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The analysis of influential machine parameters can be useful to plan and design a plastic injection molding process. However, current research in parameter analysis is mostly based on computer-aided engineering (CAE) or simulation which have been demonstrated to be inadequate for analyzing complex behavioral changes in the real injection molding process. More advanced approaches using machine learning technology specifically with artificial neural networks (ANNs) brought promising results in terms of prediction accuracy. Nevertheless, the black box and distributed representation of ANN prevent humans from gaining an insight into which process parameters give a significant influence on the final prediction output. Therefore, in this paper, we develop a simpler ANN model by using structural learning with forgetting (SLF) as the algorithm for the training process. Instead of typical backpropagation which generated a fully connected layer of the ANN model, SLF only reveals the important neurons and connections. Hence, the training process of SLF leaves only influential connections and neurons. Since each of the neurons specifically on the input layer represent each of the injection molding parameters, the ANN-SLF model can be further investigated to determine the influential process parameters. By applying SLF to the ANN training process, this experiment has successfully extracted a set of significant injection molding process parameters. [ABSTRACT FROM AUTHOR]

E-textbooks are becoming increasingly important in the learning and teaching environments as the globe shifts to online learning. The key topic is what elements influence students' behavioral desire to use e-textbooks, and how the whole operation affects academic achievement when using e-textbooks. This research aims to investigate the various factors that influence the behavioral intention to use an e-textbook, which in turn influences academic achievement in a bilingual academic environment. The research model was empirically validated using survey data from 625 e-textbook users from bilingual academic institutes from Jordan. Structural equation modeling (SEM) analysis was employed to test the research hypotheses by using Amos 20. To validate the results, artificial intelligence (AI) was employed via five machine learning (ML) techniques: artificial neural network (ANN), linear regression, and sequential minimal optimization algorithm for support vector machine (SMO), bagging with REFTree model, and random forest. The empirical results offer several key findings. First, the behavioral intention of using an e-textbook positively influences academic achievement. Second, attitude toward e-textbooks, subjective norms toward e-textbooks, and perceived behavior control toward e-textbooks positively influence behavioral intention toward using e-textbooks. Attitude toward using e-textbooks and perceived behavioral control both are positively influenced by independent factors. This study contributes to the literature by theorizing and empirically testing the impacts of e-textbooks on the academic achievement of university students in a bilingual environment in Jordan. [ABSTRACT FROM AUTHOR]