In this paper, college students in the digital campus environment are taken as the research object, the purpose is to collect and summarize the data sources about digital campus for the calculation of students’ multidimensional behaviors, and to prepare for the study of students’ behavior patterns at each different learning stage after quantitative calculation, and constructing a multi-feature fusion student achievement prediction model by using machine learning algorithm, In this way, some effective learning methods can be provided, which have a certain effect on how to improve students’ academic performance, learning feedback and early warning. In order to obtain the input features required by the prediction model in this paper, the quantitative behavior features are processed first to obtain the selected features before the next step can be carried out, then the regression algorithm, model parameters, model evaluation methods and metrics used in this paper. Ablation experiments are also used to explore the performance of the model and different characteristics of different regression algorithms, especially the necessity of approximate entropy and change complexity measurement in the model. The development of this research has far-reaching influence on students, teachers, university management and research in this field.
With the growing demand for employee competency assessment in enterprises, artificial intelligence technology has gradually become an important tool. Most of the current employee competency assessments use models such as LSTM, which cannot adapt to data scarcity or bias, resulting in poor accuracy of competency assessment. This paper takes advantage of GAN’s adversarial data expansion to optimize the LSTM employee competency assessment model to address the insufficient data volume and uneven data distribution. The study first uses an MLP to build a generator and a DNN to build a discriminator, and jointly designs the GAN structure. Then, GAN is used to generate samples that are similar to, yet distinct from, the original data. The generated samples use adversarial training to approach the original data scatter gradually. Finally, the original data and synthetic data (i.e., GAN-generated data) are sent to the LSTM model for training to capture the time series dependency of employee capability data. The experimental results show that when the LSTM model is used in conjunction with the original data and the synthetic data, the MAE is only 0.12, which is 0.03 lower than the MAE of the original data. The KL divergence of the quality of the GAN synthetic data is only 0.29. The experimental results show that the use of artificial intelligence to generate adversarial networks can significantly optimize the performance of employee ability assessment, reduce assessment errors, and promote the improvement of the company’s competitiveness.
Classification is a core task in pattern recognition, with broad applications in image processing, speech recognition, and medical diagnostics. Traditional neural networks often suffer from difficulties in parameter optimization and local optima, while the performance of approximate logical dendritic neuron models depends heavily on proper parameter configuration. To address these issues, this paper proposes a Feature-Guided Adaptive Differential Evolution algorithm, which incorporates feature-guided mechanisms, adaptive parameter control, and multi-strategy mutation to enhance the standard differential evolution framework. The algorithm is applied to optimize the dendritic neuron model’s parameters, providing a more effective and robust solution for intelligent classification in complex data environments. This approach contributes to the development of reliable optimization strategies in intelligent systems and has promising application potential.
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.
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 F1-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.
