EAI Endorsed Transactions on Scalable Information Systems

The Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities in the Context of Big Data

Xiaoxue Fan, Bingxin Zhang, Ping Zhang — Wed, 14 May 2025 00:00:00 +0000

INTRODUCTION: The advancement of appropriate instructional procedures and data techniques had already facilitated the development of a virtual evolutional innovation and entrepreneurship education process.

OBJECTIVES: The adoption of big data has also tended to result in academic revolutionary movements for businesses. To address the limitations of current university instructional setups and to broaden the scope of Big Data's (BDC) application, this study presents an optimization technique for evaluating the quality of innovation and entrepreneurship education.

METHODS: This paper presents an optimization technique for measuring the quality of education in innovation and entrepreneurship. This technique should help us move beyond the constraints of our existing approaches to higher education's pedagogical infrastructure into the domain of big data.

RESULTS: The proposed optimization algorithm differs from conventional university quality evaluation instructional practices as it integrates big data that dramatically improves the college Entrepreneurship class interaction. This article proposed a Convolution neural network algorithm with BDC for training virtualization representation and employs a standard correlation analysis method in data analysis to retrieve the correlation relationship between the information content enclosed in huge entrepreneurship education and online students.

CONCLUSION: Simulation results revealed that the proposed model has provided an accuracy of 98%. The proposed method provides a platform for sharing instructional materials that function more efficiently under heavy load. Up to 98% and 94% security are achieved under heavy and light loads, respectively. The use of cloud computing in this scenario led to improvements of 7% and 8%, respectively, yielding results of 89% and 86%.

Fuzzy TOPSIS Method for Sustainable Supplier Assortment in Green Supply Chain Management

Huzaifa Saleem, Shafeeq Ahmad, Umar Badr Shafeeque, Naseem Ahmad Khan — Mon, 26 May 2025 00:00:00 +0000

INTRODUCTION: Green supply chain management represents one of the crucial ways for organizations to start minimizing their ecological footprint in the era of increasing ecological preoccupation and sustainability objectives. The critical issues of green supply chain management involve the assortment of green suppliers who are well-suited with the environmental objectives of organizations. Traditional methods of supplier selection cannot efficiently depict the complex, uncertain nature of sustainability criteria. In this respect, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution, shortly known as Fuzzy TOPSIS, is proposed for usage in this research for improving the process of supplier assortment in green supply chain management.

OBJECTIVES: The aim of this work is, therefore, to present an integrated framework, utilizing the Fuzzy TOPSIS method for selecting sustainable suppliers in green supply chain management. The particular aim of the study will be to incorporate environmental, social, as well as economic criteria in performance evaluation at the supplier level, by considering innate uncertainties and fuzziness related to sustainability metrics.

METHODS: The Fuzzy TOPSIS process is applied to assess and rank potential suppliers based on multiple criteria considering both environmental and economic factors.

RESULTS: Application of the Fuzzy TOPSIS method in sustainable supplier assortment demonstrates its effectiveness in identifying suppliers that align with green objectives while meeting operational requirements.

CONCLUSION: The proposed framework will provide a more fine-tuned and flexible tool for decision-makers by incorporating fuzzy logic into the complexities at hand for sustainability assessment. The findings underline the importance of adopting advanced techniques in decision making in order to attain environmental responsibility and long-term sustainability in supply chain operations.

Research on Hybrid Path Planning Algorithms for UAVs in Complex Environments

Xinyue Chang, Liang Ye, Lin Ma, Shuyi Chen — Tue, 27 May 2025 00:00:00 +0000

INTRODUCTION: This paper investigates a UAV path planning algorithm in a UAV-assisted network scenario, integrating both global and local path planning. Firstly, the ASPSO (Adaptive Spherical Vector-Based Particle Swarm Optimization) algorithm is proposed for offline path planning to obtain key global path points, providing a general flight strategy for the UAV. During the flight, the UAV continuously detects surrounding obstacles in real-time. If newly detected obstacles are encountered, the ECAVF (Enhanced Collision Avoidance Vector Field) algorithm is employed for local path planning to dynamically avoid obstacles and ensure the safety of the UAV.

OBJECTIVES: The objective of this paper is to enhance the path planning capability of existing algorithms in complex three-dimensional environments, enabling UAVs to operate efficiently and safely.

