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A Smart Mobile Application for Complaints in Mauritius

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Information Systems Design and Intelligent Applications

Abstract

Receiving and managing complaints effectively are important for organisations which aim to provide excellent customer service. In order for this to happen, organisations should make it quick and easy for users to report issues. In this paper, a smart mobile application for complaints management in Mauritius is described. Users of this mobile application can report issues for different organisations using a single application on their smartphones. They can register complaints using text, images or videos, and they do not have to specify which authority the complaint is directed to. Instead, the application uses text and image analysis alongside a Convolutional Neural Network (CNN) in order to direct complaints to the correct utility organisations. The classifiers have been trained to identify different categories of complaints for each local utility organisation. Users are notified regarding the status of their complaints and can use the application to directly communicate with the personnel.

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Notes

  1. 1.

    https://www.csu.mu.

  2. 2.

    https://play.google.com/store/apps/details?id=com.wearemobimove.ecitizen&hl=en.

  3. 3.

    https://www.tensorflow.org.

  4. 4.

    http://www.image-net.org.

  5. 5.

    https://github.com/tensorflow/hub/blob/master/examples/image_retraining/retrain.py.

  6. 6.

    https://www.tensorflow.org/tutorials/image_retraining.

  7. 7.

    https://github.com/thisbejim/Pyrebase/.

  8. 8.

    https://github.com/llSourcell/tensorflow_image_classifier/blob/master/src/label_image.py.

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Correspondence to Nuzhah Gooda Sahib-Kaudeer .

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Greedharry, M., Seewoogobin, V., Gooda Sahib-Kaudeer, N. (2019). A Smart Mobile Application for Complaints in Mauritius. In: Satapathy, S., Bhateja, V., Somanah, R., Yang, XS., Senkerik, R. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 863. Springer, Singapore. https://doi.org/10.1007/978-981-13-3338-5_32

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