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A Recommender System for Predicting Students' Admission to a Graduate Program using Machine Learning Algorithms
2021
International Journal of Online and Biomedical Engineering (iJOE)
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For that reason, the main purpose of this research work is to provide a recommender system for early predicting university admission based on four Machine Learning algorithms namely Linear Regression, ...
The experimental results showed that the Random Forest Regression is the most suitable Machine Learning algorithm for predicting university admission. ...
Zhao et al [10] explained their study based on a quantitative Machine Learning approach to predict master students' admission in professional institutions. ...
doi:10.3991/ijoe.v17i02.20049
fatcat:gkwwdgdotrbinhjb3krjip6pya
International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2020)
[chapter]
2020
Lecture Notes in Computer Science
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Being able to measure, characterize, and mitigate these biases while keeping high effectiveness is a topic of central interest for the information retrieval community. ...
In this workshop, we aim to collect novel contributions in this emerging field and to provide a common ground for interested researchers and practitioners. ...
His research interests focus on Data Mining and Machine Learning approaches, mostly applied to recommender systems and social media analysis. ...
doi:10.1007/978-3-030-45442-5_84
fatcat:d3qzkywr5zhsfpfrfy24vcrwru
DeepVS: An Efficient and Generic Approach for Source Code Modeling Usage
2020
Electronics Letters
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. † The quantitative evaluation with ten real-world open-source software systems and qualitative analysis of DeepVS tool verifies that the projected method is accurate and subordinate the existing approaches ...
The proposed tool is capable of providing source code suggestions instantly in an IDE by using pre-trained BiGRU neural net. The evaluation of this work is two-fold, quantitative and qualitative. ...
We summarise the benefits of our proposed framework and the DeepVS tool as follows: † To the best of our knowledge, the proposed approach is the first one that enables the usage of machine/deep learning-based ...
doi:10.1049/el.2020.0500
fatcat:cersfbsgifbszf6vs3u4d75lni
Using artificial intelligence in medical school admissions screening to decrease inter- and intra-observer variability
2023
JAMIA Open
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By using a combined human and AI evaluation process, the accuracy of the process was demonstrated to be 96% on the "real-world" evaluation with a negative predictive value of 0.97. ...
Discussion and Conclusion These results demonstrate the feasibility of an AI approach applied to medical school admissions screening decision-making. ...
SHapley Additive exPlanation (SHAP) values were introduced by Lundberg and Lee 12 in 2017 as an approach to explaining the output of a machine learning model. ...
doi:10.1093/jamiaopen/ooad011
pmid:36819893
pmcid:PMC9936956
fatcat:g36jpfqgnjaoxp3st4p5ywwnta
Subject index
2006
JAMIA Journal of the American Medical Informatics Association
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Diagnosis automated coding using example-based classification vs. machine learning filters, 516 -525 electronic, accuracy in acute infections in primary care, 61-66 Dictionary BioThesaurus, quantitative ...
Dentists
general, in the United States, adoption of clinical com-
puting among, 344 -352
Developing countries
pragmatic approach to constructing a minimum data
set for care of HIV patients in, 253- ...
accuracy of identification using automatic text categorization of x-ray reports, 696 -698 X XML-based data management system for synthesis of data from disparate databases, 289 -301 X-ray reports accuracy ...
doi:10.1197/s1067-5027(06)00177-0
fatcat:4szekiclqzfvrcndhtrmvjp62u
VizML: A Machine Learning Approach to Visualization Recommendation
[article]
2018
arXiv
pre-print
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Here, we demonstrate a novel machine learning-based approach to visualization recommendation that learns visualization design choices from a large corpus of datasets and associated visualizations. ...
To evaluate the generalizability and uncertainty of our approach, we benchmark with a crowdsourced test set, and show that the performance of our model is comparable to human performance when predicting ...
To conclude, we discuss interpretations, limitations, and extensions of our initial machine learning approach to visualization recommendation. ...
arXiv:1808.04819v1
fatcat:iovb37lnvfc4xbzdrydltvdz4y
DARPA 's Explainable AI ( XAI ) program: A retrospective
2021
Applied AI Letters
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In August 2016, DARPA released DARPA-BAA-16-53 to call for proposals. | XAI program goals The stated goal of explainable artificial intelligence (XAI) was to create a suite of new or modified machine learning ...
learn more interpretable models, such as Bayesian Rule Lists. 12 Others were developing model-agnostic techniques that could experiment with a machine learning model-as a black box-to infer an approximate ...
The program hoped to create a portfolio of new machine learning and explanation techniques to provide future practitioners with a wider range of design options covering the performance-explainability trade ...
doi:10.1002/ail2.61
fatcat:6ye5obelonct3pupm36rdf2e4i
An Intelligent Data Analysis for Recommendation Systems Using Machine Learning
2019
Scientific Programming
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The collaborative filtering (CF) approach is one of the most popular techniques of the RS to generate recommendations. ...
