Case study using analysis of variance to determine groups’ variations

Ardelean, Flavius

doi:10.1051/matecconf/201712604008

Cited by 5 publications

(3 citation statements)

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“…If F-value is greater than the F-critical, the null hypothesis is rejected. Otherwise, the null hypothesis is accepted if F-critical is greater than F-value [28], [29]. To compute ANOVA variables, we use a confidence interval of 0.95 and the resulting output in Table 1.…”

Section: Discussionmentioning

confidence: 99%

Automatic temperature detector to mitigate the spread of COVID-19

Ibnugraha¹,

Handayani²,

NonAlinsavath³

2023

J.INFOTEL

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The COVID-19 causes wide impact in business operation. The enterprise must mitigate the risk of COVID-19 spread in its environment. The monitoring of body temperature for employees can be applied as a method to prevent COVID-19 spread. However, the monitoring system must consider several factors such as contactless system, accountable, and simple. The integration between IR temperature sensor and attendance system based on ESP32 is able to provide those need. The use of proximity, IR, and RFID sensor is affordable to detect body temperature properly within 10 cm. The proposed system provides notification if user gets fever or suspect of COVID-19 by detecting the body temperature. The accuracy of sensor is adequate. It is based on the comparison testing between proposed system with body thermometer where the testing is performed 30 times for each condition. In order to deduce the comparison result, this study uses analysis of variance method. The analysis produces F-critical (4,006) greater than F-value (0,022) where it means that the proposed system and body thermometer have similar testing result. It is shown good accuracy for the proposed system.

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Section: Discussionmentioning

confidence: 99%

Automatic temperature detector to mitigate the spread of COVID-19

Ibnugraha¹,

Handayani²,

NonAlinsavath³

2023

J.INFOTEL

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“…For future work, it is worth investigating the application of feature selection on the original dataset features before encoding into Amino acids. Feature selection methods to be tried include Information Gain (IG) [54], the Analysis Of Variance (ANOVA) test [55], and the Chi-squared (X 2 ) statistic test [56]. The exploration of ensemble techniques, combining multiple models that may achieve competitive accuracy and robustness, is another direction for future work.…”

Section: Discussionmentioning

confidence: 99%

Network Intrusion Detection Based on Amino Acid Sequence Structure Using Machine Learning

Ibaisi,

Kuhn,

Kaiiali

et al. 2023

Electronics

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The detection of intrusions in computer networks, known as Network-Intrusion-Detection Systems (NIDSs), is a critical field in network security. Researchers have explored various methods to design NIDSs with improved accuracy, prevention measures, and faster anomaly identification. Safeguarding computer systems by quickly identifying external intruders is crucial for seamless business continuity and data protection. Recently, bioinformatics techniques have been adopted in NIDSs’ design, enhancing their capabilities and strengthening network security. Moreover, researchers in computer science have found inspiration in molecular biology’s survival mechanisms. These nature-designed mechanisms offer promising solutions for network security challenges, outperforming traditional techniques and leading to better results. Integrating these nature-inspired approaches not only enriches computer science, but also enhances network security by leveraging the wisdom of nature’s evolution. As a result, we have proposed a novel Amino-acid-encoding mechanism that is bio-inspired, utilizing essential Amino acids to encode network transactions and generate structural properties from Amino acid sequences. This mechanism offers advantages over other methods in the literature by preserving the original data relationships, achieving high accuracy of up to 99%, transforming original features into a fixed number of numerical features using bio-inspired mechanisms, and employing deep machine learning methods to generate a trained model capable of efficiently detecting network attack transactions in real-time.

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“…The AAPred model uses selection algorithms to extract features and then reduces the number of features eliminating those that are irrelevant. Three different methods of feature selection are used: Information gain (IG) [ 25 ], Analysis of variance (ANOVA) test [ 26 ], and Chi-square (χ 2 ) statistic test [ 27 ]. …”

Section: Methodsmentioning

confidence: 99%