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[2]. Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E. (2022). Visiting Indian Hospitals Before, During and After COVID. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems.
[3]. Kogilavani, S. V., Sathishkumar, V. E., & Subramanian, M. (2022, May). AI Powered COVID-19 Detection System using Non-Contact Sensing Technology and Deep Learning Techniques. In 2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 400-403). IEEE.
[4]. Subramanian, M., Sathishkumar, V. E., Ramya, C., Kogilavani, S. V., & Deepti, R. (2022, May). A Lightweight Depthwise Separable Convolution Neural Network for Screening Covid-19 Infection from Chest CT and X-ray Images. In 2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 410-413). IEEE.
[5]. Subramanian, M., Lv, N. P., & VE, S. (2022). Hyperparameter optimization for transfer learning of VGG16 for disease identification in corn leaves using Bayesian optimization. Big Data, 10(3), 215-229.
[6]. Sathishkumar, V. E., & Cho, Y. (2019, December). Cardiovascular disease analysis and risk assessment using correlation based intelligent system. In Basic & clinical pharmacology & toxicology (Vol. 125, pp. 61-61). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.
[7]. Balakrishnan J M D 2010 Significance of classification techniques in prediction of learning disabilities arXiv preprint arXiv:1011.0628
[8]. David J M & Balakrishnan K 2010 Machine learning approach for prediction of learning disabilities in school-age children International Journal of Computer Applications, 9(11), pp 7-14
[9]. Julie M D & Kannan B 2010 Prediction of learning disabilities in school age children using decision tree. In Recent Trends in Networks and Communications Springer, Berlin, Heidelberg, pp 533-542
[10]. David J M & Balakrishnan K 2011 Prediction of key symptoms of Learning Disabilities in school-age ChildrenUsing rough sets. International Journal of Computer and Electrical Engineering, 3(1), p 163
[11]. David J M & Balakrishnan K 2013 Performance improvement of fuzzy and neuro fuzzy systems: prediction of learning disabilities in school-age children. International Journal of Intelligent Systems and Applications, 5(12), p 34
[12]. Ambili K & Afsar P 2016 A framework for learning disability prediction in school children using artificial neural network International Journal of Advanced Research in Science, Engineering and Technology, 3(6).
[13]. Ambili K & Afsar P 2016 A framework for learning disability prediction in school children using naïve Bayes-neural network fusion technique J Inf Knowl Res Comput Eng, 4(01).
[14]. Rello L Williams K Ali A White N C & Bigham J P 2016 Dytective: towards detecting dyslexia across languages using an online game. In Proceedings of the 13th International Web for All Conference pp. 1-4
[15]. Rello L Baeza-Yates R Ali A Bigham J P & Serra M 2020. Predicting risk of dyslexia with an online gamified test. Plos one, 15(12), e0241687.
[16]. Kaisar S & Chowdhury A 2022. Integrating oversampling and ensemble-based machine learning techniques for an imbalanced dataset in dyslexia screening tests. ICT Express.
[17]. Rauschenberger M Baeza-Yates R & Rello L 2020 Screening risk of dyslexia through a web-game using language-independent content and machine learning. In Proceedings of the 17th International Web for All Conference pp. 1-12.
[18]. Sathishkumar, V. E., Park, J., & Cho, Y. (2020). Using data mining techniques for bike sharing demand prediction in metropolitan city. Computer Communications, 153, 353-366.
[19]. VE, S., & Cho, Y. (2020). A rule-based model for Seoul Bike sharing demand prediction using weather data. European Journal of Remote Sensing, 53(sup1), 166-183.
[20]. Krishnamoorthy, N., Prasad, L. N., Kumar, C. P., Subedi, B., Abraha, H. B., & Sathishkumar, V. E. (2021). Rice leaf diseases prediction using deep neural networks with transfer learning. Environmental Research, 198, 111275.
