Edge server placement (ESP) is a critical determinant of service quality in edge–cloud computing systems, yet existing solutions often neglect the inherent collaboration between edge and cloud, leading to suboptimal performance under dynamic workloads. To address this gap, this paper proposes a novel meta-heuristic edge server placement algorithm based on the Coati Optimization Algorithm (COA). We first formulate the ESP problem as a constrained binary nonlinear programming model that explicitly incorporates edge–cloud collaboration, aiming to minimize the average request processing delay. The proposed COA-based solver features a compact one-dimensional encoding scheme that simultaneously represents server placement and request offloading decisions, a tailored boundary correction mechanism to enforce coverage and atomicity constraints, and a balanced exploration–exploitation strategy inspired by coatis’ natural hunting and escape behaviors. Extensive simulations are conducted, comparing the proposed algorithm against ten representative heuristic and meta-heuristic algorithms, including GA, PSO, DE, GWO, and their variants. The experimental results demonstrate that our algorithm significantly outperforms all compared methods in terms of the mean, minimum, and standard deviation of the overall average processing delay. Specifically, it achieves a 98.2% reduction in the mean delay relative to suboptimal algorithms while maintaining near-zero variance, confirming its effectiveness, efficiency, and robustness. The proposed algorithm provides a promising solution for service providers to enhance quality of service through optimal edge server deployment and request offloading under edge–cloud collaboration.

Accurate prediction of student graduation status is crucial for higher education institutions to implement timely interventions and improve student success. While existing methods often rely on single data sources or generic model architectures, this paper proposes a novel Multi-Branch Convolutional Neural Network (MBCNN) that systematically integrates multi-dimensional factors influencing student outcomes. The model employs eight dedicated branches to capture both subjective and objective features from four key dimensions: student characteristics, school resources, family environment, and societal factors. Through robust normalization and hierarchical feature fusion, MBCNN effectively learns discriminative representations from these heterogeneous data sources. Evaluated on a real-world dataset from Polytechnic Institute of Portalegre, our approach demonstrates superior performance compared to traditional and up-to-date machine learning methods, achieving improvements of 4.07–17.35% in accuracy, 4.60–20.19% in weighted precision, 4.07–17.35% in weighted recall, and 4.59–18.73% in weighted F1-score. The results validate that domain-specific neural architectures, designed to align with the inherent structure of educational data, significantly enhance prediction accuracy and generalization capability.

With the rapid growth of edge computing applications, optimizing both edge server placement and task offloading decisions is critical for minimizing system latency in edge–cloud environments. However, these two problems are tightly coupled and jointly form a binary non-linear programming (BNLP) problem that is NP-hard. To address this challenge, this paper proposes a novel hybrid meta-heuristic algorithm with alternating iteration, which decouples the joint optimization into two interdependent subproblems: edge server placement and task offloading. These subproblems are solved alternately using particle swarm optimization (PSO) for placement and a genetic algorithm (GA) for offloading, respectively. PSO efficiently explores the discrete placement space under bound constraints, while GA effectively navigates the high-dimensional binary offloading space. Compact encoding schemes are designed to inherently satisfy problem constraints, reducing search overhead and improving convergence. The overall algorithm exhibits polynomial-time complexity, making it scalable for practical deployments. Extensive experiments comparing the proposed method against ten baseline algorithms demonstrate that it achieves the best latency with the smallest standard deviation. The results validate the effectiveness, robustness, and scalability of the proposed alternating iterative hybrid meta-heuristic approach for joint edge server placement and task offloading optimization.

The cultivation of high-quality talents relies on the synergistic interaction of various educational stakeholders, including schools, families, and society, within the educational system. With the rapid advancement of artificial intelligence (AI) technology, new opportunities have emerged for constructing and optimizing collaborative education mechanisms. Based on a feature-rich and large-scale real dataset, this paper conducts a case study to explore novel approaches for leveraging AI to empower a cooperative education system. Specifically, correlation and association analysis methods from traditional statistics are first employed to quantify pairwise feature relationships, providing a basis for identifying key factors influencing student development. Subsequently, principal component analysis (PCA) is applied to extract dominant components from the dataset, assess the intrinsic information carried by each feature, and uncover latent relationships among features. Finally, leveraging the multi-source and heterogeneous nature of the cooperative education system, a novel multi-branch neural network model (MBDNN) is proposed to achieve accurate prediction of student academic performance. This study can provide reference and methodological support for effectiveness evaluation and decision-making within the cooperative education system.