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

Mohammed, Haider A. A.; Nazeeh, Israa; Alisawi, Wisam Ch.; Kadhim, Qusay Kanaan; Ahmed, Shaymaa Taha

doi:10.18280/ria.370411

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…The alpha, beta, and delta wolves determine the prey's likely position during the iterative search process. The GWO takes into account the hunting, social hierarchy, and searching habits of grey wolves [21]. Because of its decreased unpredictability and different numbers of participants assigned to the global and local search processes, the GWO method can be easily applied and converges quickly.…”

Section: Grey Wolf Optimizer With Crossover (Cgwo)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 (Cgwo)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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“…The evaluate the performance of the suggested strategy, we used four metrics: Generally speaking, sensitivity, specificity, and accuracy [26]. These measurements written as the following equations:…”

Section: Evaluation Of Efficiencymentioning

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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Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges

Maseer,

Kadhim,

Al‐Bander

et al. 2024

IET Networks

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Intrusion detection systems built on artificial intelligence (AI) are presented as latent mechanisms for actively detecting fresh attacks over a complex network. The authors used a qualitative method for analysing and evaluating the performance of network intrusion detection system (NIDS) in a systematic way. However, their approach has limitations as it only identifies gaps by analysing and summarising data comparisons without considering quantitative measurements of NIDS's performance. The authors provide a detailed discussion of various deep learning (DL) methods and explain data intrusion networks based on an infrastructure of networks and attack types. The authors’ main contribution is a systematic review that utilises meta‐analysis to provide an in‐depth analysis of DL and traditional machine learning (ML) in notable recent works. The authors assess validation methodologies and clarify recent trends related to dataset intrusion, detected attacks, and classification tasks to improve traditional ML and DL in NIDS‐based publications. Finally, challenges and future developments are discussed to pose new risks and complexities for network security.

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

Cited by 3 publications

References 30 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

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

Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges

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