Mostrando ítems 1-4 de 4

An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection

Chen, Zhiqing; Xuan, Ping; Asghar Heidari, Ali; Liu, Lei; Wu, Chengwen; Chen, Huiling; Escorcia-Gutierrez, José; Mansour, Romany F. (Elsevier Inc.United States, 2023)

The domains of contemporary medicine and biology have generated substantial high-dimensional genetic data. Identifying representative genes and decreasing the dimensionality of the data can be challenging. The goal of gene ...
Indicators for smart cities: tax illicit analysis through data mining

Silva, Jesús; Solano, Darwin; Fernández, Claudia; Nieto Ramos, Lainet; Urdanegui, Rosella; Herz, Jeannette; Mercado, Alberto; Ovallos-Gazabon, David (Corporación Universidad de la Costa, 2021)

The anomalies in the data coexist in the databases and in the non-traditional data that can be accessed and produced by a tax administration, whether these data are of internal or external origin. The analysis of certain ...
A machine learning solution for bed occupancy issue for smart healthcare sector

Gochhait, Dr Saikat; De-La-Hoz-Franco, Emiro; Shaheen, Qaisar; Diaz Martinez, Jorge Luis; Piñeres Espitia, Gabriel Dario; MERCADO POLO, DARWIN (Pleiades PublishingUnited States, 2021)

The health care domain is a culmination and emergence of many other economic sectors that give different services from patient treatment to healing, protective, rehabilitation, and palliative care. The GDP consumes to ...
The scheduling algorithms for two-stage grid models

amelec, viloria; Pineda Lezama, Omar Bonerge; Martínez, Karol; Mercado Caruso, Nohora (Corporación Universidad de la Costa, 2021)

This paper deals with the scheduling of parallel works in a two-stage hierarchical grid. In this configuration, one of the great challenges is to assign the tasks in order to allow an efficient use of resources, while ...

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An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection
...
Chen, Zhiqing | 2023

The domains of contemporary medicine and biology have generated substantial high-dimensional genetic data. Identifying representative genes and decreasing the dimensionality of the data can be challenging. The goal of gene selection is to minimize computing costs and enhance classification precision. Therefore, this article designs a new wrapper gene selection algorithm named artificial bee bare-bone hunger games search (ABHGS), which is the hunger games search (HGS) integrated with an artificial bee strategy and a Gaussian bare-bone structure to address this issue. To evaluate and validate the performance of our proposed method, ABHGS is compared to HGS and a single strategy embedded in HGS, six classic algorithms, and ten advanced algorithms on the CEC 2017 functions. The experimental results demonstrate that the bABHGS outperforms the original HGS. Compared to peers, it increases classification accuracy and decreases the number of selected features, indicating its actual engineering utility in spatial search and feature selection.

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Indicators for smart cities: tax illicit analysis through data mining
...
Silva, Jesús | 2021

The anomalies in the data coexist in the databases and in the non-traditional data that can be accessed and produced by a tax administration, whether these data are of internal or external origin. The analysis of certain anomalies in the data could lead to the discovery of patterns that respond to different causes, being able to evidence these causes certain illicit by taxpayers or acts of corruption when there is the connivance of the taxpayer with the public employee or public official. The purpose of this research is the theoretical development of the causal analysis of certain anomalies of tax data, demonstrating that the data mining methodology contributes to evidence of illicit and corrupt acts, through the application of certain algorithms.

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A machine learning solution for bed occupancy issue for smart healthcare sector
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Gochhait, Dr Saikat | 2021

The health care domain is a culmination and emergence of many other economic sectors that give different services from patient treatment to healing, protective, rehabilitation, and palliative care. The GDP consumes to facilitate health in terms of smart device development, clinical examinations, outsourcing, and tele-medication facilities. The Asian countries and less developed countries with a high population rate are facing health care services related issues. One of these countries is India. India has two types of health care services systems: (i) public service system and (ii) private system. The public health system, i.e., the government, provides facilities to patients as primary health centers (PHCs) through limited secondary and tertiary health institutions like hospitals in rural areas while the private service is owned by local practitioners and institutions. Both of these service providers are facing bed occupancy issues for patients due to a highly populated country. To overcome this issue, we propose a machine learning solution for patient admission scheduling autonomously. The proposed framework helps hospitals to enhance the decision process for bed occupancy for patients concerning their departments and their diseases. We have deployed our framework in real time environment and find that it facilitates the overall performance of bed allocation in the prescribed hospitals.

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The scheduling algorithms for two-stage grid models
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amelec, viloria | 2021

This paper deals with the scheduling of parallel works in a two-stage hierarchical grid. In this configuration, one of the great challenges is to assign the tasks in order to allow an efficient use of resources, while satisfying other criteria. In general, the optimization criteria are often in conflict. For solving this problem, a bi-objective genetic algorithm is proposed presenting an experimental study of six cross operators, and three mutation operators. The most influential parameters are determined through a statistical analysis of multifactorial variance which compares the proposal with five allocation strategies found in the literature.

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Listar por tema "Algorithms"

An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection ﻿

Indicators for smart cities: tax illicit analysis through data mining ﻿

A machine learning solution for bed occupancy issue for smart healthcare sector ﻿

The scheduling algorithms for two-stage grid models ﻿

Envíos recientes

An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection

Indicators for smart cities: tax illicit analysis through data mining

A machine learning solution for bed occupancy issue for smart healthcare sector

The scheduling algorithms for two-stage grid models

An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection

Indicators for smart cities: tax illicit analysis through data mining

A machine learning solution for bed occupancy issue for smart healthcare sector

The scheduling algorithms for two-stage grid models