Implementation of Neural Network to determine the New College Students

Putra, Ricky Eka; Nurhidayat, Andi Iwan; Wicaksono, Ardian Yusuf

doi:10.1088/1757-899x/288/1/012121

Cited by 9 publications

(4 citation statements)

References 11 publications

(11 reference statements)

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“…Recently, some researchers successfully simulated the SsAP system by proposing predicting models that classify the undergraduate students' academic admission using back-propagation [Rodríguez-Hernández et al, 2021]; [Prasetyawan et al, 2018]. Also, other researchers used the perceptron concept to implement an admission system with supervised neural networks [kurniadi et al, 2021]; [Putra et al, 2018].…”

Section: Related Work and Research Gapmentioning

confidence: 99%

Simulating and Predicting Students’ Academic Performance Using a New Approach based on STEAM Education

Abdulwahid¹,

Fakhfakh²,

Amous³

2022

jucs

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In many countries, particularly in Iraq, the students’ academic performance (SsAP) system is based on the final grade scores in high school. This final high school grade may not reflect the students’ intelligence level or the interests that link the student to a relevant university. Also, skills are not used to predict score-related school or college. In this research, a seven-subject, one-grade, one-output (SOO) model was proposed to simulate the classic SsAP system to show that the predicting system is completely based on the previous year’s score and not on the students’ interests and skills. Moreover, a seven-subject, twelve-year, seven-output (STS) model, which used seven parallel deep neural networks with a scaled conjugate learning algorithm, was employed to determine the students’ science, technology, engineering, art, and mathematics (STEAM) skills and interests across 12 grades and predict their corresponding most appropriate school. This article contributed to constructing two models: SOO model which simulates the classical Iraqi education system, and the STS model which predicts the acceptance of students according to the STEAM system, which is what makes it different from previous research. The results revealed that the SOO model properly simulated the classic SsAP system. Furthermore, the new approach based on STEAM education successfully predicted students’ academic performance in line with their skills and interests over a twelve-year period. The overall accuracy rate of the two proposed models (SOO and STS) is about 99% with 10-5 histogram errors between the target and the actual output. However, the optimized epochs of the SOO model are 1000 epochs while the STS model got 10–600 epochs.

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Section: Related Work and Research Gapmentioning

confidence: 99%

Simulating and Predicting Students’ Academic Performance Using a New Approach based on STEAM Education

Abdulwahid¹,

Fakhfakh²,

Amous³

2022

jucs

View full text Add to dashboard Cite

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“…After the data is transformed, the prediction process is continued to conduct training and testing on the network architecture that has been built based on the predictor variables from the data analysis process. This process will produce the optimal weight in the form of performance and epoch values based on the previous network pattern [33], [34]. The training and testing results table can be seen in Table 6.…”

Section: Predict Annmentioning

confidence: 99%

Implementation multiple linear regresion in neural network predict gold price

Yanto

Sanjaya

Yulasmi

et al. 2021

IJEECS

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<p>The movement of gold prices in the previous period was crucial for investors. However, fluctuations in gold price movements always occur. The problem in this study is how to apply multiple linear regression (MRL) in predicting artificial neural networks (ANN) of gold prices. MRL is mathematical calculation technique used to measure the correlation between variables. The results of the MRL analysis ensure that the network pattern that is formed can provide precise and accurate prediction results. In addition, this study aims to develop a predictive pattern model that already exists. The results of the correlation test obtained by MRL provide a correlation of 62% so that the test results are said to have a significant effect on gold price movements. Then the prediction results generated using an ANN has a mean squared error (MSE) value of 0.004264%. The benefits obtained in this study provide an overview of the gold price prediction pattern model by conducting learning and approaches in testing the accuracy of the use of predictor variables.</p>

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“…Recently, ML models have started to be deployed into highimpact, real-world decision-making settings such as medicine [16], self-driving cars [12], and college admissions [40]. However, this has led to problems: many of these settings have key constraints that ML models were not originally designed to handle.…”

Section: Accuracymentioning

confidence: 99%

MPC-Friendly Commitments for Publicly Verifiable Covert Security

Agrawal¹,

Bell²,

Gascón³

et al. 2021

Preprint

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We address the problem of efficiently verifying a commitment in a two-party computation. This addresses the scenario where a party P1 commits to a value 𝑥 to be used in a subsequent secure computation with another party P2 that wants to receive assurance that P1 did not cheat, i.e. that 𝑥 was indeed the value inputted into the secure computation. Our constructions operate in the publicly verifiable covert (PVC) security model, which is a relaxation of the malicious model of MPC appropriate in settings where P1 faces a reputational harm if caught cheating.We introduce the notion of PVC commitment scheme and indexed hash functions to build commitments schemes tailored to the PVC framework, and propose constructions for both arithmetic and Boolean circuits that result in very efficient circuits. From a practical standpoint, our constructions for Boolean circuits are 60× faster to evaluate securely, and use 36× less communication than baseline methods based on hashing. Moreover, we show that our constructions are tight in terms of required non-linear operations, by proving lower bounds on the nonlinear gate count of commitment verification circuits. Finally, we present a technique to amplify the security properties our constructions that allows to efficiently recover malicious guarantees with statistical security.

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Implementation of Neural Network to determine the New College Students

Cited by 9 publications

References 11 publications

Simulating and Predicting Students’ Academic Performance Using a New Approach based on STEAM Education

Simulating and Predicting Students’ Academic Performance Using a New Approach based on STEAM Education

Implementation multiple linear regresion in neural network predict gold price

MPC-Friendly Commitments for Publicly Verifiable Covert Security

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Implementation of Neural Network to determine the New College Students

Cited by 9 publications

References 11 publications

Simulating and Predicting Students&rsquo; Academic Performance Using a New Approach based on STEAM Education

Simulating and Predicting Students&rsquo; Academic Performance Using a New Approach based on STEAM Education

Implementation multiple linear regresion in neural network predict gold price

MPC-Friendly Commitments for Publicly Verifiable Covert Security

Contact Info

Product

Resources

About

Simulating and Predicting Students’ Academic Performance Using a New Approach based on STEAM Education

Simulating and Predicting Students’ Academic Performance Using a New Approach based on STEAM Education