Wearable Computing: Accelerometers’ Data Classification of Body Postures and Movements

2012 ◽  
pp. 52-61 ◽  
Author(s):  
Wallace Ugulino ◽  
Débora Cardador ◽  
Katia Vega ◽  
Eduardo Velloso ◽  
Ruy Milidiú ◽  
...  
Keyword(s):  
Wearable Computing ◽  
Data Classification ◽  
Body Postures

scholarly journals Performance evaluation of different classification techniques using different datasets

2019 ◽  
Vol 9 (5) ◽  
pp. 3584
Author(s):  
Abdulkadir Özdemir ◽  
Uğur Yavuz ◽  
Fares Abdulhafidh Dael
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Multilayer Perceptron ◽  
Data Classification ◽  
Major Effect ◽  
Classification Techniques ◽  
The Difference ◽  
Two Parameters ◽  
Accuracy Performance

<span>Nowadays data mining become one of the technologies that paly major effect on business intelligence. However, to be able to use the data mining outcome the user should go through many process such as classified data. Classification of data is processing data and organize them in specific categorize to be use in most effective and efficient use. In data mining one technique is not applicable to be applied to all the datasets. This paper showing the difference result of applying different techniques on the same data. This paper evaluates the performance of different classification techniques using different datasets. In this study four data classification techniques have chosen. They are as follow, BayesNet, NaiveBayes, Multilayer perceptron and J48. The selected data classification techniques performance tested under two parameters, the time taken to build the model of the dataset and the percentage of accuracy to classify the dataset in the correct classification. The experiments are carried out using Weka 3.8 software. The results in the paper demonstrate that the efficiency of Multilayer Perceptron classifier in overall the best accuracy performance to classify the instances, and NaiveBayes classifiers were the worst outcome of accuracy to classifying the instance for each dataset.</span>

scholarly journals DESIGN INFORMATION SYSTEM OF EMPLOYEES TARGET WITH NEURAL NETWORK BACKPROPAGATION

2018 ◽  
Vol 2 (1) ◽  
pp. 5
Author(s):  
Agus Sifaunajah ◽  
Kusworo Adi ◽  
Faikhin .
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Information System ◽  
Information Systems ◽  
Data Collection ◽  
Data Classification ◽  
Civil Servants ◽  
Web Based ◽  
Design Information

Assessment of the performance of civil servants (PNS) is still considered less objective and subjective tended to by some, so we need a solution to improve the objectivity of assessment. The target of employee work (SKP) is one solution to improve objectivity in the assessment of civil servants. Backpropagation is one of the methods in neural networks which is implemented in the information systems of SKP for used classification of data performance. Observation and literature became the method of data collection in this study. Web-based information systems of skp are facilitated for employees in the preparation of assessments. Backpropagation can be implemented to perform data classification of performance. Keyword: Neural network; Backpropagation, Classification, SKP Received: 2 February, 2017; Accepter: 15 March, 2017

COMBINING GENERALIZED NMF AND DISCRIMINATIVE MIXTURE MODELS FOR CLASSIFICATION OF GENE EXPRESSION DATA

2008 ◽  
Vol 22 (08) ◽  
pp. 1587-1598 ◽  
Author(s):  
WEIXIANG LIU ◽  
KEHONG YUAN ◽  
JIAN WU ◽  
DATIAN YE ◽  
ZHEN JI ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mixture Model ◽  
Gene Expression Data ◽  
Small Sample Size ◽  
Data Classification ◽  
Small Sample ◽  
Training Data ◽  
Microarray Data Analysis ◽  
Expression Data

Classification of gene expression samples is a core task in microarray data analysis. How to reduce thousands of genes and to select a suitable classifier are two key issues for gene expression data classification. This paper introduces a framework on combining both feature extraction and classifier simultaneously. Considering the non-negativity, high dimensionality and small sample size, we apply a discriminative mixture model which is designed for non-negative gene express data classification via non-negative matrix factorization (NMF) for dimension reduction. In order to enhance the sparseness of training data for fast learning of the mixture model, a generalized NMF is also adopted. Experimental results on several real gene expression datasets show that the classification accuracy, stability and decision quality can be significantly improved by using the generalized method, and the proposed method can give better performance than some previous reported results on the same datasets.

Satellite Parametric Description to Ontology Concepts and Semantic Classification of Satellite Data

2019 ◽  
pp. 1198-1222
Author(s):  
Sunitha Abburu ◽  
Nitant Dube
Keyword(s):  
Satellite Data ◽  
Phase 1 ◽  
Research Work ◽  
Data Classification ◽  
Data Retrieval ◽  
Satellite Observation ◽  
Two Phase ◽  
Storage And Retrieval ◽  
Kalpana Satellite

Current satellite data retrieval systems retrieves data using latitude, longitude, date, time and sensor parameters like wind, cloud etc. To achieve concept based satellite data retrieval like Storm, Hurricane, Overcast and Frost etc., requires ontological concept descriptions using satellite observation parameters and concept based classification of satellite data. The current research work has designed and implemented a two phase methodology to achieve this. The phase 1 defines ontology concepts through satellite observation parameters and phase 2 describes ontology concept based satellite data classification. The efficiency of the methodology is been tested by taking the Kalpana satellite data from MOSDAC and weather ontology. This achieves concept based retrieval of satellite data, application interoperability and strengthen the ontologies. The current methodology is implemented and results in concept based satellite data classification, storage and retrieval.

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