A Review on Data mining from Past to the Future

Venkatadri, M.; Reddy, Lokanatha C.

doi:10.5120/1961-2623

Cited by 20 publications

(2 citation statements)

References 10 publications

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“…Study and analysis of consumers electricity consumption data are helpful for detecting consumption pattern at various levels, such as individual consumers, zone and at regional level in an electricity distribution network. To analyze huge data, data preprocessing operations are carried out in first step before applying data mining algorithm [11,12]. Data preprocessing includes preparation of data in desired form to work, which is clean and free from any noise.…”

Section: Data Selection Cleaning and Pre-processingmentioning

confidence: 99%

Survey on Electricity Consumption using Data Mining Techniques

Kalaiselvi¹,

Samath²

2019

IJANA

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Electric energy consumption is the extent of energy or power used. This is the actual energy stipulate made on existing electricity supply. The biggest reason why it is important to be energy conscious and why to make every effort to conserve our electricity are conservation can save our money and Fossil fuels are not a clean source of energy either. Conservation of electrical energy can help to reduce greenhouse gas emissions. Data mining is one of the effective methods to analyze the electricity consumption. Data mining is the process of discovering useful patterns from the database. Association rule and K-means clustering has widely used data mining technique to analyze the electricity consumption.

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Section: Data Selection Cleaning and Pre-processingmentioning

confidence: 99%

Survey on Electricity Consumption using Data Mining Techniques

Kalaiselvi¹,

Samath²

2019

IJANA

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“…Although untargeted metabolomics is capable of revealing the slighter alterations in metabolite profile of a specific disease, it provides a considerable amount of metabolic information, which depends on robust methods for analysis and classification of features (12). Data mining techniques, which include machine learning, statistical analysis, and database management systems, can extract implicit patterns from massive biological data sets and, in the last decade, have been applied to identify characteristic molecular features for each condition (13,14).…”

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confidence: 99%

Metabolomics and Machine Learning Approaches Combined in Pursuit for More Accurate Paracoccidioidomycosis Diagnoses

et al. 2020

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Brazil and many other Latin American countries are areas of endemicity for different neglected diseases, and the fungal infection paracoccidioidomycosis (PCM) is one of them. Among the clinical manifestations, pneumopathy associated with skin and mucosal lesions is the most frequent. PCM definitive diagnosis depends on yeast microscopic visualization and immunological tests, but both present ambiguous results and difficulty in differentiating PCM from other fungal infections. This research has employed metabolomics analysis through high-resolution mass spectrometry to identify PCM biomarkers in serum samples in order to improve diagnosis for this debilitating disease. To upgrade the biomarker selection, machine learning approaches, using Random Forest classifiers, were combined with metabolomics data analysis. The proposed combination of these two analytical methods resulted in the identification of a set of 19 PCM biomarkers that show accuracy of 97.1%, specificity of 100%, and sensitivity of 94.1%. The obtained results are promising and present great potential to improve PCM definitive diagnosis and adequate pharmacological treatment, reducing the incidence of PCM sequelae and resulting in a better quality of life. IMPORTANCE Paracoccidioidomycosis (PCM) is a fungal infection typically found in Latin American countries, especially in Brazil. The identification of this disease is based on techniques that may fail sometimes. Intending to improve PCM detection in patient samples, this study used the combination of two of the newest technologies, artificial intelligence and metabolomics. This combination allowed PCM detection, independently of disease form, through identification of a set of molecules present in patients’ blood. The great difference in this research was the ability to detect disease with better confidence than the routine methods employed today. Another important point is that among the molecules, it was possible to identify some indicators of contamination and other infection that might worsen patients’ condition. Thus, the present work shows a great potential to improve PCM diagnosis and even disease management, considering the possibility to identify concomitant harmful factors.

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A Comprehensive Hybrid Ensemble Method with Feature Selection Techniques

Sujatha

Rani

2020

Learning and Analytics in Intelligent Systems

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A Review on Data mining from Past to the Future

Cited by 20 publications

References 10 publications

Survey on Electricity Consumption using Data Mining Techniques

Survey on Electricity Consumption using Data Mining Techniques

Metabolomics and Machine Learning Approaches Combined in Pursuit for More Accurate Paracoccidioidomycosis Diagnoses

A Comprehensive Hybrid Ensemble Method with Feature Selection Techniques

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