“Machine learning algorithms for predicting the graduation admission,”
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Another research direction approaches the admissions problem from the perspective of students by predicting the probability that an applicant will get admission at a particular university in order to help applicants better target universities based on their profiles as well as university rankings.
“Another research direction in this domain approaches the admissions problem from the perspective of students by predicting the probability that an applicant will get admission at a particular university in order to help applicants better target universities based on their profiles as well as university rankings (AlGhamdi et al., 2020; Goni et al., 2020; Mridha et al., 2022).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Mridha et al., 2022
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Geurts, P., Ernst, D., & Wehenkel, L. (2006) · Machine Learning · doi:10.1007/s10994-006-6226-1 · Cited by 9,300 · see citing works
Introduction to Machine Learning
Corso, I. A., Basili, R., & Motivazioni, W. (2010) · Kybernetes · doi:10.1108/k.2010.06739hae.001 · Cited by 1,196 · see citing works
Machine Learning Technique to Prognosis Diabetes Disease: Random Forest Classifier Approach
Palimkar, P., Shaw, R. N., & Ghosh, A. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2164-2_19 · Cited by 195 · see citing works
Self-citationGRADE: Machine‐Learning Support for Graduate Admissions
Waters, A., & Miikkulainen, R. P. (2014) · AI Magazine · doi:10.1609/aimag.v35i1.2504 · Cited by 94 · see citing works
Study and Prediction Analysis of the Employee Turnover using Machine Learning Approaches
Chakraborty, R., Mridha, K., Shaw, R. N., et al. (2021) · IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT) · doi:10.1109/gucon50781.2021.9573759 · Cited by 69 · see citing works
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Bodapati, S., Bandarupally, H., Shaw, R. N., & Ghosh, A. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-33-6919-1_4 · Cited by 64 · see citing works
Self-citationSingh, P., Bhardwaj, S., Dixit, S., et al. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-0749-3_59 · Cited by 48 · see citing works
Rawat, R., Mahor, V. K., Chirgaiya, S., et al. (2021) · Studies in computational intelligence · doi:10.1007/978-981-16-0407-2_9 · Cited by 48 · see citing works
Analysis and Prediction of COVID-19 Confirmed Cases Using Deep Learning Models: A Comparative Study
Sinha, T., Chowdhury, T., Shaw, R. N., & Ghosh, A. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2164-2_18 · Cited by 45 · see citing works
Self-citationA Comparative Study of Myocardial Infarction Detection from ECG Data Using Machine Learning
Chakraborty, A., Chatterjee, S., Majumder, K., et al. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2164-2_21 · Cited by 45 · see citing works
Rajawat, A. S., Rawat, R., Mahor, V. K., et al. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-0749-3_58 · Cited by 43 · see citing works
Unique Action Identifier by Using Magnetometer, Accelerometer and Gyroscope: KNN Approach
Palimkar, P., Bajaj, V., Mal, A. K., Shaw, R. N., & Ghosh, A. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2164-2_48 · Cited by 41 · see citing works
Self-citationAdvance machine learning and artificial intelligence applications in service robot
Das, S., Das, I., Shaw, R. N., et al. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-85498-6.00002-2 · Cited by 39 · see citing works
Self-citationBedi, P., Goyal, S. B., Rajawat, A. S., Shaw, R. N., & Ghosh, A. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2164-2_23 · Cited by 39 · see citing works
Self-citationAdaptive Fuzzy Logic Models for the Prediction of Compressive Strength of Sustainable Concrete
Garg, C., Namdeo, A., Singhal, A., et al. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2164-2_47 · Cited by 39 · see citing works
Efficient Deep Learning for Reforming Authentic Content Searching on Big Data
Rajawat, A. S., Barhanpurkar, K., Goyal, S. B., Bedi, P., Shaw, R. N., & Ghosh, A. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2164-2_26 · Cited by 32 · see citing works
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Goyal, S. B., Bedi, P., Rajawat, A. S., et al. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2164-2_25 · Cited by 29 · see citing works
Risk Detection in Wireless Body Sensor Networks for Health Monitoring Using Hybrid Deep Learning
Rajawat, A. S., Barhanpurkar, K., Shaw, R. N., & Ghosh, A. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-0749-3_54 · Cited by 28 · see citing works
Self-citationRajawat, A. S., Rawat, R., Barhanpurkar, K., et al. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-85498-6.00007-1 · Cited by 27 · see citing works
Smart Luminaires for Commercial Building by Application of Daylight Harvesting Systems
Goyal, S. B., Bedi, P., Rajawat, A. S., et al. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2164-2_24 · Cited by 26 · see citing works
Malsa, N., Vyas, V., Gautam, J., et al. (2021) · IEEE International Conference on Computing, Communication and Automation (ICCCA) · doi:10.1109/iccca52192.2021.9666311 · Cited by 22 · see citing works
Will I Get in? Modeling the Graduate Admission Process for American Universities
Gupta, N., Sawhney, A., & Roth, D. (2016) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2016.0095 · Cited by 20 · see citing works
A Statistical Approach to Graduate Admissions’ Chance Prediction
Chakrabarty, N., Chowdhury, S., & Rana, S. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-2043-3_38 · Cited by 18 · see citing works
Combating Challenges in the Construction Industry with Blockchain Technology
Singh, P., Sammanit, D., Krishnan, P., et al. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-0749-3_56 · Cited by 14 · see citing works
A quality based automated admission system for educational domain
Mishra, S., Sahoo, S., Mishra, B. K., et al. (2016) · International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) · doi:10.1109/scopes.2016.7955824 · Cited by 11 · see citing works
Temporal Features and Machine learning Approaches to Study Brain Activity with EEG and ECG
Mridha, K., Kumar, D., Shukla, M., et al. (2021) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite51222.2021.9404739 · Cited by 7 · see citing works
Self-citationIntelligent Based Waste Management Awareness Developed by Transfer Learning
Mridha, K., Shaw, R. N., & Ghosh, A. (2021) · IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT) · doi:10.1109/gucon50781.2021.9573586 · Cited by 7 · see citing works
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Proactive and reactive engagement of artificial intelligence methods for education: a review
Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139 · see citing works
- Authors
- Mridha, K., Jha, S., Shah, B., Damodharan, P., Ghosh, A., & Shaw, R. N.
- Affiliations
- Marwadi University (IN)
- The Neotia University (IN)
- Galgotias University (IN)
- Corresponding author
- Mridha, Krishna
- Venue
- Lecture notes in electrical engineering
- Publisher
- Springer Singapore
- Published
- 2022
- Pages
- 618-637
- ISSN
- 1876-1100 (ISSN-L)
- 1876-1119
- OpenAlex
- W4225590484
- Cited by
- 3
- References
- 27
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1
- FWCI
- 0.98
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in electrical engineering (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Medical Education and Admissions
- Keywords
- American universities, gradient echo, TOEFL, statement of purpose, letters of recommendation, machine learning prediction models
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