Abstract—Education plays a significant role in individuals’
development and the economic growth of developing countries
like India. Dropout of students from their studies is the major
concern for any order of education. Some models for predicting
the dropout of students are developed with several factors.
Many of them lacked consistency as they backed their studies
with the academic performance of the students. Especially, for
those students who suffered from physical impairment, the
dropout depends on several external factors. Hence, this work
proposes a novel HFIPO-DPNN to predict the student dropout
rooted in the previous semester’s marks. The proposed model
enclosed the hybrid firefly and improved particle swarm
algorithm to optimize the feature selection that influences the
dropout of hearing-impaired students. The optimized feature
data are used to predict the dropout with the novel DPNN. The
optimized data was split and used for training the DPNN. The
testing data is used to evaluate the performance of the proposed
framework. The attributes used for predicting the student
dropout are Family Size, Subject, Medium of Instruction, and
so on. The data must be collected from 250 physically impaired
children belonging to ITI institute, Bangalore. The outcome of
the proposed framework is evaluated on several metrics. The
accuracy of the proposed model is about 99.02%. The
HFIPO-DPNN framework can be enhanced for predicting the
dropout for students with other disabilities. The optimization
showed that factors influencing education other than familial
factors are to be considered in the prediction of dropout.
Index Terms—Education, dropout, physically impaired, feature selection
Marina. B is with Alagappa University, Karaikudi, Tamil Nadu, India and with St. Anne‘s Degree College for Women, Bangalore, India.
A. Senthilrajan is with Department of Computational Logistics, Alagappa University, Karaikudi, Tamil Nadu, India.
*Correspondence: email@example.com (M.B.)
Cite: Marina. B* and A. Senthilrajan, "HFIPO-DPNN: A Framework for Predicting the Dropout of Physically Impaired Student from Education," International Journal of Information and Education Technology vol. 13, no. 4, pp. 696-703, 2023.Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).