A Novel Approach of Reducing Energy Consumption by Utilizing Enthalpy in Mobile Cloud Computing

Mohammed, Mostafa Abdulghafoor; Țăpuș, Nicolae

doi:10.24846/v26i4y201706

Cited by 30 publications

(27 citation statements)

References 21 publications

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“…A classifier mainly aims at performing an accurate prediction of class values considering each instance in a set of data. The NBC [12,13] is a supervised classification technique which depends on the Bayes' Theorem to predict the class from the attributes of a dataset. Figure 2.…”

Section: Naïve Bayesmentioning

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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Section: Naïve Bayesmentioning

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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“…PBCH is broadcasted every 40 ms and follows the synchronization signals [9,25,26]. PBCH carries system information for all UEs that contains the bandwidth, system frame number (SFN), and hybrid ARQ indication channel (PHICH) length [27,28]. It is QPSK modulated and is transmitted on all antennas [11].…”

Section: Physical Signals and Channelsmentioning

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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“…There are three general subdomains of ML, these are supervised, unsupervised, and reinforcement learning. Regarding supervised learning [8,9], labeled data (with the inputs and desired outputs) are required during the training phase, but for unsupervised learning [10], there is no need for labeled training data as the inputs and desired outputs are provided by the environment. For reinforcement learning [11], it permits learning from the response received via communication with the outside environment.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Grid Optimization of SVM Hyperparameter for Big Data Classification using Spark Radoop

Ali¹,

Abdullah²

2020

Karbala International Journal of Modern Science

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The big data phenomenon is currently a challenge to the process of relevant knowledge extraction using classical machine learning technique. This is due to the need for efficient data reduction and new fastdistributed machine learning algorithms for such process on big data. The extensive application of SVM demands efficient methods of constructing the classifier to be suitable for big data and high classification capability. In reality, the efficiency of SVM relies on the efficient derivation of the optimal feature subset and the algorithmic parameters. The grid search optimization method usually presents global optima and high learning accuracy compared to PSO and GA, but its larger computation takes much time. The grid search is more attractive because it can simultaneously take part in the learning of every SVM since they do not rely on each other. A novel parallel implementation of grid optimization using Spark Radoop is proposed in this paper to minimize the great computation load and make it suitable for big data processing issues. A major contribution of this study is a significant reduction in the algorithmic computational time when compared to the serial version of gridSVM, as well as the high classification accuracy compared to the other parallel optimization techniques.

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A Novel Approach of Reducing Energy Consumption by Utilizing Enthalpy in Mobile Cloud Computing

Cited by 30 publications

References 21 publications

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

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

Analysis of LTE physical channels overhead

A Parallel Grid Optimization of SVM Hyperparameter for Big Data Classification using Spark Radoop

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