Band 16, Heft 1

Civil aviation passengers’ comments about airlines or airports on social media are the key to improving service quality. In order to make effective use of these data, in-depth analysis is needed to provide solid support for service improvement of airlines and airports. Due to its uniqueness, accurate modeling and analysis are required. First, the data are accurately collected from various network platforms and reprocessed. In this process, transfer learning, artificial data annotation, and term frequency–inverse document frequency (TF-IDF) analysis technology are innovatively integrated to ensure data quality and analysis depth. Then, according to the characteristics of the review data, the civil aviation domain-specific word vector based on Word2Vec was customized and developed, and the backtranslation – convolutional neural networks – bi-directional long short-term memory (Backtranslation-CNN-BiLSTM) model was constructed for sentiment analysis. The model is verified by multi-dimensional evaluation indicators, which shows excellent performance indicators and ensures reasonable efficiency. Finally, the cutting-edge BERTopic modeling technology was used to deeply mine the passenger comment topics to reveal the focus and potential needs of passengers. This study successfully constructed the technical system of civil aviation passenger comment sentiment analysis, which provided technical support for industry service optimization.

Campus green space management and optimization are essential when an educational campus is sustainable and conducive. Geographic Information System (GIS) technology has advanced recently, making it a powerful tool for preserving and enhancing green space networks. To guarantee that the demands of urban development inhabitants are met while considering the distribution of natural resources, the investigation has developed a GIS-based evaluation technique for the establishment of green spaces. After over 20 years of campus management in developing China, the rate of development has drastically decreased, and a renewed emphasis on creating sustainable, efficient green campuses has taken hold. Enhancing usability, availability, and appeal is a factor in evaluating whether or not green spaces can achieve their potential benefits. This study ensures the longevity and sustainability of green spaces while minimizing downtime. Furthermore, the impression of a smart campus based on Public Green Space (PGS) and its environment is not much impacted by a student’s gender or growth environment. Which is a planned space used for environmental, recreational, and aesthetic functions. The research underscores the significance of the PGS environment to the general attractiveness and accessibility of green spaces and its contribution to how smart the campus is perceived. The research recommends streamlining the layout and facilities of these spaces to accommodate the requirements of students and maximize their functionality. The demand and satisfaction of the campus PGS overall were also high, with Space-6 having the highest overall satisfaction score of 149 and Space-4 having the highest demand for walking or chatting of 135. Usage periods also had high usage participation, with after-class usage having the highest score of 160 in Space-1. The convenience of the PGS was also highly rated, with Space-1 as the most convenient for students at 88 points from the main entrance. Instead, the plant arrangement, seasonal color richness, and the amenities needed in various locations have a greater impact. Our recommendations are based on GIS and address the needs of students while enhancing the appeal and use of green campuses.

In the digital age, artistic creation needs to strike a balance between automation and personalization. This study proposes an innovative model that integrates generative adversarial networks, computer vision, and personalized adjustment technology. Through multistage iterative optimization, efficient art generation and personalized style customization are achieved. The model uses an automated generation module to generate a draft and guides the conditional vector to achieve fine-grained adjustment of the image so that the work maintains both technical innovation and the artist’s unique style. The model performance is optimized in four stages: data preparation, model training, personalized adjustment, and evaluation feedback. The actual art project “Echoes in the Mirror” is used as a case to verify the actual application effect of the model. The evaluation shows that the work receives high scores in clarity, color accuracy, style coherence, and innovation (the average score is close to 9 points). Audience feedback shows that the model performs well in enhancing immersive experience, emotional resonance, and interactive satisfaction, whereas technical acceptance also highlights room for optimization. The research results not only demonstrate the potential of automated and personalized models in artistic creation but also provide practical guidance for the deep integration of art and technology in the future and promote artistic creation to move toward the two-way improvement of innovation and audience experience.

This study explores a pioneering research effort focusing on the use of deep learning techniques to achieve high-precision automatic recognition of aluminum furniture design styles, and proposes an innovative convolutional neural network (CNN) architecture that deeply integrates the migration learning techniques of pre-trained models and the multi-level feature of the feature pyramid network (FPN) integration mechanism. This research is dedicated to solving the challenges of recognizing design elements and styles in the aluminum furniture industry, especially the robustness of recognition under different size variations, complex background interference, and diverse design styles. First, this study fills the gap of deep learning in the field of automatic classification of aluminum furniture design styles, using the powerful image understanding and pattern recognition capabilities of deep learning to effectively break through the bottleneck of the previous traditional methods that have low recognition accuracy when dealing with complex shapes, detail-rich, and diverse styles of aluminum furniture. This is the first time that deep learning technology is systematically applied to such specific scenarios, showing significantly better performance than traditional recognition means. Second, the core contribution of this study is the design of a comprehensive integration scheme that creatively combines a pre-trained CNN model and an FPN structure. This composite deep learning model is able to take full advantage of the generic feature representation acquired by the pre-trained model on large-scale image datasets, while extracting multi-scale local and global features with the advantage of FPNs, which ensures that key design style features can be accurately captured no matter how the size of the design elements of aluminum furniture changes.

