Analysis of machine learning strategies for prediction of passing undergraduate admission test

Walid, Md. Abul Ala; Ahmed, S.M. Masum; Zeyad, Mohammad; Galib, S. M. Saklain; Nesa, Meherun

doi:10.1016/j.jjimei.2022.100111

Cited by 29 publications

(4 citation statements)

References 57 publications

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“…Machine learning has been used in prior work to predict admissions decisions for graduate programs [2]; undergraduate programs [3]; MBA programs [4]; and computer science undergraduate [5] and graduate [6,7] programs. One of these studies of graduate computer science admissions was quite extensive and considered 150, 000 computer science graduate admissions applications spanning 3000 institutions [7].…”

Section: Related Workmentioning

confidence: 99%

A machine learning approach to graduate admissions and the role of letters of recommendation

Zhao,

Chen,

Xue

et al. 2023

PLoS ONE

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The graduate admissions process is time-consuming, subjective, and complicated by the need to combine information from diverse data sources. Letters of recommendation (LORs) are particularly difficult to evaluate and it is unclear how much impact they have on admissions decisions. This study addresses these concerns by building machine learning models to predict admissions decisions for two STEM graduate programs, with a focus on examining the contribution of LORs in the decision-making process. We train our predictive models leveraging information extracted from structured application forms (e.g., undergraduate GPA, standardized test scores, etc.), applicants’ resumes, and LORs. A particular challenge in our study is the different modalities of application data (i.e., text vs. structured forms). To address this issue, we converted the textual LORs into features using a commercial natural language processing product and a manual rating process that we developed. By analyzing the predictive performance of the models using different subsets of features, we show that LORs alone provide only modest, but useful, predictive signals to admission decisions; the best model for predicting admissions decisions utilized both LOR and non-LOR data and achieved 89% accuracy. Our experiments demonstrate promising results in the utility of automated systems for assisting with graduate admission decisions. The findings confirm the value of LORs and the effectiveness of our feature engineering methods from LOR text. This study also assesses the significance of individual features using the SHAP method, thereby providing insight into key factors affecting graduate admission decisions.

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Section: Related Workmentioning

confidence: 99%

A machine learning approach to graduate admissions and the role of letters of recommendation

Zhao,

Chen,

Xue

et al. 2023

PLoS ONE

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“…In recent years, artificial intelligence (AI) has made people smarter by making it easier for them to process information, make decisions, and do tasks. In addition, it can help with medical diagnosis and treatment [1] as well as human development through education and training initiatives [2]. It can also be utilized to aid disabled people in various ways.…”

Section: Introductionmentioning

confidence: 99%

An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people

Kumer Paul,

Ala Walid,

Rani Paul

et al. 2024

Bulletin EEI

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Hand gestures and sign language are crucial modes of communication for deaf individuals. Since most people can't understand sign language, it's hard for a mute and an average person to talk to each other. Because of technological progress, computer vision and deep learning can now be used to count. This paper shows two ways to use deep knowledge to recognize sign language. These methods help regular people understand sign language and improve their communication. Based on American sign language (ASL), two separate datasets have been constructed; the first has 26 signs, and the other contains three significant symbols with the crucial sequence of frames or videos for regular communication. This study looks at three different models: the improved ResNet-based convolutional neural network (CNN), the long short-term memory (LSTM), and the gated recurrent unit (GRU). The first dataset is used to fit and assess the CNN model. With the adaptive moment estimation (Adam) optimizer, CNN obtains an accuracy of 89.07%. In contrast, the second dataset is given to LSTM and GRU and a comparison has been conducted. LSTM does better than GRU in all classes. LSTM has a 94.3% accuracy, while GRU only manages 79.3%. Our preliminary models' real-time performance is also highlighted.

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“…Our dataset has uneven distribution, which may cause the splitting strategy to accept an imbalance landmark in the training set. For the unevenly distributed dataset, the biases exhibited by the models may stem from a tendency to prefer a group with a larger population [17].…”

Section: Introductionmentioning

confidence: 99%

Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification

Walid,

Mollick,

Shill

et al. 2023

Diagnostics

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The study utilizes osteosarcoma hematoxylin and the Eosin-stained image dataset, which is unevenly dispersed, and it raises concerns about the potential impact on the overall performance and reliability of any analyses or models derived from the dataset. In this study, a deep-learning-based convolution neural network (CNN) and adapted heterogeneous ensemble-learning-based voting classifier have been proposed to classify osteosarcoma. The proposed methods can also resolve the issue and develop unbiased learning models by introducing an evenly distributed training dataset. Data augmentation is employed to boost the generalization abilities. Six different pre-trained CNN models, namely MobileNetV1, Mo-bileNetV2, ResNetV250, InceptionV2, EfficientNetV2B0, and NasNetMobile, are applied and evaluated in frozen and fine-tuned-based phases. In addition, a novel CNN model and adapted heterogeneous ensemble-learning-based voting classifier developed from the proposed CNN model, fine-tuned NasNetMobile model, and fine-tuned Efficient-NetV2B0 model are also introduced to classify osteosarcoma. The proposed CNN model outperforms other pre-trained models. The Kappa score obtained from the proposed CNN model is 93.09%. Notably, the proposed voting classifier attains the highest Kappa score of 96.50% and outperforms all other models. The findings of this study have practical implications in telemedicine, mobile healthcare systems, and as a supportive tool for medical professionals.

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Analysis of machine learning strategies for prediction of passing undergraduate admission test

Cited by 29 publications

References 57 publications

A machine learning approach to graduate admissions and the role of letters of recommendation

A machine learning approach to graduate admissions and the role of letters of recommendation

An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people

Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification

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