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@article{154164,
        author = {Neethu C Sekhar and Merlin Sebastian  and Nandita Suresh  and Leoyon Reji and Shahad C K},
        title = {WHAT’S NEXT? Prediction Model for Students Future Development},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {7},
        pages = {7-11},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154164},
        abstract = {Categorizing and  predicting  the  future  of students based on their  exam performance are typical challenges for edu- cators.  Therefore,  proper and accurate  method  is very important to give timely advice to students  regarding choice of program and university.  Previously,  traditional data  mining  methods,  such as decision trees and  association  rules,  were used for classification. The rapid  development of artificial intelligence and deep learning algorithms  in recent years offers a different  method for intelligent classification and prediction  of results. Tensorflow’s artificial intelligence  engine  helps  to  classify students’  performance and predict  their  future  university.  In our  system, the deep learning model   analyzes   subjects   like   physics,   mathematics,   English, chem, bio, history and Malayalam,  as well as non academic achievements   like  driving,  sports,  and  art.  With  a  dataset   of 2500 students,  80 per  cent of that  data  is used as training data and 20 per cent is used as test data,  accuracy  ranges  from 97 per cent to 99 per cent. The optimal  configuration  of the Tensorflow deep  learning  model  to achieve  the  best  prediction  accuracy  is determined.
},
        keywords = {Artificial Intelligence, Deep Learning, future prediction,  Convolution  Neural  Network,  Recurrent Neural  Net- work, Tensor flow, prediction modelling.},
        month = {},
        }