The June 2025 (Vol. 33, No. 2) issue of CIT. Journal of Computing and Information Technology brings four papers from the areas of network security, 3D modeling, brain-computer interaction, and recommendation systems.
Accurate prediction of product lifecycle stages is crucial for enhancing inventory turnover and strategic planning in the tobacco industry. This paper proposes an intelligent prediction model that integrates Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU), further optimized by an Improved Grey Wolf Optimizer (IGWO). The model fuses multi-source enterprise data—including sales trends, IoT logistics information, environmental conditions, and inventory records—to dynamically forecast lifecycle stages and remaining durations. The dataset comprises 180,000 labeled samples collected from real-world tobacco enterprise operations, encompassing multi-source variables such as sales volume, inventory changes, logistics routes, and environmental feedback. Experimental evaluations based on this dataset demonstrate that the proposed IGWO-CNN-GRU model achieves a Mean Squared Error (MSE) of 2.13, a Mean Absolute Error (MAE) of 1.17, and an R² of 0.932, significantly outperforming baseline models. In practical deployment simulations, the prediction deviation is limited to ±5 days, improving allocation efficiency and reducing inventory risks. The approach provides a robust and adaptable solution for full-lifecycle management in tobacco supply chains, offering practical value for intelligent production and market deployment strategies.
The Internet of Medical Things (IoMT) consists of interconnected devices and applications that enable real-time collection, transmission, and analysis of medical data for healthcare applications. This study utilizes medical data from the publicly available BCICIV2a dataset rather than data collected directly from individuals or medical institutions. With advancements in neuroinformatics and intelligent computing, the classification of electroencephalography (EEG) signals has become increasingly important, particularly for detecting and predicting epilepsy. However, existing EEG classification methods often suffer from low accuracy, high computational complexity, and slow processing. To address these challenges, this study proposes an EEG classification approach utilizing a Backpropagation Neural Network (BPNN) enhanced with Bayesian optimization. This method enhances the identification and prediction of epileptic seizures by utilizing IoT-enabled EEG data. Performance evaluation on the BCICIV2a dataset demonstrates that the proposed model achieves an accuracy of 93.21%, outperforming conventional techniques. The results indicate that this approach enhances efficiency and accuracy in EEG signal processing, contributing to real-time medical diagnostics. The integration of IoMT with advanced neural networks represents a significant advancement in medical informatics and telemedicine, providing promising directions for future research and clinical applications.
Facial 3D modeling technology is widely used and has become an important research direction in the fields of artificial intelligence and computer vision. However, the modeling accuracy and robustness of existing technologies in dealing with weak texture areas and complex lighting conditions are insufficient, which limits their practical application in production. Therefore, a facial 3D modeling method based on multi-scale feature fusion and lighting robustness optimization was proposed, and a multiscale dense feature network and lighting robustness feature fusion network were constructed. The experimental outcomes indicated that the method exhibited excellent performance on the dataset. Among them, structural similarity reached 0.954, and the average absolute error was the lowest at 0.63 mm. Under dynamic lighting conditions, the feature consistency reached 0.941, and the point cloud error was reduced to 0.85 mm. In addition, tests in security and virtual reality scenarios showed that after using this method, the accuracy increased to 92.8%, the peak signal-tonoise ratio reached 33.0 dB, and the model running efficiency improved to 36 frames per second, verifying the practicality and reliability of the method. The research provides new ideas for developing stable, efficient, and practical facial 3D modeling methods, which is expected to promote the widespread application of related technologies in complex environments.
This research proposes an efficient parallel graph partitioning algorithm for the big data environment, aiming to solve the bottlenecks of traditional clustering techniques in terms of processing speed and scalability. The algorithm adopts a multi-level graph partitioning framework, decomposing the network information processing task into multiple levels, gradually simplifying the graph structure and backtracking refinement, thereby significantly reducing the computational complexity while ensuring the partitioning quality. The algorithm focuses on balancing the node cohesion within partitions and the edge cutting cost of inter-partition communication. By constructing a global objective function, it minimizes the number of edges across partitions and the workload differences among various sub-graphs, thereby achieving a more balanced partitioning result. The research results show that this algorithm achieves a resource utilization rate of 0.95. In the Hadoop cluster environment, 95% of the computing resources are effectively used for actual task processing, which is significantly higher than that of the competing algorithms. The energy efficiency ratio reaches 0.98, indicating that the number of tasks completed per unit of energy consumption is close to the optimal level, which is superior to the 0.78 to 0.67 range of existing methods, reflecting the advantages of this algorithm in green computing. The load imbalance rate is only 0.00395, and the point weight imbalance rate is 0.00141, which are much lower values than those of the comparison algorithm. This indicates that the algorithm achieves a high degree of balance in task allocation and node weight distribution, effectively avoiding resource waste and performance bottlenecks.
