Machine Learning Techniques for Anomalies Detection and Classification

Abdel-Aziz, Amira; Hassanien, Aboul Ella; Azar, Ahmad Taher; Hanafi, Sanaa El-Ola

doi:10.1007/978-3-642-40597-6_19

Cited by 22 publications

(3 citation statements)

References 8 publications

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“…CNNs leverage the concept of local receptive fields, where small filters scan the input image to capture local patterns and structures. (Aziz et al, 2013b;Banu et al, 2017;Ding et al, 2015;Elshazly et al, 2013c;Kumar et al, 2015b;Sayed et al, 2020;Samanta et al, 2018). As the information propagates through the network, deeper layers extract increasingly abstract and task-specific features.…”

Section: Feature Extraction Using Convolutional Neural Networkmentioning

confidence: 99%

Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images

Nivetha,

Hannah Inbarani

2023

International Journal of Sociotechnology and Knowledge Development

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The difficulty in predicting early cancer is due to the lack of early illness indicators. Metaheuristic approaches are a family of algorithms that seek to find the optimal values for uncertain problems with several implications in optimization and classification problems. An automated system for recognizing illnesses can respond with accuracy, efficiency, and speed, helping medical professionals spot abnormalities and lowering death rates. This study proposes the Novel Hybrid GAO (Genetic Arithmetic Optimization algorithm based Feature Selection) (Genetic Arithmetic Optimization Algorithm-based feature selection) method as a way to choose the features for several machine learning algorithms to classify readily available data on COVID-19 and lung cancer. By choosing just important features, feature selection approaches might improve performance. The proposed approach employs a Genetic and Arithmetic Optimization to enhance the outcomes in an optimization approach.

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Section: Feature Extraction Using Convolutional Neural Networkmentioning

confidence: 99%

Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images

Nivetha,

Hannah Inbarani

2023

International Journal of Sociotechnology and Knowledge Development

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“…They showed that in general J48 gave better results than other classifiers, while for certain attacks Naïve Bayes gave the best results. Subsequently in (Abdel-Aziz et al, 2013b), Aziz et al proposed an anomaly detector generation approach using genetic algorithm in combination with several feature selection techniques, including principle component analysis, sequential floating, and correlation-based feature selection. To generate a set of detectors from a single run, a genetic algorithm was applied with deterministic crowding niching techniques.…”

Section: Related Work and Our Contributionmentioning

confidence: 99%

Learning Algorithms for Anomaly Detection from Images

Ahmed

Pathan

Ahmed

2015

International Journal of System Dynamics Applications

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Visual surveillance networks are installed in many sensitive places in the present world. Human security officers are required to continuously stare at large numbers of monitors simultaneously, and for lengths of time at a stretch. Constant alert vigilance for hours on end is difficult to maintain for human beings. It is thus important to remove the onus of detecting unwanted activity from the human security officer to an automated system. While many researchers have proposed solutions to this problem in the recent past, significant gaps remain in existing knowledge. Most existing algorithms involve high complexities. No quantitative performance analysis is provided by most researchers. Most commercial systems require expensive equipment. This work proposes algorithms where the complexities are independent of time, making the algorithms naturally suited to online use. In addition, the proposed methods have been shown to work with the simplest surveillance systems that may already be publicly deployed. Furthermore, direct quantitative performance comparisons are provided.

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“…In general, these methods strive to explicitly and effectively model the non-outliers as means to detect the outliers. 18 As an example, these methods are used by the financial industry to define normal patterns of customer spending in order to capture the anomalous, fraudulent transactions. 19 In this study, we defined mIDH GBM as anomalies within the distribution of all GBMs and utilized established anomaly detection methods for their detection.…”

mentioning

confidence: 99%

Detection of Isocitrate Dehydrogenase Mutated Glioblastomas Through Anomaly Detection Analytics

Taha¹,

Li²,

Boley³

et al. 2021

Neurosurg.

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BACKGROUND The rarity of Isocitrate Dehydrogenase mutated (mIDH) glioblastomas relative to wild-type IDH glioblastomas, as well as their distinct tumor physiology, effectively render them “outliers”. Specialized tools are needed to identify these outliers. OBJECTIVE To carefully craft and apply anomaly detection methods to identify mIDH glioblastoma based on radiomic features derived from magnetic resonance imaging. METHODS T1-post gadolinium images for 188 patients and 138 patients were downloaded from The Cancer Imaging Archive's (TCIA) The Cancer Genome Atlas (TCGA) glioblastoma collection, and from the University of Minnesota Medical Center (UMMC), respectively. Anomaly detection methods were tested on glioblastoma image features for the precision of mIDH detection and compared to standard classification methods. RESULTS Using anomaly detection training methods, we were able to detect IDH mutations from features in noncontrast-enhancing regions in glioblastoma with an average precision of 75.0%, 69.9%, and 69.8% using three different models. Anomaly detection methods consistently outperformed traditional two-class classification methods from 2 unique learning models (67.9%, 67.6%). The disparity in performances could not be overcome through newer, popular models such as neural networks (67.4%). CONCLUSION We employed an anomaly detection strategy in the detection of IDH mutation in glioblastoma using preoperative T1 postcontrast imaging. We show these methods outperform traditional two-class classification in the setting of dataset imbalances inherent to IDH mutation prevalence in glioblastoma. We validate our results using an external dataset and highlight new possible avenues for radiogenomic rare event prediction in glioblastoma and beyond.

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Machine Learning Techniques for Anomalies Detection and Classification

Cited by 22 publications

References 8 publications

Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images

Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images

Learning Algorithms for Anomaly Detection from Images

Detection of Isocitrate Dehydrogenase Mutated Glioblastomas Through Anomaly Detection Analytics

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