A Recommendation System for Selecting the Appropriate Undergraduate Program at Higher Education Institutions Using Graduate Student Data

Zayed, Yara; Salman, Yasmeen; Hasasneh, Ahmad

doi:10.3390/app122412525

Cited by 13 publications

(12 citation statements)

References 29 publications

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“…SVM is one of the supervised learning techniques for classification [ 49 ], regression [ 50 ], and outlier detection [ 51 ]. SVM is a classifier that works by creating a hyperplane or multiple hyperplanes for separation, which implies giving the training data labels based on the optimal hyperplane that will categorize the new sample [ 52 ].…”

Section: Methodsmentioning

confidence: 99%

Infant Cry Signal Diagnostic System Using Deep Learning and Fused Features

2023

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Early diagnosis of medical conditions in infants is crucial for ensuring timely and effective treatment. However, infants are unable to verbalize their symptoms, making it difficult for healthcare professionals to accurately diagnose their conditions. Crying is often the only way for infants to communicate their needs and discomfort. In this paper, we propose a medical diagnostic system for interpreting infants’ cry audio signals (CAS) using a combination of different audio domain features and deep learning (DL) algorithms. The proposed system utilizes a dataset of labeled audio signals from infants with specific pathologies. The dataset includes two infant pathologies with high mortality rates, neonatal respiratory distress syndrome (RDS), sepsis, and crying. The system employed the harmonic ratio (HR) as a prosodic feature, the Gammatone frequency cepstral coefficients (GFCCs) as a cepstral feature, and image-based features through the spectrogram which are extracted using a convolution neural network (CNN) pretrained model and fused with the other features to benefit multiple domains in improving the classification rate and the accuracy of the model. The different combination of the fused features is then fed into multiple machine learning algorithms including random forest (RF), support vector machine (SVM), and deep neural network (DNN) models. The evaluation of the system using the accuracy, precision, recall, F1-score, confusion matrix, and receiver operating characteristic (ROC) curve, showed promising results for the early diagnosis of medical conditions in infants based on the crying signals only, where the system achieved the highest accuracy of 97.50% using the combination of the spectrogram, HR, and GFCC through the deep learning process. The finding demonstrated the importance of fusing different audio features, especially the spectrogram, through the learning process rather than a simple concatenation and the use of deep learning algorithms in extracting sparsely represented features that can be used later on in the classification problem, which improves the separation between different infants’ pathologies. The results outperformed the published benchmark paper by improving the classification problem to be multiclassification (RDS, sepsis, and healthy), investigating a new type of feature, which is the spectrogram, using a new feature fusion technique, which is fusion, through the learning process using the deep learning model.

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Section: Methodsmentioning

confidence: 99%

Infant Cry Signal Diagnostic System Using Deep Learning and Fused Features

2023

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“…In [15], utilizes supervised machine learning techniques to predict undergraduate majors, focusing on academic history and job market factors. By applying hyper-tuning, it outperforms previous research, with random forest achieving 97.70% accuracy and identifying key features like degree percentage and entry test results for program recommendations.…”

Section: IImentioning

confidence: 99%

Design and Implementation of Machine Learning Algorithms in Automatic Grading of Students' Assignments

Fang Xu

2024

jes

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This study introduces machine learning methods for automatically grading students' assignments, catering to the changing demands of education in the digital age. By utilizing diverse techniques, the aim is to simplify the grading process, give prompt feedback, and improve overall efficiency in assessment practices. The research delves into the different types of assignments such as written responses and coding tasks, and presents a range of ML algorithms including NB, Decision Trees (DT), RF, SVM, Linear Regression (LR), KNN, and Ensemble Methods (EMs). The implementation involves extracting relevant features from assignments, preprocessing data to handle noise and outliers, and training models on a varied set of examples. The grading system prioritizes interpretability, accuracy, and efficiency while aligning with educational objectives and grading policies. Evaluation is done using suitable metrics like recall, accuracy, precision, F1 score for each algorithm. The study contributes to the progress of automated grading systems by providing valuable insights for educators on the potential impact of machine learning in enhancing assessment processes. Additionally, the designed algorithms demonstrate adaptability for different types of assignments and serve as a foundation for future enhancements in grading precision and efficiency.

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“…Zayed et al [27] have developed a system that utilizes supervised machine learning techniques, including Decision Trees, Random Forests, and Support Vector Machines to predict the undergraduate majors of MBA students. They examined various input features, including the student's academic background and the job market, to ensure a high academic degree and employment prospects for students.…”

Section: The Use Of Machine Learning In Educational Counsellingmentioning

confidence: 99%

“…As part of the ongoing efforts to improve the efficacy of educational counseling, particularly in terms of career and academic guidance [27][28][29], a novel web-based counseling system has been designed and developed as a supportive resource to aid students in making informed decisions pertaining to their university and major preferences. The system is aimed to support and help high school students who are navigating college applications in selecting the most appropriate college or university [27] by considering each student's academic background and interests, as well as the requirements of their selected university.…”

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confidence: 99%

AI-Powered Academic Guidance and Counseling System Based on Student Profile and Interests

Majjate,

Bellarhmouch,

Jeghal

et al. 2023

ASI

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Over the past few decades, the education sector has achieved impressive advancements by incorporating Artificial Intelligence (AI) into the educational environment. Nevertheless, specific educational processes, particularly educational counseling, still depend on traditional procedures. The current method of conducting group sessions between counselors and students does not offer personalized assistance or individual attention, which can cause stress to students and make it difficult for them to make informed decisions about their coursework and career path. This paper proposes a counseling solution designed to aid high school seniors in selecting appropriate academic paths at the tertiary level. The system utilizes a predictive model that considers academic history and student preferences to determine students’ likelihood of admission to their chosen university and recommends similar alternative universities to provide more opportunities. We developed the model based on data from 500 graduates from 12 public high schools in Morocco, as well as eligibility criteria from 31 institutions and colleges. The counseling system comprises two modules: a recommendation module that uses popularity-based and content-based recommendations and a prediction module that calculates the likelihood of admission using the Huber Regressor model. This model outperformed 13 other machine learning modules, with a low MSE of 0.0017, RMSE of 0.0422, and the highest R-squared value of 0.9306. Finally, the system is accessible through a user-friendly web interface.

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A Recommendation System for Selecting the Appropriate Undergraduate Program at Higher Education Institutions Using Graduate Student Data

Cited by 13 publications

References 29 publications

Infant Cry Signal Diagnostic System Using Deep Learning and Fused Features

Infant Cry Signal Diagnostic System Using Deep Learning and Fused Features

Design and Implementation of Machine Learning Algorithms in Automatic Grading of Students' Assignments

AI-Powered Academic Guidance and Counseling System Based on Student Profile and Interests

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