A data mining based load forecasting strategy for smart electrical grids

Saleh, Ahmed I.; Rabie, Asmaa H.; Abo-Al-Ez, Khaled M.

doi:10.1016/j.aei.2016.05.005

Cited by 81 publications

(64 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Secondly, GA iterations will be performed until a termination condition is satisfied. At the end, the best chromosome provide the best subset of features that should be evaluated by using classifier such as Naïve Bayes (NB) as a standard classifier [14] . Generally, GA is an evolutionary algorithm that performs a global search to optimally solve the problem depending on its fitness value [16] .…”

Section: The Proposed Covid-19 Patient Detection Strategymentioning

confidence: 99%

See 1 more Smart Citation

A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier

Shaban¹,

Rabie

Saleh

et al. 2020

Knowledge-Based Systems

Self Cite

192

116

View full text Add to dashboard Cite

COVID-19 infection is growing in a rapid rate. Due to unavailability of specific drugs, early detection of (COVID-19) patients is essential for disease cure and control. There is a vital need to detect the disease at early stage and instantly quarantine the infected people. Many research have been going on, however, none of them introduces satisfactory results yet. In spite of its simplicity, K-Nearest Neighbor (KNN) classifier has proven high flexibility in complex classification problems. However, it can be easily trapped. In this paper, a new COVID-19 diagnose strategy is introduced, which is called COVID-19 Patients Detection Strategy (CPDS). The novelty of CPDS is concentrated in two contributions. The first is a new hybrid feature selection Methodology (HFSM), which elects the most informative features from those extracted from chest Computed Tomography (CT) images for COVID-19 patients and non COVID-19 peoples. HFSM is a hybrid methodology as it combines evidence from both wrapper and filter feature selection methods. It consists of two stages, namely; Fast Selection Stage (FS 2 ) and Accurate Selection Stage (AS 2 ). FS 2 relies on filter, while AS 2 uses Genetic Algorithm (GA) as a wrapper method. As a hybrid methodology, HFSM elects the significant features for the next detection phase. The second contribution is an enhanced K-Nearest Neighbor (EKNN) classifier, which avoids the trapping problem of the traditional KNN by adding solid heuristics in choosing the neighbors of the tested item. EKNN depends on measuring the degree of both closeness and strength of each neighbor of the tested item, then elects only the qualified neighbors for classification. Accordingly, EKNN can accurately detect infected patients with the minimum time penalty based on those significant features selected by HFSM technique. Extensive experiments have been done considering the proposed detection strategy as well as recent competitive techniques on the chest CT images. Experimental results have shown that the proposed detection strategy outperforms recent techniques as it introduces the maximum accuracy rate.

show abstract

Section: The Proposed Covid-19 Patient Detection Strategymentioning

confidence: 99%

“…Selecting the meaningful features enables the classification method to accurately classify COVID-19 patients with the minimum time penalty. There are numerous feature selection approaches grouped to three basic classes, which are; filter, wrapper, and hybrid approach [13] , [14] , [15] .…”

Section: Introductionmentioning

confidence: 99%

A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier

Shaban¹,

Rabie

Saleh

et al. 2020

Knowledge-Based Systems

Self Cite

192

116

View full text Add to dashboard Cite

show abstract

“…Saleh et al 26 puts forward a load forecasting strategy by employing data mining techniques. During the preprocessing stage, outlier rejection and feature selection are employed to give a meaningful pattern to the data.…”

Section: Load Forecastingmentioning

confidence: 99%

A review on the features and technologies for energy efficiency of smart grid

Anjana¹,

Shaji

2017

Int J Energy Res

View full text Add to dashboard Cite

Summary In today's world striving for efficiency in every sector, especially power generation and distribution, smart grids emerge as the solution for efficiently meeting the increasing demand. They adjust themselves to optimally deliver energy at the lowest cost and highest quality possible. The grid successfully makes use of renewable energy resources, electric vehicles, and smart pricing techniques in its attempt to achieve energy efficiency. It also promotes a greener environment by striving to reduce greenhouse gas emissions. Information communication technology (ICT) helps the grid in collecting consumption data from the consumers and in sharing tariff information. ICT also helps to gather information about the status of the grid with regard to aspects like power quality, faults etc. The purpose of this paper is to review recent literature with a view to comprehensively present the technologies employed in the smart grid for achieving energy efficiency and the challenges involved therein.

show abstract

“…An experiment has been handled on human acute leukemia's for cancer classification with the help of gene expression microarrays to finding the class discovery acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) for predicting and finding the classes [8]. Using the three feature selection methods filter, wrapper and embedded are used in classifying the predictive accuracy with the help of the significant features [9].By using support vector machine, the tissue samples of cancer diseases are validated [10].RFE-SVM is used for choosing the suited features and later the given datasets are changed to two subsets for further classification. And then the result validation is carried out to get the goodness of the features.…”

Section: Introductionmentioning

confidence: 99%

Gene Selection using a Hybrid RFE along with LASSO for Cancer Classification

Abinash¹,

Vasudevan²

2019

IJEAT

View full text Add to dashboard Cite

Gene expression profiling using microarray technology has done with the chip based phenomena. For studying gene expression data are more helpful in knowing various diseases and more useful in finding diseases. Recently in the bioinformatics field, cancer prediction using gene expression data had made the assuring area. Samples having the gene attributes will not surely give the efficient amount of classification. Overcoming these contribution, a strong method is required for selecting the relevant gene features for building the classification model effectively. Basically least absolute shrinkage and selection operator (LASSO) and Recursive feature elimination (RFE) are automatic gene feature selection methods used for classification. Here in our proposed work, we use these two methods as a hybrid one for selecting the features and later it applied into the Support vector machine (SVM) for easy classification. It made best when compared to the existing techniques by their performance measures, were regulated on six publically available cancer datasets. Just out it gives the good awareness in the selection of features.

show abstract

A data mining based load forecasting strategy for smart electrical grids

Cited by 81 publications

References 39 publications

A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier

A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier

A review on the features and technologies for energy efficiency of smart grid

Gene Selection using a Hybrid RFE along with LASSO for Cancer Classification

Contact Info

Product

Resources

About