Study the Effect of Integrating the Solar Energy Source on Stability of Electrical Distribution System

Salih, Ziyad Hussien; Hasan, Ghanim Thiab; Mohammed, Mostafa Abdulghafoor; Klib, Marwan Ali Shnan; Ali, Ahmed H.; Ibrahim, Ruwaida A.

doi:10.1109/cscs.2019.00081

Cited by 24 publications

(23 citation statements)

References 9 publications

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“…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. When predicting a test case, the leaf can only output a naïve Bayes prediction when its overall accuracy is more than the majority class, else, it will output a majority class prediction [26][27][28].…”

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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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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“…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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“…Haar wavelet transform is used to extract the local features in the signature image. One of the important aspect of haar wavelet transform is to find corners and contours [18][19][20][21]. The reason for choosing the haar wavelet in this work is due to its being required low computational cost, fast and each sub bands (detail) contains a special edges type [22][23][24][25][26].…”

Section: Haar Wavelet Transformmentioning

confidence: 99%

Offline signatures matching using haar wavelet subbands

2020

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The complexity of multimedia contents is significantly increasing in the current world. This leads to an exigent demand for developing highly effective systems to satisfy human needs. Until today, handwritten signature considered an important means that is used in banks and businesses to evidence identity, so there are many works tried to develop a method for recognition purpose. This paper introduced an efficient technique for offline signature recognition depending on extracting the local feature by utilizing the haar wavelet subbands and energy. Three different sets of features are utilized by partitioning the signature image into non overlapping blocks where different block sizes are used. CEDAR signature database is used as a dataset for testing purpose. The results achieved by this technique indicate a high performance in signature recognition.

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Study the Effect of Integrating the Solar Energy Source on Stability of Electrical Distribution System

Cited by 24 publications

References 9 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

Offline signatures matching using haar wavelet subbands

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