We have proposed a novel CF recommendation approach in which opinion-based sentiment analysis is used to achieve hotel feature matrix by polarity identification. ...
Within the field of data analytics, machine learning is part of a piece known as predictive analytics. ...
doi:10.1155/2019/5941096
fatcat:imsm2f7mevaf7fb2shikhprfwe
Interactive and Iterative Annotation for Biomedical Entity Recognition
[chapter]
2015
Lecture Notes in Computer Science
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In this paper, we demonstrate the impact of interactive machine learning for the development of a biomedical entity recognition dataset using a human-into-the-loop approach: during annotation, a machine ...
The experiments validate our method qualitatively and quantitatively, and give rise to a more personalized, responsive information extraction technology. ...
Experiment and Evaluation Simulating Interactive Learning: In order to prove that interactive machine learning can yield a quality-annotated data set in a short training loop, we conduct our first experiment ...
doi:10.1007/978-3-319-23344-4_34
fatcat:chjxvtnlxvdftikeldk5icppsa
A Comparison of Classification Models in Predicting Graduate Admission Decision
2021
Journal of Higher Education Theory and Practice
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The study also proposes a framework that incorporates machine learning-based classification into the admissions decision process. ...
Prospective students applying to graduate programs experience a real predicament of selecting the right schools to invest limited resources for the application. ...
This research paper also proposes a machine learning-based decision framework for higher education institutes using machine learning approaches. ...
doi:10.33423/jhetp.v21i7.4498
fatcat:nh4qiotfkva6neea3y4b5oyso4
ML-based Visualization Recommendation: Learning to Recommend Visualizations from Data
[article]
2020
arXiv
pre-print
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We also describe an evaluation framework to quantitatively evaluate visualization recommendation models learned from a large corpus of visualizations and datasets. ...
In this work, we propose the first end-to-end ML-based visualization recommendation system that takes as input a large corpus of datasets and visualizations, learns a model based on this data. ...
visualization recommendation systems that leverage machine learning. ...
arXiv:2009.12316v1
fatcat:k3rr5muny5hn3kvrehglnplgra
A Comparative Study of Feature Types for Age-Based Text Classification
[article]
2020
arXiv
pre-print
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The results obtained show that the features describing the text at the document level can significantly increase the quality of machine learning models. ...
The ability to automatically determine the age audience of a novel provides many opportunities for the development of information retrieval tools. ...
The results showed that the use of these attributes in digital libraries and recommendation systems could significantly improve the quality of machine learning approaches. ...
arXiv:2009.11898v1
fatcat:5c265snymnganelawre2k32inu
GRADE: Machine Learning Support for Graduate Admissions
2014
The AI Magazine
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This article describes GRADE, a statistical machine learning system developed to support the work of the graduate admissions committee at the University of Texas at Austin Department of Computer Science ...
In recent years, the number of applications to the UTCS PhD program has become too large to manage with a traditional review process. ...
Acknowledgements Thanks to John Chambers and Patti Spencer for their technical assistance, Bruce Porter for financial support, and Peter Stone, Raymond Mooney, and the admissions committees of 2012 and ...
doi:10.1609/aimag.v35i1.2504
fatcat:u3khlwlrvrhr7lir4q76ph6ygy
'Is it worth doing?' Measuring the impact of patient and public involvement in research
2015
Research Involvement and Engagement
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This review reflects on the use of quantitative approaches to evaluating impact. ...
On this basis, researchers' accounts of their experience potentially provide a source of insight and learning to influence others, in the same way that the patient experience helps to shape research. ...
Acknowledgements I would like to thank the staff at INVOLVE, especially Helen Hayes, for all their work in developing The Evidence Library. ...
doi:10.1186/s40900-015-0008-5
pmid:29062495
pmcid:PMC5598089
fatcat:rh66tkdnbfhzdljfqa2nvb5rqm
CONTEXT AWARE EXTRACTION OF CONCEPTS FROM UNSTRUCTURED DATA USING MACHINE LEARNING ALGORITHMS
2023
Zenodo
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Recently, Machine Learning (ML) approaches offer a novel chance to emerge unstructured data into existing knowledge bases without the requirement to manually organize the data into topic-based content ...
Hence in this work, Context aware extraction of concepts from unstructured data using Machine Learning algorithms is presented. ...
A framework for clustering metadata describes CUIL (Classification of Unstructured Data), that clusters metadata, gives a name to every cluster, and later makes a system utilizing an Extreme Learning Machine ...
doi:10.5281/zenodo.7572604
fatcat:hzfob67axnesfg5o5dbpcldlyy
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