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DNA Microarray data is a high-dimensional data that enables the researchers to analyze the expression of many genes in a single reaction quickly and in an efficient manner. Its characteristics such as small sample size, class imbalance, and data complexity causes it difficult to classified. Feature selection is a process that automatically selects features that are most relevant to the predictive...
The article describes typical problems solved by means of inductive modeling, provides information on the development of this scientific direction in Ukraine and abroad, characterizes the basic fundamental, applied and technological achievements, and formulates the most promising ways of further research.
Color transfer techniques have been widely developed to manipulate color information of image data in many applications. In this paper, color transfer research is presented for camouflage applications, along with the challenges facing many state-of-the-art color transfer methods. To address these challenges, a new fast color transfer algorithm is proposed. This new fast color transfer algorithm follows...
Classification is one of the important tasks in Data Mining or Knowledge Discovery with prolific applications. Satisfactory classification depends on characteristics of the dataset too. Numerical and nominal attributes are commonly occurred in the dataset. However, classification performance may be aided by discretization of numerical attributes. At present, several discretization methods and numerous...
This paper proposes a classification algorithm based on ensemble neural networks. In the training phase, the proposed algorithm uses a random number of training data to develop multiple random artificial neural network (ANN) models until those ANN models converge. Those models with lower accuracy than the threshold are filtered out. The remaining highly accurate models will be used to predict the...
Business Intelligence proves to be extremely useful to a vendor in order to raise the sales and product performance of products. It is an essential aspect to take business conclusions into account. There is massive data on social media that can be exploited to give us useful information. The present paper deals with a system created to exhibit intelligence. This system speculates the sales performance...
This paper presents a predictive model which to predict the trends of stock prices using Data Mining techniques. This research will allow the investor to make a more informed decision to buy and sell stocks, and in the most appropriate period. The predictive concept in this work implies learning historical price patterns, indicators, and behavior; and then predicting the future trends in one, five,...
Supervised methods for inferring gene regulatory networks (GRNs) perform well with good training data. However, when training data is absent, these methods are not applicable. Unsupervised methods do not need training data but their accuracy is low. In this paper, we combine supervised and unsupervised methods to infer GRNs using time-series gene expression data. Specifically, we use results obtained...
In this paper, a self-healing scheme in active distribution network (ADN) with inverter-based distributed generators (IBDGs) based on multi-agent and big data is proposed. The multi-agent system (MAS), big data storage and mining technology are used to accomplish fault discrimination, fault localization, isolation and service restoration. In this paper, the use of a new type of the relay which takes...
A challenge task of data mining is to process massive data in the big data era. MapReduce is an attractive model to overcome this challenge. This paper presents a new method to accelerate the process of learning Markov blanket Bayesian network(MBBN). Markov blanket is a better model type of Bayesian network in some complex datasets. The time and space cost of learning Markov blanket is large, and...
This paper presents a data mining technique for qualitative analysis of Hodgkin-Huxley model of cell excitability. Such problem cannot be solved analytically. Therefore we apply Monte-Carlo techniques for the generation of model parameters, and use data mining algorithm for classification of learning tuples obtained. As a result we attain a decision tree capable of classifying the excitability depending...
Machine learning has become a powerful tool in real applications such as decision making, sentiment prediction and ontology engineering. In the form of learning strategies, machine learning can be specialized into two types: supervised learning and unsupervised learning. Classification is a special type of supervised learning task, which can also be referred to as categorical prediction. In other...
Vocational is one of education types in Indonesia. Graduates from vocational school need to have enough motivation to get into working environment either as employees or as entrepreneurs. In vocational education, it is important to monitor students' motivation and achievement. It will help to understand students' condition and give an overview in setting the appropriate program for the students. This...
As social media services (e.g. Wikipedia, Facebook, Twitter, Linkedin, and so on) become more and more popular, it is of greater research interest to raise the efficiency of using Sentiment Analysis to predict future opinion trends. Based on the naïve Bayes classifier, this research proposes a novel emotion classifier, CCLM (Combined CKIP Language Model), to enhance the precision of opinion classification...
The rapid computerization and advancement in the technology has led to huge amount of data in the databases. Research has shown that the amount of data in the world doubles in every 20 months. However, this available data consists of large number of noise values and thus, cannot be directly used. The extraction of information from the vast pool of data has emerged a major challenge.
Student performance classification is a challenging task for teacher and stakeholder for better academic planning and management. Data mining can be used to find knowledge from student data to improve the performance of classifying model. Before applying a classification model, feature selection method is proposed in data preprocessing process to find out the most significant and intrinsic features...
The paper presents a data mining technique for qualitative analysis of functional differential equations of compartmental type. As a result we get the decision tree able to classify the system behaviour depending on relations between initial conditions and between rate constants. Antitumour immunity example is presented.
In big data universities, an understanding of how the individual learning style and preferences interacts with the instructional medium presented is needed. In this study we examined the VARK learning style inventory using the variable-centered, person-centered and social approaches. We worked on a big “data set” which encompasses two data sources the first was LMS while the second was social media...
Dynamic security assessment (DSA) is an important issue in modern power system security analysis. This paper proposes a novel pattern discovery (PD)-based fuzzy classification scheme for the DSA. First, the PD algorithm is improved by integrating the proposed centroid deviation analysis technique and the prior knowledge of the training data set. This improvement can enhance the performance when it...
With the semi-structured data rapidly growing, it is crucial to obtain valuable information for different applications. So many data mining methods are proposed and the frequent sub trees mining is an important and typical method. The current mining methods demand substantial computational time and space, and return a huge number of patterns, but some important sub trees are often missed and some...
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