Machine Learning

Λουρίδας, Πάνος; Ebert, Christof

doi:10.1109/ms.2016.114

Cited by 170 publications

(90 citation statements)

References 9 publications

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“…Two data analysis approaches that are specially focused in prediction and could be of great service to prognostic studies are: machine learning (ML) and microsimulation (MS) [8,9]. …”

Section: Introductionmentioning

confidence: 99%

Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review

et al. 2017

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BackgroundDementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is known about its mechanisms. Having prognostic estimates about dementia can help researchers, patients and public entities in dealing with this disorder. Thus, health data, machine learning and microsimulation techniques could be employed in developing prognostic estimates for dementia.ObjectiveThe goal of this paper is to present evidence on the state of the art of studies investigating and the prognosis of dementia using machine learning and microsimulation techniques.MethodTo achieve our goal we carried out a systematic literature review, in which three large databases—Pubmed, Socups and Web of Science were searched to select studies that employed machine learning or microsimulation techniques for the prognosis of dementia. A single backward snowballing was done to identify further studies. A quality checklist was also employed to assess the quality of the evidence presented by the selected studies, and low quality studies were removed. Finally, data from the final set of studies were extracted in summary tables.ResultsIn total 37 papers were included. The data summary results showed that the current research is focused on the investigation of the patients with mild cognitive impairment that will evolve to Alzheimer’s disease, using machine learning techniques. Microsimulation studies were concerned with cost estimation and had a populational focus. Neuroimaging was the most commonly used variable.ConclusionsPrediction of conversion from MCI to AD is the dominant theme in the selected studies. Most studies used ML techniques on Neuroimaging data. Only a few data sources have been recruited by most studies and the ADNI database is the one most commonly used. Only two studies have investigated the prediction of epidemiological aspects of Dementia using either ML or MS techniques. Finally, care should be taken when interpreting the reported accuracy of ML techniques, given studies’ different contexts.

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“…Two data analysis approaches that are specially focused in prediction and could be of great service to prognostic studies are: machine learning (ML) and microsimulation (MS) [8,9]. …”

Section: Introductionmentioning

confidence: 99%

Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review

et al. 2017

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“…It offers a capability that accelerates data-driven insights and knowledge acquisition. Although machine learning has been around for decades, it's the all-pervasive nature of data due to social networking, videostreaming, cloud computing etc., machine learning has become the pioneer of business intelligence [3].…”

Section: Machine Learning Insightsmentioning

confidence: 99%

Machine Learning Approaches for Healthcare Data Analysis

Eedi¹,

Kolla²

2020

jcr

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Breast cancer is the most common cancer in women worldwide and it remains the most common cause of cancer-related death in woman globally. Machine Learning techniques have been proven to be of great help in prognosis and diagnosis of various health related issues. This work constitutes a comparison of five machine learning (ML) algorithms: Logistic Regression (LR), K-Nearest Neighbor (KNN), Naive-Bayes (NB), Decision Tree (DT), Random Forest (RF) on the Breast Cancer Wisconsin Diagnostic (BCWD) dataset. Features were extracted from the digitized images of FNA tests on a breast mass. Results show that Random Forests performs better among all the models across different classification metrics such as accuracy, precision, recall, and f1-score.

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“…According to [29] the general idea behind most of the machine learning framework is that by training using an example set of training data, the program learns to perform a task. This training enables the distributed computer and controller system to perform similar tasks where the system is confronted with entirely new datasets that have not been previously encountered.…”

Section: Machine Learning Approachmentioning

confidence: 99%

Effects of Machine Learning Approach in Flow-Based Anomaly Detection on Software-Defined Networking

Dey

Rahman²

2019

Symmetry

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Recent advancements in software-defined networking (SDN) make it possible to overcome the management challenges of traditional networks by logically centralizing the control plane and decoupling it from the forwarding plane. Through a symmetric and centralized controller, SDN can prevent security breaches, but it can also bring in new threats and vulnerabilities. The central controller can be a single point of failure. Hence, flow-based anomaly detection system in OpenFlow Controller can secure SDN to a great extent. In this research, we investigated two different approaches of flow-based intrusion detection system in OpenFlow Controller. The first of which is based on machine-learning algorithm where NSL-KDD dataset with feature selection ensures the accuracy of 82% with random forest classifier using the gain ratio feature selection evaluator. In the later phase, the second approach is combined with a deep neural network (DNN)-based intrusion detection system based on gated recurrent unit-long short-term memory (GRU-LSTM) where we used a suitable ANOVA F-Test and recursive feature elimination selection method to boost classifier output and achieve an accuracy of 88%. Substantial experiments with comparative analysis clearly show that, deep learning would be a better choice for intrusion detection in OpenFlow Controller.Symmetry 2020, 12, 7 2 of 21 of the network, taking advantage of decision-making from the global perspective of the network in the controller. However, to be successful in current dynamic environments, traffic monitoring becomes a cornerstone in SDN given that management applications often need to make use of accurate and timely traffic measurements. Forwarding components like switches, routers, etc., are elements of the data plane and the controller is the only element of the control plane. Decoupling of the network control and forwarding functions, and direct programmability of the network give network managers enough control over the network. Separation of the routing and forwarding activities of networking components (e.g., switches, routers, etc.) from the data plane, makes the administration and management of the network straightforward because the control plane now only has to handle logical network topology-related information, traffic routing, and so forth while the data plane only needs to manage the network traffic using the configuration provided by the control unit. The SDN paradigm was the outcome of some works of the research world that suggested the necessity of building programmable networks as a practical way to experiment with new protocols in production networks. From its inception, it has gained lots of attention from academia and industry. It is supported by giants of the Internet world like Google, Cisco, HP, Juniper or NEC and by standardization organizations like the Open Network Foundation (ONF) or the Internet Engineering Task Force (IETF), so one can state that this network paradigm has a lot of potential to succeed. The proposal of the OpenFlow protocol [2] in 2008 wa...

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Machine Learning

Cited by 170 publications

References 9 publications

Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review

Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review

Machine Learning Approaches for Healthcare Data Analysis

Effects of Machine Learning Approach in Flow-Based Anomaly Detection on Software-Defined Networking

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