METHODS: The proposed ASPSO algorithm determines parameter ranges for different scenarios during the initialization phase, effectively reducing initialization time. Additionally, a multi-strategy optimization approach is introduced during the search process. Expanding the search space in the early iterations helps escape local optima, while minor perturbations are introduced in the later iterations to continue exploring within the neighbourhood of high-quality solutions. Finally, a method utilizing virtual control points for path refinement is proposed to smooth the trajectory. The ECAVF algorithm incorporates a dynamic adjustment factor based on relative velocity to optimize the vector field in the presence of multiple moving obstacles. By integrating factors such as distance and velocity, a hybrid vector field is constructed, demonstrating superior robustness in complex multi-obstacle scenarios.

RESULTS: The proposed method is compared with the PSO (Particle Swarm Optimization), the Spherical Vector-based PSO, and the original CAVF (Collision Avoidance Vector Field) method. The results demonstrate that the proposed method exhibits higher initialization efficiency, superior initial solution quality, and the ability to obtain a more optimal global path. Additionally, it shows stronger dynamic obstacle avoidance capabilities and a higher success rate in avoiding obstacles.

CONCLUSION: These results demonstrate that the proposed method effectively enhances the quality of global path planning solutions and improves the success rate of dynamic obstacle avoidance.

Smart Data Prefetching Using KNN to Improve Hadoop Performance

Rana Ghazali, Douglas G. Down — Thu, 17 Apr 2025 00:00:00 +0000

Hadoop is an open-source framework that enables the parallel processing of large data sets across a cluster of machines. It faces several challenges that can lead to poor performance, such as I/O operations, network data transmission, and high data access time. In recent years, researchers have explored prefetching techniques to reduce the data access time as a potential solution to these problems. Nevertheless, several issues must be considered to optimize the prefetching mechanism. These include launching the prefetch at an appropriate time to avoid conflicts with other operations and minimize waiting time, determining the amount of prefetched data to avoid overload and underload, and placing the prefetched data in locations that can be accessed efficiently when required. In this paper, we propose a smart prefetch mechanism that consists of three phases designed to address these issues. First, we enhance the task progress rate to calculate the optimal time for triggering prefetch operations. Next, we utilize K-Nearest Neighbor clustering to identify which data blocks should be prefetched in each round, employing the data locality feature to determine the placement of prefetched data. Our experimental results demonstrate that our proposed smart prefetch mechanism improves job execution time by an average of 28.33% by increasing the rate of local tasks.

Enhancing Stakeholder Analysis with AI: A Comparative Study of Productivity and Quality in the Educational Context

Vijay Kanabar, Kalinka Kaloyanova — Fri, 23 May 2025 00:00:00 +0000

This paper examines the application of generative artificial intelligence in stakeholder management while studying the business aspects of software development and project management in two different universities. It explores a novel intersection of AI with software development and project management practices, offering valuable insights for both academia and industry. By investigating how students use AI alongside traditional methods under supervision, this study evaluates the effectiveness, quality of results, and creativity of students’ project assignments in identifying stakeholders and defining communication strategies. The findings suggest that AI can enhance work completion speed and contribute to greater project success due to a more complete identification of stakeholders and formulation of innovative stakeholder engagement strategies. There is a consensus, within this context, that while AI can be invaluable for project stakeholder management, human judgment remains essential.

Deep Learning Empowered Enterprise Knowledge Graph with Attention Mechanism

Yadong Shi, Liangbo Zeng, Liang Li, Junwei Zhu, Rongyin Tan — Tue, 27 May 2025 00:00:00 +0000

Enterprise knowledge graphs (EKGs) are pivotal in structuring and analyzing vast amounts of enterprise data, yet conventional construction methods struggle to efficiently capture complex relationships and dynamic enterprise contexts. This paper proposes a Deep Learning (DL)-based enterprise knowledge graph framework that integrates transformer-based architectures, graph attention networks (GATs), and reinforcement learning to enhance the construction, refinement, and querying of EKGs. Specifically, we employ a business-enhanced RoBERTa (BERTO) model for entity and relation extraction from unstructured data, a graph attention network for refining edge weights, and a reinforcement learning agent to adaptively update relationships based on user feedback. Additionally, a query-aware attention mechanism is incorporated for context-sensitive knowledge retrieval. Simulation results demonstrate that the proposed scheme outperforms conventional knowledge graph (GK) and deep learning (DL) models in predictive accuracy, especially under varying signal-to-noise ratio (SNR) conditions. Numerical comparisons reveal that at 10 dB SNR, the proposed scheme achieves a prediction accuracy of 0.74, surpassing the conventional GK (0.49) and conventional DL (0.34) methods. These results underscore the effectiveness of the proposed framework in improving accuracy, adaptability, and scalability in enterprise knowledge management.