Security and accountability for sharing the data stored in the cloud

Mohammed, Mostafa Abdulghafoor; Salih, Zeyad Hussein; Țăpuș, Nicolae; Hasan, Raed Abdul Kareem

doi:10.1109/roedunet.2016.7753201

Cited by 27 publications

(20 citation statements)

References 11 publications

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“…A recent metaheuristic, teaching-learning-based optimization (TLBO), has been reported to be an efficient optimization tool that is inspired by the knowledge passing mechanisms of teachers and learners in a classroom [19][20][21][22]. It has been applied to several well-known combinatorial optimization problems, producing good results [23][24][25][26]. The following sections discussed the novel multi-objective TLBO optimization algorithm for attaining better feature selection accuracy.…”

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

Self Cite

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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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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.

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“…The number of routing tables varies from protocol to protocol [20][21][22]. The example of this protocol is dynamic destination sequenced distance vector routing protocol (DSDV), cluster gateway switch routing protocol (CGSR), wireless routing protocol (WRP) [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of two models in the reactive routing protocol in manets

Abbas

Al-Hilfi²,

Sutikno

2021

IJEECS

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Due to the need for the multicast connection required by multimedia applications such as video conferencing and natural disaster management, it was necessary to use a set of portable and connected wireless peripheral devices without the need for infrastructure and without a central router, by use wireless network as "a mobile ad hoc network (MANET) " Despite the difficulties facing this technology in the process of effective guidance and the expansion of the use of such networks, in this type of network because of its applications in the fields of civil and military systems that do not need an infrastructure for the network to provide communication, the researchers specialized in this field have improved the current routing protocols and design new protocols that are appropriate to the challenges that Routing protocols face due to continuous change In network topology over time. In this paper we improved the current routing protocols and design new protocols that are appropriate to the challenges that Routing protocols face due to continuous change In network topology over time. The NS2 simulation environment was used for the purpose of a comprehensive evaluation of the performance of the TORA and AODV routing protocols By using properties of packet delivery fraction and end-to-end delay was calculated.ManetOdavPacket deliveryTora

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Security and accountability for sharing the data stored in the cloud

Cited by 27 publications

References 11 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

Performance evaluation of two models in the reactive routing protocol in manets

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