A comprehensive study: Ant Colony Optimization (ACO) for facility layout problem

Hasan, Raed Abdulkareem; Mohammed, Mostafa Abdulghafoor; Țăpuș, Nicolae; Hammood, Omar A.

doi:10.1109/roedunet.2017.8123738

Cited by 31 publications

(21 citation statements)

References 17 publications

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“…It involves the use of FS algorithms to filter out irrelevant and redundant data features from the original dataset to prevent over-fitting [6,13] and improve the classification accuracy of the model. Feature selection also reduces the classification models' complexity in time and space domains [14][15][16][17][18]. The main idea of this paper is to employ the TLBO-based algorithm for features subset selection in BC diagnosis.…”

Section: Telkomnika Telecommun Comput El Controlmentioning

confidence: 99%

A new model for large dataset dimensionality reduction based on teaching learning-based optimization and logistic regression

et al. 2020

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One of the human diseases with a high rate of mortality each year is breast cancer (BC). Among all the forms of cancer, BC is the commonest cause of death among women globally. Some of the effective ways of data classification are data mining and classification methods. These methods are particularly efficient in the medical field due to the presence of irrelevant and redundant attributes in medical datasets. Such redundant attributes are not needed to obtain an accurate estimation of disease diagnosis. Teaching learning-based optimization (TLBO) is a new metaheuristic that has been successfully applied to several intractable optimization problems in recent years. This paper presents the use of a multi-objective TLBO algorithm for the selection of feature subsets in automatic BC diagnosis. For the classification task in this work, the logistic regression (LR) method was deployed. From the results, the projected method produced better BC dataset classification accuracy (classified into malignant and benign). This result showed that the projected TLBO is an efficient features optimization technique for sustaining data-based decision-making systems.

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Section: Telkomnika Telecommun Comput El Controlmentioning

confidence: 99%

A new model for large dataset dimensionality reduction based on teaching learning-based optimization and logistic regression

et al. 2020

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“…Hence, a hybrid adaptive approach called Hoeffding Naive Bayes Tree (hnbt) which performs better than the component prediction methods for both complex and simple concepts has been proposed. This concept of this method based on executing a naive Bayes prediction on each training feature, then, comparing the prediction performance with the majority class [19][20][21][22][23][24][25]. The number of times the naïve Bayes makes a correct prediction of the true class is noted (by taking counts) compared to the majority class.…”

Section: Hoeffding Tree (Ht)mentioning

confidence: 99%

Efficient method for breast cancer classification based on ensemble hoffeding tree and naïve Bayes

Alhayali

Ahmed

Mohialden

et al. 2020

IJEECS

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<p><span>The most dangerous type of cancer suffered by women above 35 years of age is breast cancer. Breast Cancer datasets are normally characterized by missing data, high dimensionality, non-normal distribution, class imbalance, noisy, and inconsistency. Classification is a machine learning (ML) process which has a significant role in the prediction of outcomes, and one of the outstanding supervised classification methods in data mining is Naives Bayess Classification (NBC). Naïve Bayes Classifications is good at predicting outcomes and often outperforms other classifications techniques. Ones of the reasons behind this strong performance of NBC is the assumptions of conditional Independences among the initial parameters and the predictors. However, this assumption is not always true and can cause loss of accuracy. Hoeffding trees assume the suitability of using a small sample to select the optimal splitting attribute. This study proposes a new method for improving accuracy of classification of breast cancer datasets. The method proposes the use of Hoeffding trees for normal classification and naïve Bayes for reducing data dimensionality.</span></p>

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“…LTE supports various MCS which can be changed per 1 ms subframe based on wireless channel conditions and interference. The different types of supported modulation include 64QAM, 16QAM, and QPSK [8,21,22]. All user traffic and signaling traffic are organized in channels.…”

Section: Explaining Lte Layers and Protocolsmentioning

confidence: 99%

Analysis of LTE physical channels overhead

Al-Hilfi¹

2020

TELKOMNIKA

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LTE Network is the common mobile technology these days around the world and all service providers seek to how improve the network capacity and deliver the best performance in terms of delivered data rates and coverage area. LTE network consists of many protocols that work together to establish network connectivity, these protocols add variable headers that contains many control information that the network needs to operate. At the same time these headers decrease the effective capacity of the network, so there is a need to optimize the overhead size that used in various channels. The study will illustrate the different overheads that effect on the network capacity and investigate the effect of different values on achieving the best network capacity.

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A comprehensive study: Ant Colony Optimization (ACO) for facility layout problem

Cited by 31 publications

References 17 publications

A new model for large dataset dimensionality reduction based on teaching learning-based optimization and logistic regression

A new model for large dataset dimensionality reduction based on teaching learning-based optimization and logistic regression

Efficient method for breast cancer classification based on ensemble hoffeding tree and naïve Bayes

Analysis of LTE physical channels overhead

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