Dyslexia is among the most common neurological disorders in children. Detection of dyslexia therefore remains an important pursuit for the research works across various domains which is illustrated by the plethora of work presented in diverse scientific articles. The work presented herein attempted to utilize the potential of a unified gaming test of subjects (dyslexia/controls) in tandem with principal components derived from data to detect dyslexia. The work aims to build a machine learning model for dyslexia detection using comprehensive gaming test data. We have attempted to explore the potential of various kernel functions of the Support Vector Machine (SVM) on different number of principal components to reduce the computational complexity. A detection accuracy of 92% is obtained from the radial basis function with 5 components, and the highest detection accuracy obtained from the radial basis function with 3 components is 93%. On the contrary, the Artificial Neural Network(ANN) shows an added advantage with minimal number of hyperparameters with 3 components for obtaining an accuracy of 95%. The comparison of the proposed method with some of the existing works shows efficacy of this method for dyslexia detection.
Requirements elicitation (RE) phase is very critical and crucial to the success of IS projects in telecommunication sector. Unfortunately, this phase of IS projects development is susceptible to a large degree of errors, affected by key factors embedded in the applied communication techniques, which results in a requirement conflicts that widen the gap of what is being build and what is being desired by the stakeholders. The aim of this paper is to present a Requirements conflict resolution and communication model to address the lack of a systematic mechanism to quantify the communication obstacles and to classify the requirements conflicts in the RE phase. The proposed model will introduce the conflict detection and resolution mechanism based on the normalized cross correlations function (NCCF), standard deviation (SD) and the standard error (SE) functions to detect and quantify the conflicted requirements based on the calculation of the requirements correlations and accuracy. The proposed model will overcome the reoccurring issues of the requirement elicitation phase.
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