
Auc Value

Objective: To analyze the performance of each algorithm model under different processing conditions such as data preprocessing (standardization, normalization and regularization), balancing and shuffling based on the data attributes of three common research types in clinical studies as the research examples. To compare and analyze advantages and disadvantages of the decision tree model and the neural network model in clinical studies as well as their scope of application. Methods: Python was used to construct ID3 and CART decision tree models. Three typical clinical research data sets were downloaded from UCI and used to perform data preprocessing, balancing, and shuffling on the models. The model evaluation indexes included time complexity, accuracy, precision, recall and F1-Score. As for visualization, the model results, confusion matrix and ROC curve were drawn. The importance rankings of different data set attributes on the model results were also analyzed. In addition, one typical data set was selected to conduct the comparative analysis by using the neural network model. SPSS was used to perform the significance analysis of different data processing schemes. The SPSS platform was used to conduct the statistical test of the results. Results: (1) There were a total of 96 decision trees based on 2 decision tree algorithms, 3 data sets, 4 types of data preprocessing, 2 balanced choices and 2 shuffling choices. (2) The AUC value of the Thoracic Surgery Data Set significantly increased after balancing with a maximum increase of 0.3, which was statistically significant (P <0.01). (3) The AUC value of the Breast Cancer Wisconsin (Diagnostic) Data Set generally increased after normalization, which decreased after regularization. The maximum decrease was 0.6 without statistical significance (P = 0.3). (4) The AUC value of the Statlog (Heart) Data Set increased after regularization but it was not statistically significant. The maximum increase was 0.03. (5) Data balancing and shuffling can increase the AUC value. (6) The performance of the neural network model was between the best and worst performance of the decision tree model.

Data mining of digital phytosanitary

2021 ◽  
Author(s):  
T. Z. Ibragimov ◽  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Decision Tree ◽  
Bayesian Classifier ◽  
Design Parameters ◽  
Data Set ◽  
Septoria Leaf Blotch ◽  
Leaf Blotch ◽  
Artificial Neural

methods of data mining were used to predict the Septoria leaf blotch of wheat. A system has been developed that allows parallel forecasting with the same data set using the methods of an artificial neural network, a decision tree, and a naive Bayesian classifier. The system allows you to interactively adjust the design parameters for each of the methods, see the results obtained and evaluate their effectiveness.

scholarly journals Application of intellectual data analysis methods for digital educational platform

2020 ◽  
pp. 1-5
Author(s):  
Kristina Zhatkina ◽  
Oksana Kreider
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Data Analysis ◽  
Educational Services ◽  
Data Mining Techniques ◽  
Analysis Methods ◽  
The Neural Network ◽  
Competency Based ◽  
Using Data ◽  
Data Analysis Methods

This article describes the possibility of using data mining techniques. In order to join new carpet participants, it is necessary to understand that the system of interaction with them is public educational services. To implement digital educational platforms, it is proposed to create an agent that collects information about sites, and also selects and tests the architecture of the neural network to build an individual trajectory that is trained using the competency-based model.

scholarly journals PREDICTIVE MODELING OF TECHNOLOGICAL DEVELOPMENT TRENDS

2020 ◽  
Vol 55 ◽  
pp. 103-108
Author(s):  
Alla G. Kravets ◽  
◽  
Natalia A. Salnikova ◽  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Predictive Modeling ◽  
Technological Development ◽  
Data Sources ◽  
Development Trends ◽  
Data Mining Techniques ◽  
The Neural Network ◽  
Existing Data

In the work, the problem of forecasting technological development trends was considered. A review of the sources of the global patent space, an analysis of technological development trends, a survey of data sources for training the neural network were carried out. Existing data mining techniques were analyzed for more accurate and faster forecasting. A module for predictive modeling of trends in technological development was developed, algorithms for the module for predictive modeling of trends in technological development were described.

scholarly journals Evaluation of Data Mining Techniques and Its Fusion with IoT Enabled Smart Technologies for Effective Prediction of Available Parking Space

2021 ◽  
Vol 12 (4) ◽  
pp. 187-197
Author(s):  
Anchal Dahiya ◽  
Pooja Mittal
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Support Vector ◽  
Learning Approaches ◽  
Parking Space ◽  
Data Mining Technique ◽  
Data Set ◽  
Data Mining Techniques ◽  
Hard Times ◽  
Smart Technologies

