Modeling Student’s Academic Performance During Covid-19 Based on Classification in Support Vector Machine

Samsudin, Et. al. Nor Ain Maisarah

doi:10.17762/turcomat.v12i5.2190

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…In order to improve the quality and efficiency of data mining, it is necessary to clean up the collected data, fill in the missing values, smooth the noise data, correct the inconsistent data and convert all the data into a unified data format. The data information in multiple data sources is integrated, and according to the research purpose, the data is transformed into an analytical data model by means of smooth aggregation, data generalization and standardization [8].…”

Section: Data Mining Classification Implementation Processmentioning

confidence: 99%

Data Analysis of Undergraduate Employment Based on Decision Classification Algorithm

Guan¹,

Luo²

2023

Atlantis Highlights in Social Sciences, Education and Humanities

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When the school uses the employment situation analysis system to analyze the employment situation of undergraduate students every year, because the classification decision rules are complex, it is difficult for the system to understand the complex rules when analyzing the employment situation of graduates, which leads to the slow operation of the system. Therefore, a brand-new employment situation analysis system is designed based on the decision classification algorithm. On the system hardware, the communication circuit of CAN bus is redesigned. On the software side, we use the decision classification algorithm to set up a most concise system classification decision rule, so as to automatically generate the analysis results of the employment situation of undergraduate graduates. The proposed system is applied to the analysis of the employment situation of undergraduate graduates in a university. The results show that the time taken by the system to analyze the employment situation is 97 356 ms and 70 372 ms shorter than the previous system. It can be seen that the rules set this time are easier to be understood by the system, and the results of employment situation analysis can be quickly obtained.

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Section: Data Mining Classification Implementation Processmentioning

confidence: 99%

Data Analysis of Undergraduate Employment Based on Decision Classification Algorithm

Guan¹,

Luo²

2023

Atlantis Highlights in Social Sciences, Education and Humanities

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“…There are numerous different hyperplanes that may be used to split data points into classes. According to studies [9] that have used SVM to model student academic performance, the greatest accuracy attained by SVM employing a linear kernel is 73.68%. Using a radial basis kernel, SVM was able to predict academic achievement with 90% accuracy [12].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

A comparative study of machine learning algorithms for virtual learning environment performance prediction

Ismanto

Ghani

Saleh

2023

IJ-AI

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Virtual learning environment is becoming an increasingly popular study option for students from diverse cultural and socioeconomic backgrounds around the world. Although this learning environment is quite adaptable, improving student performance is difficult due to the online-only learning method. Therefore, it is essential to investigate students' participation and performance in virtual learning in order to improve their performance. Using a publicly available Open University learning analytics dataset, this study examines a variety of machine learning-based prediction algorithms to determine the best method for predicting students' academic success, hence providing additional alternatives for enhancing their academic achievement. Support vector machine, random forest, Nave Bayes, logical regression, and decision trees are employed for the purpose of prediction using machine learning methods. It is noticed that the random forest and logistic regression approach predict student performance with the highest average accuracy values compared to the alternatives. In a number of instances, the support vector machine has been seen to outperform the other methods.

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“…Ninawe [18] presented a brand-new method for identifying fruit images. In addition, Peruma [19] employed computer vision processing techniques and a classifier based on Support Vector Machine (SVM) to test the efficient philosophy proposed for recognizing sicknesses found in guava leaves.Behera [20] also conducted investigations on papaya fruits using a variety of classification algorithms, including K-Nearest Neighbor and SVM. A range of machine learning algorithms were employed to identify and distinguish the fruit visuals that were afflicted with multiple illnesses.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Guava Wilt Disease Segmentation Using K-Means Clustering and Neural Network Techniques

Mirjat,

Mahar,

Siddiqui

et al. 2024

VAWKUM trans. comput. sci.

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Guava fruit production is influenced by myriad of external and internal and the rapid decline in production is often attributed to various diseases. Among these, ‘Wilt’ disease stands out as a significant contributor to the decrease in guava fruit yield. This paper proposes and implement a framework leveraging advanced technologies, including image processing and machine learning techniques, to detect wilt disease at its early stages. To facilitate this, a database comprising 1420 images of guava plant leaves affected by wilt disease is created. The database is further categorized into three datasets based on the level of noise present in the images: fully noisy images, partially noisy images and noise-free-images. The segmentation of guava wilt disease is achieved through the application of K-means clustering sand Convolutional Neural Network techniques on the guava images. Remarkably, the proposed framework demonstrates a cumulative accuracy of 93.82% and 95.17% using K-means clustering and neural network algorithms, respectively. This innovative approach hold s promise for effectively managing and increasing guava fruit production by identifying and addressing wilt disease in its early stages.

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Modeling Student’s Academic Performance During Covid-19 Based on Classification in Support Vector Machine

Cited by 9 publications

References 5 publications

Data Analysis of Undergraduate Employment Based on Decision Classification Algorithm

Data Analysis of Undergraduate Employment Based on Decision Classification Algorithm

A comparative study of machine learning algorithms for virtual learning environment performance prediction

A Framework for Guava Wilt Disease Segmentation Using K-Means Clustering and Neural Network Techniques

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