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The inputs to the outputs of nonlinear systems can be modeled using machine and deep learning approaches, among which artificial neural networks (ANNs) are a promising option. However, noisy signals affect ANN modeling negatively; hence, it is important to investigate these signals prior to the modeling. Herein, two customized and simple approaches, visual inspection and absolute correlation, are proposed to examine the relationship between the inputs and outputs of a nonlinear system. The system under consideration uses biosignals from surface electromyography as inputs and human finger joint angles as outputs, acquired from eight intact participants performing movements and grasping tasks in dynamic conditions. Furthermore, the results of these approaches are tested using the standard mutual information measure. Hence, the system dimensionality is reduced, and the ANN learning (convergence) is accelerated, where the most informative inputs are selected for the next phase. Subsequently, four ANN types, i.e., feedforward, cascade-forward, radial basis function, and generalized regression ANNs, are used to perform the modeling. Finally, the performance of the ANNs is compared with findings from the signal analysis. Results indicate a high level of consistency among all the aforementioned signal pre-analysis techniques from one side, and they also indicate that these techniques match the ANN performances from the other side. As an example, for a certain movement set, the ANN models resulted in the rotation estimation accuracy of the joints in the following descending order: carpometacarpal, metacarpophalangeal, proximal interphalangeal, and distal interphalangeal. This information has been indicated in the signal pre-analysis step. Therefore, this step is crucial in input–output variable selections prior to machine-/deep-learning-based modeling approaches.

This paper seeks to gain insights into complex and multiple influences which may behind the different components of intimate partner violence (IPV) against Jordanian wives. Drawing on a quantitative approach, the paper explores and presents findings of the determinants of domestic violence sustained by female partners during the year preceding a nationally representative survey. The survey is based on national multi-stage random sampling data from the 2012 Jordan Demographic Health Survey (JDHS). The paper applies the social-ecological framework. A method of Leastwise deletion techniques is used to remove missing data. Doing this resulted in 6213 married women used in the data analysis. Logistic regression models are used to estimate/predict different forms of IPV against domestic female partners. The findings of the study suggest that wives are victimized at all levels of the framework. Specifically, family, community, and social levels were the most vital factors affecting victims experiencing IPV. More specifically, wives witnessing their fathers beat their mothers and wives who are scared of their husbands are more prone to sustain violence inflicted by their partners. Furthermore, wives' education, working status, and age at marriage do not predict IPV. Another key finding is that wife empowerment in family relationships is a protective factor against domestic violence against her. The implication is that the patriarchal explanation of domestic violence against wives is valid in the Jordanian cultural context. This study underscores the need to reevaluate the effectiveness of Jordan's general human development programs and women empowerment programs as an essential measure for alleviating IPV inflicted on married women in Jordan.