Intelligent Feature Selection Methods: A Survey

Jameel, Noor Ghazi M.; Abdullah, Hasanen S.

doi:10.30684/etj.v39i1b.1623

Cited by 11 publications

(9 citation statements)

References 11 publications

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“…Leaders go from the meetings to update their locations. The update happens by including variance between the most powerful leader's place and that of the ordinary leader as shown in (12). ∆Pos=Ψ (…”

Section: Main Stepsmentioning

confidence: 99%

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Optimizing Alzheimer's disease prediction using the nomadic people algorithm

Ahmed

Kadhem

2023

IJECE

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<span lang="EN-US">The problem with using microarray technology to detect diseases is that not each is analytically necessary. The presence of non-essential gene data adds a computing load to the detection method. Therefore, the purpose of this study is to reduce the high-dimensional data size by determining the most critical genes involved in Alzheimer's disease progression. A study also aims to predict patients with a subset of genes that cause Alzheimer's disease. This paper uses feature selection techniques like information gain (IG) and a novel metaheuristic optimization technique based on a swarm’s algorithm derived from nomadic people’s behavior (NPO). This suggested method matches the structure of these individuals' lives movements and the search for new food sources. The method is mostly based on a multi-swarm method; there are several clans, each seeking the best foraging opportunities. Prediction is carried out after selecting the informative genes of the support vector machine (SVM), frequently used in a variety of prediction tasks. The accuracy of the prediction was used to evaluate the suggested system's performance. Its results indicate that the NPO algorithm with the SVM model returns high accuracy based on the gene subset from IG and NPO methods.</span>

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Section: Main Stepsmentioning

confidence: 99%

“…Otherwise, the ordinary leaders appraise the places through (12). It is an equation the representation from a part of survey steps in NPO.…”

Section: Main Stepsmentioning

confidence: 99%

Optimizing Alzheimer's disease prediction using the nomadic people algorithm

Ahmed

Kadhem

2023

IJECE

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“…When applied to a single signal, PCA fails to extract features, and when applied to a signal with varying frequencies, PCA fails to give information about the features existing in the signal. For feature extraction, SVD can be more useful than principal component analysis due to the fact that frequency differences may mask genuine differences across physiological states [29][30][31].…”

Section: Features Extractionmentioning

confidence: 99%

A Novel CNN Model with Dimensionality Reduction for WSN Intrusion Detection

Alhasan,

Hamza

2023

RIA

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Wireless sensor networks (WSNs) play a critical role in cyber-physical systems, enabling communication between autonomous sensors. When integrated with the Internet of Things (IoT), WSNs unfortunately become vulnerable to various attacks, such as blackhole, grayhole, flooding, and scheduling, thereby posing significant security threats. Existing methods for intrusion detection in WSNs often suffer from low detection rates, significant computational overhead, and false alarms, primarily due to resource constraints and data correlations. This study introduces IDS-CNN, a novel intrusion detection method leveraging Convolutional Neural Networks (CNNs). The proposed IDS-CNN model, designed to optimize efficiency and reduce processing time, comprises nine convolutional neural network layers and six Max-Pooling1D layers. To alleviate computational demands, dimensionality reduction techniques, specifically Principal Component Analysis and Singular Value Decomposition, are applied to raw traffic data. The IDS-CNN model is then employed to classify and categorize network threats. Experimental evaluations suggest that the IDS-CNN approach yields a high accuracy rate of 99% compared to existing methods, based on tests performed on two datasets, WSN-DS and UNSW-NB15. Notably, with the UNSW-NB15 dataset, accuracy rates were further improved to 99.99% and 100%. By leveraging deep learning techniques to enhance intrusion detection in WSNs, this study presents a significant contribution to the field. The IDS-CNN model advances our understanding of WSN security by exceeding the accuracy rates of prior models. As it addresses the limitations of existing methods, the implications of this research are substantial, offering a more reliable and efficient solution for WSN intrusion detection. The findings underscore the potential of IDS-CNN in safeguarding WSNs and IoT systems from sophisticated and evolving cyber threats.

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“…Microarray technology is a valuable medical device that biologists use to track gene expression levels in different organisms. Through a variety of practical situations, microarray data analysis aims to assist in accurate medical diagnosis across several genes and determining the best treatment for a number of ailments [2]. The data of gene expression obtained by microarray technology, on the other hand, suffers from a high dimensionality curse.…”

Section: Introductionmentioning

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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Intelligent Feature Selection Methods: A Survey

Cited by 11 publications

References 11 publications

Optimizing Alzheimer's disease prediction using the nomadic people algorithm

Optimizing Alzheimer's disease prediction using the nomadic people algorithm

A Novel CNN Model with Dimensionality Reduction for WSN Intrusion Detection

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

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