After experiencing the hard times of pandemic situations we learned that if we could have a smart system that can help us in automatic parking of the vehicles then it could be a great help to society. This idea motivated us to carry out this current work. Though, nowadays, in almost every application domain, IoT techniques are the buzzword. IoT techniques can also be used to achieve efficacy in predicting free available parking space in advance. But the biggest challenge with IoT techniques is that they generate numerous data, which makes its analysis intangible. It was realized that if IoT techniques can be fused with outperforming data mining techniques, more efficient predictions can be performed. Thus, for this purpose, the main objective of our paper is to firstly, select the most appropriate data mining technique, based on performance evaluation, and then to perform prediction of available parking space in advance by fusing it with IoT techniques. Due to the busy schedule, the drivers need to get information about free parking spaces in advance by using smart phones. With the help of this information, it will be easy for the drivers to park their vehicle in the exact location without wasting their precious time and will maintain social distancing in crowded areas too. Data mining techniques can play an important role in the prediction of available parking space, by extracting only relevant and important information when applied to the given dataset. For this purpose, a comparative analysis of five data mining techniques such as the Support Vector Machine, K- Nearest approach, Decision Tree, Random Forest, and Ensemble learning approaches are applied on PK lot data set by using Python language. For calculation of result anaconda (spyder) is used as a supportive tool. The main outcome of the paper is to find the technique that will give better results for the prediction of the available space and if we fused data mining techniques with IoT technologies results are improvised. Evaluation parameters that are used for finding the best technique are precision, recall, accuracy, and F1-Score. For numerical calculation of the results, the k-fold cross-validation method is used. As the empirical results are calculated using the Pk lot dataset, the decision tree outperformed the best among all the techniques that are selected for analysis.

scholarly journals Customer Retention in Banking Sector using Decision Tree-Neuro Based System

2020 ◽  
Vol 9 (3) ◽  
pp. 481-485
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Banking Sector ◽  
Learning Algorithm ◽  
Decision Tree Model ◽  
Tree Model ◽  
Data Mining Techniques ◽  
Individual Decisions ◽  
Bank Marketing

All the bank marketing campaigns mostly deals with large amount of data. when they need to deal with huge electronic data of customers, then it is very difficult to analyze the data manually or by human analyst. Here comes the picture of data mining techniques to deal with the large amount of data and to come up with useful data which helps in decision making process. All the data mining techniques helps in analyzing the data. some of the techniques that can be used for this bank marketing campaigns are naive bayes, logistics regression technique and Decision tree model technique etc. among all these techniques decision Tree model gives the best solution in analyzing the human decisions. Artificial neural networks is a learning algorithm which learns from multiple individual decisions and their judgements, thus aggregates and generalizes the customers decision making knowledge.

NEURAL NETWORK BASED AUTOMATIC DIAGNOSIS OF CHILDREN WITH BRAIN DYSFUNCTION

2001 ◽  
Vol 11 (04) ◽  
pp. 361-369 ◽  
Author(s):  
SUNGZOON CHO ◽  
MIN SUP SHIN
Keyword(s):  
Neural Network ◽  
Discriminant Analysis ◽  
Decision Tree ◽  
Experimental Test ◽  
Bayesian Learning ◽  
Brain Dysfunction ◽  
Accuracy Rate ◽  
The Neural Network ◽  
Important Input ◽  
Better Than

This paper proposes the use of multilayer perceptron for brain dysfunction diagnosis. The performance of MLP was better than that of Discriminant Analysis and Decision Tree classifiers, with an 85% accuracy rate in an experimental test involving 332 subjects. In addition, the neural network employing Bayesian learning was able to identify the most important input variable. These two results demonstrate that the neural network can be effectively used in the diagnosis of children with brain dysfunction.

scholarly journals Analysis for Clients Churn of Credit Cards in Model Construction in Banking Industry

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Jianyao Liu
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Financial Market ◽  
Banking Industry ◽  
Credit Cards ◽  
Training Data ◽  
Tree Model ◽  
Data Set ◽  
Mining Technology ◽  
Testing Data

Data mining technology has been more and more important in the economics and financial market. Helping the banks to predict a customers’ behavior, which is that whether the existing customers will continue use their credit cards or not, we utilize the data mining technology to construct a convenient and effective model, Decision Tree. By using our Decision Tree model, which can classify the customers according to different features step by step, the banks are able to predict the customers’ behavior well. The main steps of our experiment includes collecting statistics from the bank, utilizing Min-Max normalization to preprocess the data set, employing the training data set to construct our model, examining the model by testing data set, and analyzing the results.

scholarly journals Performance Comparison between Deep Learning and Optical Flow-Based Techniques for Nowcast Precipitation from Radar Images

Forecasting ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 194-210 ◽  
Author(s):  
Marino Marrocu ◽  
Luca Massidda
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
State Of The Art ◽  
Performance Comparison ◽  
Data Set ◽  
Radar Images ◽  
The Neural Network ◽  
Meteorological Radar ◽  
One Year ◽  
Better Than

In this article, a nowcasting technique for meteorological radar images based on a generative neural network is presented. This technique’s performance is compared with state-of-the-art optical flow procedures. Both methods have been validated using a public domain data set of radar images, covering an area of about 104 km2 over Japan, and a period of five years with a sampling frequency of five minutes. The performance of the neural network, trained with three of the five years of data, forecasts with a time horizon of up to one hour, evaluated over one year of the data, proved to be significantly better than those obtained with the techniques currently in use.

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