Predicting numeric ratings for Google apps using text features and ensemble learning

Umer, Muhammad; Ashraf, Imran; Mehmood, Arif; Ullah, Saleem; Choi, Gyu Sang

doi:10.4218/etrij.2019-0443

Cited by 35 publications

(17 citation statements)

References 25 publications

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“…For example, study [48] uses a stack generalization technique and ensemble learning approach for pulsar prediction. Similarly, ensemble approaches are also used for predicting the numeric scores for Google apps in [49]. Hybrid or ensemble approaches are also used for text analysis [50].…”

Section: Proposed Methodology For Pulsar Detectionmentioning

confidence: 99%

Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach

Lee

Rustam

Aljedaani

et al. 2021

Advances in Astronomy

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Pulsar stars, usually neutron stars, are spherical and compact objects containing a large quantity of mass. Each pulsar star possesses a magnetic field and emits a slightly different pattern of electromagnetic radiation which is used to identify the potential candidates for a real pulsar star. Pulsar stars are considered an important cosmic phenomenon, and scientists use them to study nuclear physics, gravitational waves, and collisions between black holes. Defining the process of automatic detection of pulsar stars can accelerate the study of pulsar stars by scientists. This study contrives an accurate and efficient approach for true pulsar detection using supervised machine learning. For experiments, the high time-resolution (HTRU2) dataset is used in this study. To resolve the data imbalance problem and overcome model overfitting, a hybrid resampling approach is presented in this study. Experiments are performed with imbalanced and balanced datasets using well-known machine learning algorithms. Results demonstrate that the proposed hybrid resampling approach proves highly influential to avoid model overfitting and increase the prediction accuracy. With the proposed hybrid resampling approach, the extra tree classifier achieves a 0.993 accuracy score for true pulsar star prediction.

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Section: Proposed Methodology For Pulsar Detectionmentioning

confidence: 99%

Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach

Lee

Rustam

Aljedaani

et al. 2021

Advances in Astronomy

Self Cite

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“…A comparison of the proposed deep SVM is carried out with other selected machine learning models. These models are selected with regards to the results reported in the existing literature [52][53][54]. From this perspective, RF, LR, ETC, GBM, and SGD are implemented for performance comparison.…”

Section: Performance Comparison With Machine Learning Modelsmentioning

confidence: 99%

Predicting Students’ Academic Performance with Conditional Generative Adversarial Network and Deep SVM

Sarwat

Ullah

Sadiq

et al. 2022

Sensors

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The availability of educational data obtained by technology-assisted learning platforms can potentially be used to mine student behavior in order to address their problems and enhance the learning process. Educational data mining provides insights for professionals to make appropriate decisions. Learning platforms complement traditional learning environments and provide an opportunity to analyze students’ performance, thus mitigating the probability of student failures. Predicting students’ academic performance has become an important research area to take timely corrective actions, thereby increasing the efficacy of education systems. This study proposes an improved conditional generative adversarial network (CGAN) in combination with a deep-layer-based support vector machine (SVM) to predict students’ performance through school and home tutoring. Students’ educational datasets are predominantly small in size; to handle this problem, synthetic data samples are generated by an improved CGAN. To prove its effectiveness, results are compared with and without applying CGAN. Results indicate that school and home tutoring combined have a positive impact on students’ performance when the model is trained after applying CGAN. For an extensive evaluation of deep SVM, multiple kernel-based approaches are investigated, including radial, linear, sigmoid, and polynomial functions, and their performance is analyzed. The proposed improved CGAN coupled with deep SVM outperforms in terms of sensitivity, specificity, and area under the curve when compared with solutions from the existing literature.

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“…In addition to the above-discussed models, this study proposes a voting classifier to enhance the prediction accuracy. The voting classifier is an ensemble model that combines different base models to make the final predictions as voting classifiers tend to show better performance than individual models [45]- [48]. This study uses SVM, RF, and K-NN as sub-estimators and final predictions are obtained using both hard and soft voting methods.…”

Section: ) Prediction Approachmentioning

confidence: 99%

Integrating Learning Analytics and Collaborative Learning for Improving Student’s Academic Performance

et al. 2021

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Big data analytics has shown tremendous success in several fields such as businesses, agriculture, health, and meteorology, and education is no exception. Concerning its role in education, it is used to boost students' learning process by predicting their performance in advance and adapting the relevant instructional design strategies. This study primarily intends to develop a system that can predict students' performance and help teachers to timely introduce corrective interventions to uplift the performance of low-performing students. As a secondary part of this research, it also explores the potential of collaborative learning as an intervention to act in combination with the prediction system to improve the performance of students. To support such changes, a visualization system is also developed to track and monitor the performance of students, groups, and overall class to help teachers in the regrouping of students concerning their performance. Several well-known machine learning models are applied to predict students performance. Results suggest that experimental groups performed better after treatment than before treatment. The students who took part in each class activity, prepared and submitted their tasks perform much better than other students. Overall, the study found that collaborative learning methods play a significant role to enhance the learning capability of the students.

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Predicting numeric ratings for Google apps using text features and ensemble learning

Cited by 35 publications

References 25 publications

Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach

Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach

Predicting Students’ Academic Performance with Conditional Generative Adversarial Network and Deep SVM

Integrating Learning Analytics and Collaborative Learning for Improving Student’s Academic Performance

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