Using support vector machine regression to reduce cloud security risks in developing countries

Dhahi, Sanaa Hammad; Dhahi, Estqlal Hammad; Khadhim, Ban Jawad; Ahmed, Shaymaa Taha

doi:10.11591/ijeecs.v30.i2.pp1159-1166

Cited by 3 publications

(3 citation statements)

References 22 publications

(25 reference statements)

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“…An enhancement on GWO's search functionality has been created. A crossover operator decreases the chances of the algorithm of falling in the local optima, because it discovers more regions far from the current best solution once there no new improvements [29].…”

Section: Grey Wolf Optimizer With Crossover For Support Vector Machin...mentioning

confidence: 99%

Predicting Alzheimer's Disease Using a Modified Grey Wolf Optimizer and Support Vector Machine

Mohammed,

Jizany,

Mahmood

et al. 2024

ISI

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Alzheimer's Disease (AD), which is left untreated, is the primary cause of neurodegenerative dementia, which primarily affects those over 65. Memory and thought processes are gradually disrupted by the irreversible brain disruption and, eventually, the capacity to fulfil simple tasks. Detecting AD early prevents its progression and diminishes its signs. The study aimed to build a classification model that might predict the early stages of Alzheimer's disease using accurate early-stage gene expression data from blood obtained from a clinical Alzheimer's dataset. The datasets used in this work were gathered from the Gene Expression Omnibus (GEO) and are GSE63060 and GSE63061.It has the right rows for (569 samples) and columns for (16382 genes). The suggested GWO gene selection method aims to identify the ideal feature subset for medical data. A model for predicting early Alzheimer's disease is proposed based on the modified Grey Wolf Optimizer and Support Vector Machine (GWO-SVM). These methods will help reduce the number of trivial and redundant genes in the original datasets. We attained an accuracy of 82%-88% using only the Support Vector Machine method. When we utilized several evolutionary algorithms to implement gene selection, we observed an increase in accuracy of 6%-11%, with modified Grey Wolf optimization with Crossover (CGWO) called the proposed algorithm (CGWO-SVM) achieving the highest accuracy of (97.41%) with the Alzheimer's disease dataset in comparison to the other competitive schemes in the existing literature. Thus, the proposed system is suitable for picking more accurate classification and interesting genes to increase classification accuracy while decreasing gene dimensions.

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Section: Grey Wolf Optimizer With Crossover For Support Vector Machin...mentioning

confidence: 99%

Predicting Alzheimer's Disease Using a Modified Grey Wolf Optimizer and Support Vector Machine

Mohammed,

Jizany,

Mahmood

et al. 2024

ISI

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“…A deep neural network (DNN) [9] was utilized as deep learning to increase the accuracy level of cancer identification from three datasets, including STAD (Stomach adenocarcinoma), LUAD (lung adenocarcinoma), and BRCA. The grey wolf technique was employed to extract significant characteristics in the pre-processing step (breast invasive carcinoma).…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection in Human Disease: A Hybrid Approach Using GWO-SVM for Gene Selection

Mohammed,

Nazeeh,

Alisawi

et al. 2023

RIA

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The goal is to facilitate early disease detection. A Grey Wolf Optimizer (GWO) was implemented in the proposed method, a meta-heuristic algorithm known for its efficiency in reducing computational time for high-dimensional data. This optimization technique simplifies the problem by breaking it into manageable subsets. Following this, a filter approach, such as analysis of variance (ANOVA), was used to select informative genes from the reduced data. A Support Vector Machine (SVM) was also used as a classifier to select genes that efficiently categorize anomalous cases, serving as a fitness function-this combined approach, referred to as GWO-SVM, and aimed to reduce computational time while improving accuracy. The experimental results demonstrated that the proposed method achieved an accuracy rate of 96.46% in predicting disease detection, representing a significant improvement compared to previous methods. These findings underscore the potential of the GWO-SVM approach in advancing anomaly detection in human diseases.

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“…After feature maps are extracted and dimensions are reduced by selecting the best features between them, low dimensional feature vector fed into a classifier. The classifier returns the likelihood of class that the input may belong to it [22]. The classifier includes one or more completely connected layers to do that purpose [23].…”

Section: Classifiermentioning

confidence: 99%

Early Detection and Segmentation of Ovarian Tumor Using Convolutional Neural Network with Ultrasound Imaging

Alwan,

Kadhim,

Issa

et al. 2023

RIA

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The clinical differentiation between benign and malignant ovarian tumors represents a substantial challenge in the fields of obstetrics and gynecology, particularly following the detection of ovarian cysts through ultrasound. Ovarian cancers, diverse in type, often exhibit overlapping characteristics that complicate diagnosis. In this study, a deep learningbased methodology was developed to aid in the rapid and accurate differentiation of ovarian cancer types using ultrasound imaging. A deep learning approach, utilizing transfer learning with Convolutional Neural Network (CNN) models, was employed. To ensure the stability and robustness of the solution, ten iterations of training and validation were executed, with data randomly sampled for each iteration. The mean of the ten iterations' outcomes constituted the final evaluation metric. Initially, ultrasound images were enhanced to augment the quality of the training dataset, followed by the extraction of low-level texture features for the segmentation of images. Subsequently, ten established CNN models were utilized for both training and transfer learning processes. In the culmination of the study, a multitask model was proposed, capable of concurrently executing detection and segmentation tasks. The conducted evaluations reveal that the deep learning models can classify ovarian tumors with an accuracy of 98.79%, a rate comparable to that of skilled medical practitioners.

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Using support vector machine regression to reduce cloud security risks in developing countries

Cited by 3 publications

References 22 publications

Predicting Alzheimer's Disease Using a Modified Grey Wolf Optimizer and Support Vector Machine

Predicting Alzheimer's Disease Using a Modified Grey Wolf Optimizer and Support Vector Machine

Anomaly Detection in Human Disease: A Hybrid Approach Using GWO-SVM for Gene Selection

Early Detection and Segmentation of Ovarian Tumor Using Convolutional Neural Network with Ultrasound Imaging

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