Applications of Machine Learning in Anomaly Detection

Nikhil, Gadilli Sai; Rajasekaran, Arun Sekar; Parimala, M.; Velliangiri, S.

doi:10.1007/978-981-16-8484-5_48

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…Every distinct class has a binary column that indicates whether it exists or not. To ensure accurate categorical information representation in a manner appropriate for machine learning models [12], this transformation is essential for algorithms that need numerical inputs. OneHotEncoder is especially helpful in classification situations when it's necessary to convert categorical labels into a format that predictive models can understand.…”

Section: Splitting Datamentioning

confidence: 99%

Optimizing Breast Cancer Diagnosis with Advanced Deep Learning Techniques in Medical Imaging

Veera V Rama Rao M

2024

jes

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This research explores the integration of deep learning techniques into medical imaging for early breast cancer detection. Focused on enhancing current methodologies, the study develops a specialized deep learning model using a diverse dataset of medical images. The primary objectives include evaluating the model's performance, identifying strengths and limitations, and addressing ethical considerations inherent in deploying such technology in healthcare. The findings offer significant implications for advancing early breast cancer detection, potentially revolutionizing diagnostic practices and improving patient outcomes. The study contributes to bridging existing gaps in the literature, providing novel insights into the potential of deep learning in the context of medical imaging. By examining the model's efficacy, ethical considerations, and its broader impact on healthcare, this research lays the foundation for further innovations in the critical intersection of artificial intelligence and early cancer diagnostics.

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Section: Splitting Datamentioning

confidence: 99%

Optimizing Breast Cancer Diagnosis with Advanced Deep Learning Techniques in Medical Imaging

Veera V Rama Rao M

2024

jes

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“…Moreira et al [3] proposed ISAD an intelligent system for network anomaly detection by combining fog computing and cloud computing approaches. The proposed model services machine learning methods to comprehend the normal performance of the system currents in the keen situation, provided that firm alteration to the common besides unexpected vagaries in system performance to evaluate the performance of the proposed technique it is tested by creating an artificial fog environment using Microsoft AZURE.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Keen Surroundings incline in the harvest extra system streams then old-fashioned complexes, payable to the mammoth ruler for shrewd campaigns in the system, as well as the various types of applications in these devices. Hence, the monitoring of these network currents produces a huge capacity of information, assembly the presentation for fogging and utility calculating indispensable to this situation [3].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection for IOT/Cloud-Based Model in Fog Computing Using Machine Learning

Nayak¹,

Khan²

2022

IJOSCIENCE

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We know that the key technologies that are involved in the Internet of Things are wireless sensor networks and cloud computing, big data, embedded systems, and the internet. It is a giant network with connected devices. These devices gather and share data. But many IoT devices have poor security and cybercriminals are taking benefit of this. The two techniques cloud and fog computing both combined can be used to transfer secure data in IoT devices as cloud computing provides storage of data on cloud servers and fog computing offers us various services to access data and provides support for cloud servers. This research work presents various techniques to detect an intruder and anomaly detection in IoT-based cloud systems. Also, a comparison of all the techniques used to detect intruders and anomalies are compared on various parameters like accuracy, performance, efficiency, precision, recall, the detection rate.

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Applications of Machine Learning in Anomaly Detection

Cited by 2 publications

References 5 publications

Optimizing Breast Cancer Diagnosis with Advanced Deep Learning Techniques in Medical Imaging

Optimizing Breast Cancer Diagnosis with Advanced Deep Learning Techniques in Medical Imaging

Anomaly Detection for IOT/Cloud-Based Model in Fog Computing Using Machine Learning

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