Claim Missing Document
Check
Articles

Found 2 Documents
Search

Feature selection of unbalanced breast cancer data using particle swarm optimization Amal Elnawasany; Mohamed Abd Allah Makhlouf; BenBella Tawfik; Hamed Nassar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4951-4959

Abstract

Breast cancer is one of the significant deaths causing diseases of women around the globe. Therefore, high accuracy in cancer prediction models is vital to improving patients’ treatment quality and survivability rate. In this work, we presented a new method namely improved balancing particle swarm optimization (IBPSO) algorithm to predict the stage of breast cancer using unbalanced surveillance epidemiology and end result (USEER) data. The work contributes in two directions. First, design and implement an improved particle swarm optimization (IPSO) algorithm to avoid the local minima while reducing USEER data’s dimensionality. The improvement comes primarily through employing the cross-over ability of the genetic algorithm as a fitness function while using the correlation-based function to guide the selection task to a minimal feature subset of USEER sufficiently to describe the universe. Second, develop an improved synthetic minority over-sampling technique (ISMOTE) that avoid overfitting problem while efficiently balance USEER. ISMOTE generates the new objects based on the average of the two objects with the smallest and largest distance from the centroid object of the minority class. The experiments and analysis show that the proposed IBPSO is feasible and effective, outperforms other state-of-the-art methods; in minimizing the features with an accuracy of 98.45%.
Remarks on a stochastic geometric model for interference-limited cellular communications Hamed Nassar; Gehad Mohamed Taher; El Sayed El Hady
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1376-1388

Abstract

A plethora of stochastic geometry (SG) models have been developed for cellular communications, especially in the context of internet of things (IoT) applica tions. A typical assumption in such models is that base stations (BS) are de ployed in the Euclidean plane as a spatial poisson point process (PPP) of some density λ, with each communicating equipment transmitting at some power p. The usual objective of these models is to characterize the cellular coverage prob ability in both the downlink (DL) and uplink (UL) directions. In this article we expose, in the form of four remarks, the peculiar behavior of a baseline stochas tic geometric model of an interference-limited cellular system. Specifically, we reveal that under some assumptions, the coverage probability in both the UL and DL directions for this system is independent of both λ and p, flagrantly contra dicting intuition. The aim of the article is by no means to invalidate the use of SG in modeling communications systems, but rather to point out that such modeling may not be adequate all the time.