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 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 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 Chinese Language Feature Analysis Based on Multilayer Self-Organizing Neural Network and Data Mining Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiujin Yu ◽  
Shengfu Liu ◽  
Hui Zhang
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Feature Recognition ◽  
High Efficiency ◽  
Chinese Language ◽  
Strong Support ◽  
Superior Performance ◽  
Feature Analysis ◽  
Data Mining Techniques ◽  
Self Organizing

As one of the oldest languages in the world, Chinese has a long cultural history and unique language charm. The multilayer self-organizing neural network and data mining techniques have been widely used and can achieve high-precision prediction in different fields. However, they are hardly applied to Chinese language feature analysis. In order to accurately analyze the characteristics of Chinese language, this paper uses the multilayer self-organizing neural network and the corresponding data mining technology for feature recognition and then compared it with other different types of neural network algorithms. The results show that the multilayer self-organizing neural network can make the accuracy, recall, and F1 score of feature recognition reach 68.69%, 80.21%, and 70.19%, respectively, when there are many samples. Under the influence of strong noise, it keeps high efficiency of feature analysis. This shows that the multilayer self-organizing neural network has superior performance and can provide strong support for Chinese language feature analysis.

Informational Data Mining

2013 ◽  
pp. 58-65
Author(s):  
Feyza Gürbüz ◽  
Fatma Gökçe Önen
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Knowledge Discovery ◽  
Major Change ◽  
Research Community ◽  
Data Sources ◽  
Accurate Information ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
Information Strategies

The previous decades have witnessed major change within the Information Systems (IS) environment with a corresponding emphasis on the importance of specifying timely and accurate information strategies. Currently, there is an increasing interest in data mining and information systems optimization. Therefore, it makes data mining for optimization of information systems a new and growing research community. This chapter surveys the application of data mining to optimization of information systems. These systems have different data sources and accordingly different objectives for knowledge discovery. After the preprocessing stage, data mining techniques can be applied on the suitable data for the objective of the information systems. These techniques are prediction, classification, association rule mining, statistics and visualization, clustering and outlier detection.

Data Mining

2011 ◽  
pp. 921-926
Author(s):  
Sherry Y. Chen ◽  
Xiaohui Liu
Keyword(s):  
Data Mining ◽  
Electronic Commerce ◽  
New Technologies ◽  
Research Area ◽  
Future Research ◽  
Data Mining Techniques ◽  
Wide Range ◽  
Degree Of Confidence ◽  
Common Application ◽  
Existing Data

There is an explosion in the amount of data that organizations generate, collect, and store. Organizations are gradually relying more on new technologies to access, analyze, summarize, and interpret information intelligently. Data mining, therefore, has become a research area with increased importance (Amaratunga & Cabrera, 2004). Data mining is the search for valuable information in large volumes of data (Hand, Mannila, & Smyth, 2001). It can discover hidden relationships, patterns, and interdependencies and generate rules to predict the correlations, which can help the organizations make critical decisions faster or with a greater degree of confidence (Gargano & Ragged, 1999). There is a wide range of data mining techniques, which has been successfully used in many applications. This article is an attempt to provide an overview of existing data mining applications. The article begins by explaining the key tasks that data mining can achieve. It then moves to discuss applications domains that data mining can support. The article identifies three common application domains, including bioinformatics, electronic commerce, and search engines. For each domain, how data mining can enhance the functions will be described. Subsequently, the limitations of current research will be addressed, followed by a discussion of directions for future research.

Visual Data Mining for Collaborative Filtering

2017 ◽  
pp. 217-235 ◽  
Author(s):  
Marenglen Biba ◽  
Narasimha Rao Vajjhala ◽  
Lediona Nishani
Keyword(s):  
Data Mining ◽  
Collaborative Filtering ◽  
State Of The Art ◽  
The State ◽  
Visual Exploration ◽  
Visual Data ◽  
Visual Data Mining ◽  
Data Mining Techniques ◽  
Existing Data

This book chapter provides a state-of-the-art survey of visual data mining techniques used for collaborative filtering. The chapter begins with a discussion on various visual data mining techniques along with an analysis of the state-of-the-art visual data mining techniques used by researchers as well as in the industry. Collaborative filtering approaches are presented along with an analysis of the state-of-the-art collaborative filtering approaches currently in use in the industry. Visual data mining can provide benefit to existing data mining techniques by providing the users with visual exploration and interpretation of data. The users can use these visual interpretations for further data mining. This chapter dealt with state-of-the-art visual data mining technologies that are currently in use apart. The chapter also includes the key section of the discussion on the latest trends in visual data mining for collaborative filtering.

Visual Data Mining for Collaborative Filtering

2017 ◽  
pp. 1274-1292
Author(s):  
Marenglen Biba ◽  
Narasimha Rao Vajjhala ◽  
Lediona Nishani
Keyword(s):  
Data Mining ◽  
Collaborative Filtering ◽  
State Of The Art ◽  
The State ◽  
Visual Exploration ◽  
Visual Data ◽  
Visual Data Mining ◽  
Data Mining Techniques ◽  
Existing Data

This book chapter provides a state-of-the-art survey of visual data mining techniques used for collaborative filtering. The chapter begins with a discussion on various visual data mining techniques along with an analysis of the state-of-the-art visual data mining techniques used by researchers as well as in the industry. Collaborative filtering approaches are presented along with an analysis of the state-of-the-art collaborative filtering approaches currently in use in the industry. Visual data mining can provide benefit to existing data mining techniques by providing the users with visual exploration and interpretation of data. The users can use these visual interpretations for further data mining. This chapter dealt with state-of-the-art visual data mining technologies that are currently in use apart. The chapter also includes the key section of the discussion on the latest trends in visual data mining for collaborative filtering.

EEG Data Mining Using PCA

2011 ◽  
pp. 161-180 ◽  
Author(s):  
Lenka Lhotská ◽  
Vladimír Krajca ◽  
Jitka Mohylová ◽  
Svojmil Petránek ◽  
Václav Gerla
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Principal Components ◽  
The Neural Network ◽  
Eeg Data ◽  
Eigen Decomposition ◽  
Electroencephalogram Eeg ◽  
Epileptic Spike ◽  
Eeg Signal Processing

This chapter deals with the application of principal components analysis (PCA) to the field of data mining in electroencephalogram (EEG) processing. The principal components are estimated from the signal by eigen decomposition of the covariance estimate of the input. Alternatively, they can be estimated by a neural network (NN) configured for extracting the first principal components. Instead of performing computationally complex operations for eigenvector estimation, the neural network can be trained to produce ordered first principal components. Possible applications include separation of different signal components for feature extraction in the field of EEG signal processing, adaptive segmentation, epileptic spike detection, and long-term EEG monitoring evaluation of patients in a coma.

Sign in / Sign up

Export Citation Format

Share Document