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Extreme Learning Machine (ELM) is a neural network architecture with Single Layer Feed-forward Neural Network (SLFN). For meaningful results, the structure of ELM has to be optimized through the inclusion of regularization and the ℓ2 — norm based regularization is mostly used. ℓ2-norm based regularization achieves better performance than the traditional ELM. The estimate of the regularization parameter...
Structured gait patterns are currently used as a biometric technique to recognize individuals and in building appropriate exoskeleton technologies. In this study, the features involved in gait were extracted and analyzed. Multiple accelerometers were used to collect the data which was then used to identify gait at various axial positions form healthy volunteers with total of 60 trails. Using machine...
Data anonymization is a technique used to increase the assurance that private data is not accessible to third parties. In data mining processes, anonymization can impact the results, since anonymized data may hinder the data analysis performed by algorithms commonly used in this context. The goal of this Practical Experience Report is to evaluate the accuracy and per-formance impact of data anonymization...
Two GF-1 WFV images on August 3, 2015 and October 2, 2015 were selected to extract the cultivated area of paddy rice in Jianhu county of Jiangsu Province. Vegetation indexes were extracted from the original spectrum data in order to extract paddy rice area with Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM) and Classification and Regression Trees (CART). The extraction accuracy...
Neuroscience researchers have a keen interest in finding the connection between various brain regions of an organism. Researchers all across the globe are finding new connections everyday and it is very difficult to keep track of all those, so it is important to create a centralized system which is able to give the relation between brain entities. Databases like PubMed contains abstracts and references...
Currently, many of the elements that surround us in daily life need software systems that work from the information available in the domain (data-driven application domains) by performing a process of data mining from it. Between the data mining techniques used in everyday problems we find the k-Nearest Neighbors technique. However, in domains and real situations it is very common to find vague, ambiguous...
Objective: This paper presents a novel supervised regularized canonical correlation analysis, termed as CuRSaR, to extract relevant and significant features from multimodal high dimensional omics datasets. Methods: The proposed method extracts a new set of features from two multidimensional datasets by maximizing the relevance of extracted features with respect to sample categories and significance...
Integration of automated ECG analysis techniques with the home monitoring devices can incorporate the necessary “smartness” which can help in earlier diagnosis of Myocardial Infarction (MI), better known as heart attack, thus reducing the mortality rate. Most of the reported techniques suffer from the disadvantages of large feature dimension, computational complexity of the features and complex classifiers...
This paper aims to identify lead users from an online user innovation community. Based on three dimensions of user characteristics — user activeness, community influence, and user relations, a Random Forest classification model for lead user identification is proposed. Using the data from the MIUI forum of Xiaomi community, this model is tested. The result shows that Random Forest classification based...
Extinction profile (EP) is an effective feature extraction method which can well preserve the geometrical characteristics of a hyperspectral image (HSI) and by extracting the EP from first three independent components (ICs) of an HSI, three correlated and complementary groups of EP features can be constructed. In this paper, an EPs fusion (EPs-F) strategy is proposed for HSI classification by exploring...
Feature selection is an important technique, which applied in those areas where amount of data is too large to analyze and it becomes crucial to minimize large datasets. Bacteria Foraging Optimization (BFO) algorithm is an optimization algorithm, which get its inspiration from Escherichia Coli bacteria. This work proposed a hybrid approach of BFO algorithm and Naive Bayes classification. To evaluate...
In the quest of developing more accurate methodologies for Earth Observation (EO) image retrieval, visualization and information content exploration, a deep understanding of the data being analyzed is needed. In this paper we propose a simple but efficient visual data mining methodology that can be used for these tasks. Our solution consists in a patch-based feature extraction to derive image features...
A decision tree is an important classification technique in data mining classification. Decision trees have proved to be valuable tools for the classification, description, and generalization of data. J48 is a decision tree algorithm which is used to create classification model. J48 is an open source Java implementation of the C4.5 algorithm in the Weka data mining tool. In this paper, we present...
Recruitment and selection of new employees rank to the important processes of human potential management and development. Especially the process of employee selection prepares proper conditions for a successful work performance and decides on a future progress-ability of the organizations. In a unique sector of private security, the precise realization of employee selection can solve one of the most...
In the Area of Security, Intrusion Detection System (IDS) form an individual trailing and plays an essential role in information Security. As the usability of the internet among the users in a wide area is increasing day by day so as the importance of security and to keep the system aware of the malicious activities is also increasing. It has the following limitations on low detection rate, high false...
Currently, many educational institutions are highly oriented to improve the quality of education and students? learning achievement-examination result. To fulfil such intention, predicting students? performance by analyzing their learning behavior is one of the best way can be taken into account. Once the performance was predicted, it will be easy for teachers, school authority or other related parties...
In this paper we propose a methodology for extracting complex sales expert rules by analyzing the data from the past lost/won deals stored in Customer Relationship Management Systems.
Cardiovascular disease is one of the leading causes of death in the United States. It is critical to identify the risk factors associated with cardiovascular diseases and to alert individuals before they experience a heart attack. In this paper, we propose RFMiner, a risk factor discovery and mining framework for identifying significant risk factors using integrated measures. We provide the blueprints...
When individual-level health data is shared in biomedical research the privacy of patients and probands must be protected. This is typically achieved with methods of data de-identification, which transform data in such a way that formal guarantees about the degree of protection from re-identification can be provided. In the process it is important to minimize loss of information to ensure that the...
Bayesian Network algorithms are widely applied in the fields of bioinformatics, document classification, big data, and marketing informatics. In this paper, several Bayesian Network algorithms are evaluated, including Naive Bayes, Tree Augmented Naive Bayes, k-BAN, and k-BAN with Order Swapping. The algorithms are implemented using Scala and compared with the bnlearn library in R and Weka. Several...
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