Learning ANOVA concepts using simulation

Chandrakantha, Leslie

doi:10.1109/aseezone1.2014.6820644

Cited by 15 publications

(4 citation statements)

References 11 publications

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“…A one-way ANOVA was conducted to compare the effect of FB usage on undergraduate students' academic performance at Charmo University. The reason of using ANOVA is, The ANOVA is an essential test because it allows researchers to see for example how effective two different kinds of treatment are and how durable they are (Chandrakantha, 2014).…”

Section: Advanced Analysis Of the Survey By Spssmentioning

confidence: 99%

The Effect of Facebook on Academic Performance for Undergraduate Students at Charmo University

M-Saleh¹,

Abdul²,

Ameen³

2017

IJCT

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Facebook occupies an important part of people's time and life due to the recent developments in the field of communication technologies (CT). This paper aims at investigating the Facebook impact (FI) on undergraduate students at Charmo University. Moreover, Facebook also has a great impact on students' life studies at universities. However, in addition to its benefits, Facebook also has some side effects on students' life study. In regard to data collection, an online survey was used in which 15 questions were answered by 100 participants from different faculties at Charmo University. The data collection process was conducted from First of March to 30 of April 2016. The program of SPSS was used to analyze the results of the survey. The results of the current study show that students spend a significant amount of their times on using Facebook.

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Section: Advanced Analysis Of the Survey By Spssmentioning

confidence: 99%

The Effect of Facebook on Academic Performance for Undergraduate Students at Charmo University

M-Saleh¹,

Abdul²,

Ameen³

2017

IJCT

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“…To further reinforce the results of our performance metric, we perform significance testing to evaluate the impact each learner and class distribution has on AUC values. We use ANalysis Of VAriance (ANOVA) [45] as a means to determine if our factors are equal. As we are evaluating the significance of both learner and distribution impact, we perform two-way ANOVA [46].…”

Section: Metricsmentioning

confidence: 99%

Impact of class distribution on the detection of slow HTTP DoS attacks using Big Data

Calvert

Khoshgoftaar

2019

J Big Data

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IntroductionThe continued rise of network and cloud-based applications allow for companies to provide robust and dynamic services to their customers across the world both cheaply and efficiently. In 2018, nearly 80% of enterprises have begun to experiment with apps running on cloud or network based platforms [1]. Due to the growing reliance on such services, the security of the networks hosting these applications is more paramount than ever. Attackers are very much aware of the importance these applications hold for various companies, making them obvious targets for malicious exploits. Industry domains ranging from telecommunications to software publishing are finding themselves under AbstractThe integrity of modern network communications is constantly being challenged by more sophisticated intrusion techniques. Attackers are consistently shifting to stealthier and more complex forms of attacks in an attempt to bypass known mitigation strategies. In recent years, attackers have begun to focus their attack efforts on the application layer, allowing them to produce attacks that can exploit known issues within specific application protocols. Slow HTTP Denial of Service attacks are one such attack variant, which targets the HTTP protocol and can imitate legitimate user traffic in order to deny resources from a service. Successful mitigation of this attack type requires network analysts to evaluate large quantities of network traffic to identify and block intrusive traffic. The issue, is that the number of legitimate traffic instances can far outnumber the amount of attack instances, making detection problematic. Machine learning techniques can be used to aid in detection, but the large level of imbalance between normal (majority) and attack (minority) instances can lead to inaccurate detection results. In this work, we evaluate the use of data sampling to produce varying class distributions in order to counteract the effects of severely imbalanced Slow HTTP DoS big datasets. We also detail our process for collecting real-world representative Slow HTTP DoS attack traffic from a live network environment to create our datasets. Five class distributions are generated to evaluate the Slow HTTP DoS detection performance of eight machine learning techniques. Our results show that the optimal learner and class distribution combination is that of Random Forest with a 65:35 distribution ratio, obtaining an AUC value of 0.99904. Further, we determine through the use of significance testing, that the use of sampling techniques can significantly increase learner performance when detecting Slow HTTP DoS attack traffic. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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“…Data Processing with ANOVA Ideally, the importance of a particular feature (generally a voxel) can be quantified in the context of a certain classification task. The ANOVA F-score [6] for each feature yields a value that identifies which features vary significantly between any of the categories in the dataset.…”

Section: Data Setmentioning

confidence: 99%

fMRI classification based on analysis of variance combined with support vector machine

Sun

Park

2015

2015 International Conference on Information and Communication Technology Convergence (ICTC)

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To relieve the curse of dimensionality in functional magnetic resonance imaging (fMRI), we combine analysis of variance (ANOVA) with a support vector machine (SVM) to form a feature-based classification method. ANOVA is applied to find a more compact representation of the data by extracting features from fMRI images. A linear kernel SVM classifier is then trained on the selected features. Combining ANOVA with SVM significantly reduces the computational burden of the SVM process and establishes a less complex classifier. Experiments using Haxby's dataset to classify fMRI images show that the proposed method yields good performance, with an average accuracy of up to 96.25%.

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Learning ANOVA concepts using simulation

Cited by 15 publications

References 11 publications

The Effect of Facebook on Academic Performance for Undergraduate Students at Charmo University

The Effect of Facebook on Academic Performance for Undergraduate Students at Charmo University

Impact of class distribution on the detection of slow HTTP DoS attacks using Big Data

fMRI classification based on analysis of variance combined with support vector machine

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