A Hybrid Genetic Algorithm — Decision Tree Classifier

Salem, Abdel-Badeeh M.; Mahmoud, Abeer M.

doi:10.1007/978-3-540-36562-4_23

Cited by 9 publications

(3 citation statements)

References 3 publications

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“…The optimal performance based on F1-scores, Precision, Recall and accuracy are reported for (CNN and DNN) compared to the other traditional techniques. The DNN-based model; on the three datasets; recorded highest results of average of 88% while Decision Tree 38 recorded the lowest results of 67%. This nominates the proposed DNN model for similar mining tasks and supports its efficiency and performance.…”

Section: Experimental and Discussionmentioning

confidence: 93%

“…Results in this section are reported using 10-fold cross-validation, which are carried out by partitioning the owner dataset (Genera_Data) into 10 folds where 9 are used for training and one for testing, then this procedure is repeated 10 times averaging results. In the following a comparative analysis of two implemented deep learning models; (1) Basic CNN holds identical configuration in 34 and (2) our proposed DNN architecture; with traditional techniques (Decision tree 35 , 36 , SVM 17 , Logistic Regression 37 and Naïve Bayes 38 ) is presented. Also each approach is analyzed from variant permutation of features, we name as models (M(1) to M(2)) to check optimal findings.…”

Section: Experimental and Discussionmentioning

confidence: 99%

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A pre-protective objective in mining females social contents for identification of early signs of depression using soft computing deep framework

Karamti,

Mahmoud

2023

Sci Rep

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Currently, a noteworthy volume of information is available and shared every day through participation and communication of individuals on social media. These enormous contents with the right exploit and research leads to valuable discoveries. In this study, a deep framework of learning accurate detection of women’s depression is proposed. It is beneficially guided by social media content of individual posts and tweets and an essential support from psycho-linguistic for providing the indicator depression signs vocabulary that creates the embedding words necessary for building the applied approach. The presented model is validated using dual datasets extracted from Twitter: the first dataset is general data formed by 700 women from different countries; the second contains only 80 women from KSA. A third benchmark dataset CLPsych 2015 is used for comparative analysis purposes. The model proved its performance on the three datasets and the obtained and reported in this paper results shows its effectiveness.

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Section: Experimental and Discussionmentioning

confidence: 93%

Section: Experimental and Discussionmentioning

confidence: 99%

A pre-protective objective in mining females social contents for identification of early signs of depression using soft computing deep framework

Karamti,

Mahmoud

2023

Sci Rep

Self Cite

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“…In the semi-supervised hashing (SSH) category, several methods are proposed that not only utilize images with a few labels, but also utilize images with a set of labels. These methods generate hash codes and minimize the empirical errors between pairwise data in order to avoid over-fitting [22]. We can refer to the bootstrap sequential projection learning (BSPLH) technique as an example of SSH methods [23].…”

Section: Related Workmentioning

confidence: 99%

A deep locality-sensitive hashing approach for achieving optimal ‎image retrieval satisfaction

Karamti

Shaiba

Mahmoud

2022

IJECE

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<span>Efficient methods that enable high and rapid image retrieval are continuously needed, especially with the large mass of images that are generated from different sectors and domains like business, communication media, and entertainment. Recently, deep neural networks are extensively proved higher-performing models compared to other traditional models. Besides, combining hashing methods with a deep learning architecture improves the image retrieval time and accuracy. In this paper, we propose a novel image retrieval method that employs locality-sensitive hashing with convolutional neural networks (CNN) to extract different types of features from different model layers. The aim of this hybrid framework is focusing on both the high-level information that provides semantic content and the low-level information that provides visual content of the images. Hash tables are constructed from the extracted features and trained to achieve fast image retrieval. To verify the effectiveness of the proposed framework, a variety of experiments and computational performance analysis are carried out on the CIFRA-10 and NUS-WIDE datasets. The experimental results show that the proposed method surpasses most existing hash-based image retrieval methods.</span>

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Computational Intelligence for Digital Healthcare Informatics

Abdel-Badeeh M. Salem

2023

Studies in Computational Intelligence

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A Hybrid Genetic Algorithm — Decision Tree Classifier

Cited by 9 publications

References 3 publications

A pre-protective objective in mining females social contents for identification of early signs of depression using soft computing deep framework

A pre-protective objective in mining females social contents for identification of early signs of depression using soft computing deep framework

A deep locality-sensitive hashing approach for achieving optimal ‎image retrieval satisfaction

Computational Intelligence for Digital Healthcare Informatics

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