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ANN might be used to improve psychiatric diagnosis.

With the development of the Internet of Things (IoT) and the widespread use of electric vehicles (EV), vehicle-to-grid (V2G) has sparked considerable discussion as an energy-management technology. Due to the inherently high maneuverability of EVs, V2G systems must provide on-demand service for EVs. Therefore, in this work, we propose a hybrid computing architecture based on fog and cloud with applications in 5G-based V2G networks. This architecture allows the bi-directional flow of power and information between schedulable EVs and smart grids (SGs) to improve the quality of service and cost-effectiveness of energy service providers. However, it is very important to select an EV suitable for scheduling. In order to improve the efficiency of scheduling, we first need to determine define categories of target EV users. We found that grouping on the basis of EV charging behavior is one effective method to identify target EVs. Therefore, we propose a hybrid artificial intelligence classification method based on the charging behavior profile of EVs. Through this classification method, target EVs can be accurately identified. The results of cross-validation experiments and performance evaluations suggest that this method is effective.

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With the development of gesture-based interaction technologies (e.g., touchscreen devices and kinetic controllers), consumers can directly use their hands to interact with web interfaces, which may assist in creating a sense of touch for consumers. Drawing on feelings-as-information theory, this study investigates the impacts of two types of gesture-based interaction (i.e., touchscreen interaction and mid-air interaction) on consumers’ sense of touch. Results from a laboratory experiment showed that touchscreen interaction elicited a higher sense of touch than mid-air interaction when the importance of product haptic information was high. However, touchscreen interaction did not differ from mid-air interaction in terms of eliciting consumers’ sense of touch when the importance of product haptic information was low. Furthermore, consumers’ sense of touch improved their shopping experience satisfaction by reducing uncertainty about products and fostering attachment to products. Theoretically, this study contributes to the existing literature by empirically investigating the effects of gesture-based interaction on consumers’ bodily sensations, elucidating the role of the sense of touch in affecting consumers’ virtual product experience, and highlighting the impact of the interaction method on consumer behavior. This study also provides practical insights into the application of gesture-based interaction technologies.