Transactions on Engineering and Computing Sciences <p>Transactions on Engineering and Computing Sciences is peer-reviewed open access online journal that provides a medium of the rapid publication of original research papers, review articles, book reviews and short communications covering all areas of machine learning and artificial Intelligence. The journal publishes state-of-the-art research reports and critical evaluations of applications, techniques and algorithms in Engineering Management, Cloud Systems, Electrical Engineering, Industrial Networks and Intelligent Systems, Mechanical Civil and Chemiical Engineering, Internet of Things, Mathematical Modeling, Robotics Research, Engineering informatics, Computer Science, Computer Hardware/Software, Robotics and application, Embedded Systems, Data Base Management &amp; Information Retrievals, Geographical Information Systems/ Global Navigation Satellite Systems, Fuzzy Systems, Web and Internet computing, Machine learning, Artificial intelligence, Cognitive science, Software engineering, Database systems, Soft computing, Optimization and modelling and related application areas.</p> Services for Science and Education, United Kingdom en-US Transactions on Engineering and Computing Sciences 2054-7390 Quadratic Stability of Switched a ne DAEs Via State-Dependent Switching Law <p>In this paper, we deal with quadratic stabilization for a particular class of impulse-free switched differential affine algebraic systems (switched affine DAEs) which consisted of a finite set of affine subsystems with the same algebraic constraint, where both subsystem matrices and affine vectors in the vector fields are switched independently and no single subsystem has desired quadratic stability. We show that if a convex combination of subsystem matrices such that the result LTI algebraic system is assymptotically stable, and another convex combination of affine vectors is zero, then we can design a state-dependent switching law such that the entire switched affine DAEs system is quadratically stable at the origin. But when the convex combination of affine vectors is not zero, we discuss the quadratic stabilization to a convergence set defined by the convex combination of subsystem matrices and the convex combination of affine vectors.</p> <p><br><br></p> <p>&nbsp;</p> Marie-Louise Dossou-Yovo Guy Degla Copyright (c) 2023 Marie-Louise Dossou-Yovo, Guy Degla 2023-05-13 2023-05-13 11 3 1 9 10.14738/tecs.113.14574 An Exploration of Prediction Accuracy of Developmental Degrees of Music Expression in Early Childhood Based on A Simultaneous Analysis of Both Eye Movement and Full-Body Movement <p>This article explores validity to introduce eye movement of children in association with body movement as feature quantities of machine learning, in particular, to assess children’s maturity level of musical expression. There is a growing awareness of applying machine learning technique to yield practical benefit on predicting developmental degrees of music education by observing body movements with motion capture technology. From professional teaching perspective, it is believed only experienced teachers can evaluate children’s music achievement because coordinated functioning of body movements need carefully be observed in connection with various music factor such as rhythm, beat strength, tones, etc. If new teachers can take advantage of having objective results of evaluation of children’s expression level by machines, it will enhance educational efficiency to certain levels which experts could attain. The author aims to improve classification accuracy of machine learning by focusing on eye movements data simultaneously recorded with body movements. In this study, children (n=43) at two child facilities participated in the data capture of both eye movement and body movement during musical expression. Feature quantities were extracted from the results of a three-way of ANOVA, and applied to machine learning to improve the classification accuracy of developmental degree in musical expression. Specifically, when using several classifiers such as NN (Neural Network model), the classification accuracy of developmental degree of musical development was more precisely in the case of both body movement data and eye movement data included in feature quantities than in the case of only body movement data as feature quantities.</p> Mina Sano Copyright (c) 2023 Mina Sano 2023-05-21 2023-05-21 11 3 10 25 10.14738/tecs.113.14682