Prediction model for students' future development by deep learning and tensorflow artificial intelligence engine

Fok, Wwt; He, Yuze; Yeung, H.W.; Law, Kan Yip; Cheung, Ka Chun; Ai, Yy.; Ho, Pui-Kuen

doi:10.1109/infoman.2018.8392818

Cited by 53 publications

(25 citation statements)

References 4 publications

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“…These results were obtained from the model implemented using advanced neural network tools such as Keras [34] and Tensorflow [35]. In this model, 130 iterations were used for the data relation for every 60 values analyzed.…”

Section: Resultsmentioning

confidence: 99%

Air Quality Prediction (PM2.5 and PM10) at the Upper Hunter Town - Muswellbrook using the Long-Short-Term Memory Method

Delgado¹,

Ricardo²,

Carbajal³

2020

IJACSA

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Air quality is crucial for the environment and the life quality of citizens. Therefore, in the present study a software application is developed to predict air quality on the basis of 2.5 particulate matter (.) and 10particulate matter (), in the city of Upper Hunter, Australia, as it is considered to be one of the cities with the lowest air quality levels worldwide. For this purpose, it has been decided to use the methodology of long-short term memory (LSTM) from data collected by NSW department of planning industry and environment during the period of 30 September 2012 to 30 September 2019, to predict the behavior of the mentioned particulate matter during the month of October 2019. A comparison between the average and maximum values suggested by the software and the actual values has been made and it is shown that the predicted results of the study are quite close to reality. Finally, the results obtained in this study may serve as a basis for local authorities to proceed with the necessary protocols and measures in case an alarming prediction occurs.

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Section: Resultsmentioning

confidence: 99%

Air Quality Prediction (PM2.5 and PM10) at the Upper Hunter Town - Muswellbrook using the Long-Short-Term Memory Method

Delgado¹,

Ricardo²,

Carbajal³

2020

IJACSA

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“…An in-depth model analysis is conducted for the dataset of not only traditional academic achievements, including mathematics, Chinese, English, physics, chemistry, biology, and history, but also for a dataset of services, behavior, sports, and art [74][75][76][77][78]. A deep learning model called the Tensor flow [80][81][82][83][84][85] engine is an example of including the number of intermediate nodes and the number of in-depth study layers for a cumbersome dataset. That is, from the database of 2,000 students, 75% of this data were used as training data and 25% were used as test data to predict students' future pathways with accuracy rates ranging from 80% to 91%.…”

Section: Exam Revisionmentioning

confidence: 99%

“…The optimal configuration of a tensor-flow deep learning model achieves a high predictive accuracy for large databases. Similar to the Tensor flow model, another model [84] utilizes the feature selection correlation methods, Csquare and Euclidean distance, to predict weak students. The researchers also compared the prediction results with Naive bayes, K-neighbor, and End tree's artificial neural network classifiers and determined accurate prediction results.…”

Section: Figure 4 Dnn Predicting the Student Performance In E-learning Environment [73]mentioning

confidence: 99%

Feature Evaluation of Emerging E-Learning Systems Using Machine Learning: An Extensive Survey

et al. 2021

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As of late, with the progression of AI and man-made brainpower, there has been a developing spotlight on versatile e-learning. As all ways to deal with e-learning lose their allure and the level of online courses builds, they move towards more customized versatile learning so as to collaborate with students and achieve better learning results. The schools focus on the examination, mindfulness, and arranging techniques that infuse innovation into the vision and educational program. E-learning issues are a standard examination issue for us all. The motivation behind this research analysis is to separate the potential outcomes of assessing e-learning models utilizing AI strategies such as Supervised, Semi Supervised, Reinforced Learning advances by investigating upsides and downsides of various methods organization. The literature review methodology is to review the cross sectional impacts of e-learning and Machine learning algorithms from existing literatures from the year 1993 to 2020 and to assess the essentialness of elearning features to optimize the e-learning models with available Machine learning techniques from peerinspected journals, capable destinations, and books. Second, it legitimizes the chances of e-learning structures introduction, and changes demonstrated through AI and Machine Learning algorithms. This examination assists in providing helpful new highlights to analysts, researchers and academicians. It gives an exhaustive structure of existing e-learning frameworks for the most recent innovations identified with learning framework capacities and learning tasks to envision ML research openings in appropriate spaces. The survey paper identifies and demonstrates the important role of different types of e-learning features such as Individual pertinent feature, Course pertinent feature, Context pertinent feature and Technology pertinent feature in framework performance tuning. The performance of Machine Learning algorithms to optimize the features of E-Learning models were reviewed in previous literatures and Support Vector Machine technique was found to be the one of the best to predict the input and output parameters of elearning models and it is found that Fuzzy C Means, Deep Learning algorithms are producing better results for Big Data sets.

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“…Some of the papers are presented that is much related to this experiment. Fok et al [1] applied Deep Learning (DL) analytic engine to evaluates the student's performance. The study involves the analysis of the influence of academic performance and their extracurricular activities like services, arts, and their conduct.…”

Section: Literature Reviewmentioning

confidence: 99%

Student Classification Based on Cognitive Abilities and Predicting Learning Performances Using Machine Learning Models

Vital¹,

Sangeeta²,

Kumar³

2021

IJCDS

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Prediction model for students' future development by deep learning and tensorflow artificial intelligence engine

Cited by 53 publications

References 4 publications

Air Quality Prediction (PM2.5 and PM10) at the Upper Hunter Town - Muswellbrook using the Long-Short-Term Memory Method

Air Quality Prediction (PM2.5 and PM10) at the Upper Hunter Town - Muswellbrook using the Long-Short-Term Memory Method

Feature Evaluation of Emerging E-Learning Systems Using Machine Learning: An Extensive Survey

Student Classification Based on Cognitive Abilities and Predicting Learning Performances Using Machine Learning Models

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