@article{scholars12146,
         journal = {International Journal of Advanced Computer Science and Applications},
       publisher = {Science and Information Organization},
           pages = {422--425},
            year = {2019},
           title = {Accuracy performance degradation in image classification models due to concept drift},
          number = {5},
          volume = {10},
            note = {cited By 12},
             doi = {10.14569/ijacsa.2019.0100552},
        abstract = {Big data is playing a significant role in the current computing revolution. Industries and organizations are utilizing their insights for Business Intelligence by using Deep Learning Networks (DLN). However, dynamic characteristics of BD introduce many critical issues for DLN; Concept Drift (CD) is one of them. CD issue appears frequently in Online Supervised Learning environments in which data trends change over time. The problem may even worsen in a BD environment due to the veracity and variability factors. The CD issue may render the DLN inapplicable by degrading the accuracy of classification results in DLN which is a very serious issue that needs to be addressed. Therefore, these DLN need to quickly adapt to changes for maintaining the accuracy level of the results. To overcome classification accuracy, we need some dynamical changes in the existing DLN. Therefore, in this paper, we examine some of the existing Shallow Learning and Deep Learning models and their behavior before and after the Concept Drift (in experiment 1) and validate the pre-trained Deep Learning network (ResNet-50). In future work, this experiment will examine the most recent pre-trained DLN (Alex Net, VGG16, VGG19) and identify their suitability to overcome Concept Drift using fine-tuning and transfer learning approaches. {\^A}{\copyright} 2018 The Science and Information (SAI) Organization Limited.},
        keywords = {Deep learning; Image classification, Classification models; Concept drifts; Current computing; Deep learning; Fine tuning; Images classification; Learning network; Performance degradation; Pre-trained network; Transfer learning, Computer aided instruction},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066761486&doi=10.14569\%2fijacsa.2019.0100552&partnerID=40&md5=24f60a2a9383e88df6d0416db7c0ad11},
            issn = {2158107X},
          author = {Hashmani, M. A. and Jameel, S. M. and Alhussain, H. and Rehman, M. and Budiman, A.}
}