Abstract This research explores the impact of visual merchandising elements on visitor engagement, satisfaction, and purchasing behavior in art galleries. Drawing upon the Servicescape Model and the Stimulus-Organism- Response (S-O-R) Model, the study integrates findings from a comprehensive... This research explores the impact of visual merchandising elements on visitor engagement, satisfaction, and purchasing behavior in art galleries. Drawing upon the Servicescape Model and the Stimulus-Organism- Response (S-O-R) Model, the study integrates findings from a comprehensive literature review and qualitative analysis of semi-structured interviews and focus groups. Key visual merchandising elements, including lighting, layout, color schemes, signage, and interactive displays, are examined for their influence on visitor emotions and cognitive responses. Read More Read Less

Abstract With the proliferation of technology, the field of adaptive e-learning has garnered significant attention in recent years. This is because it has allowed users to learn at their own pace and to define personal learning paths based... With the proliferation of technology, the field of adaptive e-learning has garnered significant attention in recent years. This is because it has allowed users to learn at their own pace and to define personal learning paths based on their individual interests and needs. Using several different devices and sensors around the world can provide to generate massive amounts of data. The analysis of this collected data will provide a basic solid information to ensure adaptive e-learning. Machine learning and data analytics are today very common techniques that can help extract information and find valuable patterns within the collected data. In this work, the field of adaptive e-learning is investigated in terms of definitions and characteristics. Moreover, a taxonomy of various challenges, used machine learning algorithms, the data used in this process are discussed. Also, some of the works proposed in the literature, which tackle these challenges are presented. Our study shows that, despite attempts made by these works to improve the adaptive e-learning. Data processing is generally performed in deferred time, which does not reflect the current state and needs of learners. Likewise, the learner’s behavior is often unpredictable, it can be influenced by several mental and environmental factors and it changes rapidly over time. Data stream mining is very important in adaptive e-learning which originated many main research directions for this area that merit further exploration and investigation. Read More Read Less

Abstract The deep recurrent neural networks (RNNs) and their associated gated neurons, such as Long Short–Term Memory (LSTM) have demonstrated a continued and growing success rates with researches in various sequential data processing applications, especially when applied to... The deep recurrent neural networks (RNNs) and their associated gated neurons, such as Long Short–Term Memory (LSTM) have demonstrated a continued and growing success rates with researches in various sequential data processing applications, especially when applied to speech recognition and language modeling. Despite this, amongst current researches, there are limited studies on the deep RNNs architectures and their effects being applied to other application domains. In this paper, we evaluated the different strategies available to construct bidirectional recurrent neural networks (BRNNs) applying Gated Recurrent Units (GRUs), as well as investig-ating a reservoir computing RNNs, i.e., Echo state networks (ESN) and a few other conventional machine learning techniques for skeleton-based human motion recognition. The evaluation of tasks focuses on the generalization of different approaches by employing arbitrary untrained viewpoints, combined together with previously unseen subjects. Moreover, we extended the test by lowering the subsampling frame rates to examine the robustness of the algorithms being employed against the varying of movement speed. Read More Read Less