Cite this article
Santhiya,S.;KanimozhiSelvi,C.S. (2023). A study on dyslexia detection using machine learning techniques for checklist, questionnaire and online game based datasets . Applied and Computational Engineering,5,837-842.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Shanmugavadivel, K., Sathishkumar, V. E., Kumar, M. S., Maheshwari, V., Prabhu, J., & Allayear, S. M. (2022). Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection in ECG Images. Computational
[2]. Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E. (2022). Visiting Indian Hospitals Before, During and After COVID. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems.
[3]. Kogilavani, S. V., Sathishkumar, V. E., & Subramanian, M. (2022, May). AI Powered COVID-19 Detection System using Non-Contact Sensing Technology and Deep Learning Techniques. In 2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 400-403). IEEE.
[4]. Subramanian, M., Sathishkumar, V. E., Ramya, C., Kogilavani, S. V., & Deepti, R. (2022, May). A Lightweight Depthwise Separable Convolution Neural Network for Screening Covid-19 Infection from Chest CT and X-ray Images. In 2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 410-413). IEEE.
[5]. Subramanian, M., Lv, N. P., & VE, S. (2022). Hyperparameter optimization for transfer learning of VGG16 for disease identification in corn leaves using Bayesian optimization. Big Data, 10(3), 215-229.
[6]. Sathishkumar, V. E., & Cho, Y. (2019, December). Cardiovascular disease analysis and risk assessment using correlation based intelligent system. In Basic & clinical pharmacology & toxicology (Vol. 125, pp. 61-61). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.
[7]. Balakrishnan J M D 2010 Significance of classification techniques in prediction of learning disabilities arXiv preprint arXiv:1011.0628
[8]. David J M & Balakrishnan K 2010 Machine learning approach for prediction of learning disabilities in school-age children International Journal of Computer Applications, 9(11), pp 7-14
[9]. Julie M D & Kannan B 2010 Prediction of learning disabilities in school age children using decision tree. In Recent Trends in Networks and Communications Springer, Berlin, Heidelberg, pp 533-542
[10]. David J M & Balakrishnan K 2011 Prediction of key symptoms of Learning Disabilities in school-age ChildrenUsing rough sets. International Journal of Computer and Electrical Engineering, 3(1), p 163
[11]. David J M & Balakrishnan K 2013 Performance improvement of fuzzy and neuro fuzzy systems: prediction of learning disabilities in school-age children. International Journal of Intelligent Systems and Applications, 5(12), p 34
[12]. Ambili K & Afsar P 2016 A framework for learning disability prediction in school children using artificial neural network International Journal of Advanced Research in Science, Engineering and Technology, 3(6).
[13]. Ambili K & Afsar P 2016 A framework for learning disability prediction in school children using naïve Bayes-neural network fusion technique J Inf Knowl Res Comput Eng, 4(01).
[14]. Rello L Williams K Ali A White N C & Bigham J P 2016 Dytective: towards detecting dyslexia across languages using an online game. In Proceedings of the 13th International Web for All Conference pp. 1-4
[15]. Rello L Baeza-Yates R Ali A Bigham J P & Serra M 2020. Predicting risk of dyslexia with an online gamified test. Plos one, 15(12), e0241687.
[16]. Kaisar S & Chowdhury A 2022. Integrating oversampling and ensemble-based machine learning techniques for an imbalanced dataset in dyslexia screening tests. ICT Express.
[17]. Rauschenberger M Baeza-Yates R & Rello L 2020 Screening risk of dyslexia through a web-game using language-independent content and machine learning. In Proceedings of the 17th International Web for All Conference pp. 1-12.
[18]. Sathishkumar, V. E., Park, J., & Cho, Y. (2020). Using data mining techniques for bike sharing demand prediction in metropolitan city. Computer Communications, 153, 353-366.
[19]. VE, S., & Cho, Y. (2020). A rule-based model for Seoul Bike sharing demand prediction using weather data. European Journal of Remote Sensing, 53(sup1), 166-183.
[20]. Krishnamoorthy, N., Prasad, L. N., Kumar, C. P., Subedi, B., Abraha, H. B., & Sathishkumar, V. E. (2021). Rice leaf diseases prediction using deep neural networks with transfer learning. Environmental Research, 198, 111275.