Understanding customer behavior has become critical for marketers and tourist service providers since global travel has rapidly expanded. Recognizing consumer behavior patterns allows organizations to modify their strategy for better service delivery. However, the conventional approaches frequently fail to adequately capture the variety and complexity of tourism consumer behavior because of the large and diverse data available. This research seeks to investigate the use of fuzzy clustering analysis to better understand tourism consumer behavior patterns. The method combines fuzzy clustering algorithms with customer behavior data such as demographics, travel preferences, and purchasing patterns. The investigation reveals separate groups of consumers, providing insights into how various factors influence tourist purchasing decisions. The data were gathered using questionnaires, online booking platforms, and travel websites, where customers provided information about their previous travel experiences and preferences. Data preparation was used to normalize the data for analysis. Principal component analysis was employed to decrease dimensionality. The Sea Turtle Foraging Optimized Fuzzy C -Means clustering (STFO-FCMC) is presented as an extension of normal FCMC that incorporates an optimization procedure based on sea turtle foraging habits. This optimization enhances the accuracy and efficiency of cluster center selection and membership values, making STFO-FCMC especially well-suited for dealing with the complexity and unpredictability of tourism behavior data. The findings show multiple consumer behavior patterns, including diverse preferences for various types of tourist products and services, which are split by age, income, and travel objectives. The STFO-FCMC method is assessed using metrics, including accuracy of 97.84%, precision, recall, and F 1-score. These data assist service providers create individualized services and marketing strategies that improve consumer satisfaction and business performance. Overall, fuzzy clustering analysis, particularly with the STFO-FCMC approach, is a successful tool for detecting tourist consumer behavior, with substantial promise for improving tourism product and service targeting.

This study investigates the transformative incorporation of human–robot interaction (HRI) within contemporary supply chain management (SCM), focusing on improving efficiency and collaboration. Employing a multifaceted approach, our research examines the intricate dynamics between human workers and robots across various SCM operations, including logistics, inventory management, and storage. Through empirical case studies, qualitative evaluations, and technical insights, we investigate innovative interfaces and communication protocols aimed at fostering intuitive and productive engagement. Our methodology encompasses a thorough analysis of HRI’s impact on resource allocation, operational costs, and error reduction, elucidating both its benefits and challenges. Crucially, we emphasize the imperative of seamless collaboration between humans and robots, facilitated by technology. Our findings underscore the pivotal role of HRI in augmenting the agility and reactivity of supply chain ecosystems, offering strategic insights for addressing dynamic market needs and optimizing operations. Ultimately, this research contributes to a deeper understanding of evolving SCM dynamics and paves the way for a seamless and productive future for companies.

As an important part of Chinese culture, red culture has a profound historical heritage and rich spiritual connotations. The red culture experience aims to inspire participants’ patriotic feelings and national pride by recreating historic and revolutionary scenes and sharing stories of the revolution. To further enhance the impact of red cultural experience activities, this paper employs a minimum spanning tree (MST) analysis to investigate the emotional effects of eye and brain movement features on participants in these activities. A multimodal physiological signal emotion recognition model was constructed using EEG and eye movement data, and the accuracy of emotion classification for happy, sad, fearful, and neutral emotions was investigated. The research results show that the emotion recognition effect is optimal when eye movement, differential entropy feature, and MST attribute are integrated simultaneously. The three can achieve effective integration between EEG signals and eye movement signals across modes to obtain more emotion-related information and fully utilise the complementarity between this multimodal information.

This article aims at presenting a novel framework for dance action recognition and health enhancement. It uses embedded systems with sensors and machine learning. System makes use of an array of inertial measurement units. Also, accelerometers and gyroscopes collect motion data across multiple dimensions, thereby capturing the dynamics of dance movements. This is to recognize the dance actions effectively. And as a solution to the temporal and spatial data dependencies, the convolutional neural network-long short-term memory-based model is utilized. Feature extraction and optimization is performed via a built-in data preprocessing module. This model gives an added advantage of efficiency and low latency. An health promotion module includes biometric tracking and awareness. It tracks and displays data about the dancer’s heart rate, energy consumption, and joint loading. Several experiments are performed to assess the system performance, using various datasets and comparisons with the traditional approaches. Outcome shows enhancement in the recognition rate, energy consumption, and feedback efficiency. This work benefits the development of intelligent dance systems. It also has implications for the application of embedded systems in sports training, rehabilitation, and health monitoring.