The sensing-computing integrated chips and systems can be used for intelligent transportation to process and acquire traffic data. Traffic data can be used to effectively forecast real-time traffic flow at a specific future time, which is crucial for promoting efficient transportation systems and supporting economic development in the era of 6G. However, traditional real-time traffic flow prediction models exhibit poor performance when dealing with noise, uncertainty, and nonlinear data. To address this issue, this paper constructs a deep fuzzy rough neural network model based on large-scale multiobjective optimization algorithm(LMO-DFRNN) for real-time traffic flow prediction. By simultaneously optimizing multiple objectives, the model achieves an optimal balance between performance and simplicity in traffic flow tasks. To improve the model’s accuracy and adaptability in real-time traffic flow forecasting, this study presents a large-scale multiobjective optimization method that uses a state-information-based dynamic balancing evaluation strategy. The evaluation method comprises diversity and convergence, each corresponding to a specific factor. By dynamically adjusting the weights of two factors based on the individual performance on diversity and convergence, a balance between these two indicators is achieved. The experiments were conducted by using real-world traffic flow datasets, and the findings reveal that, in comparison with five advanced models, the proposed model achieved reductions in the evaluation metrics MAE, RMSE and MAPE by 43.73%, 46.22%, and 34.87% respectively.

The aim of sequential pattern mining (SPM) is to discover potentially useful information from a given sequence. Although various SPM methods have been investigated, most of these focus on mining all of the patterns. However, users sometimes want to mine patterns with the same specific prefix pattern, called co-occurrence pattern. Since sequential rule mining can make better use of the results of SPM, and obtain better recommendation performance, this paper addresses the issue of maximal co-occurrence nonoverlapping sequential rule (MCoR) mining and proposes the MCoR-Miner algorithm. To improve the efficiency of support calculation, MCoR-Miner employs depth-first search and backtracking strategies equipped with an indexing mechanism to avoid the use of sequential searching. To obviate useless support calculations for some sequences, MCoR-Miner adopts a filtering strategy to prune the sequences without the prefix pattern. To reduce the number of candidate patterns, MCoR-Miner applies the frequent item and binomial enumeration tree strategies. To avoid searching for the maximal rules through brute force, MCoR-Miner uses a screening strategy. To validate the performance of MCoR-Miner, eleven competitive algorithms were conducted on eight sequences. Our experimental results showed that MCoR-Miner outperformed other competitive algorithms, and yielded better recommendation performance than frequent co-occurrence pattern mining. All algorithms and datasets can be downloaded fromhttps://github.com/wuc567/Pattern-Mining/tree/master/MCoR-Miner.

Medical data widely exist in the hospital and personal life, usually across institutions and regions. They have essential diagnostic value and therapeutic significance. The disclosure of patient information causes people’s panic, therefore, medical data security solution is very crucial for intelligent health care. The emergence of federated learning (FL) provides an effective solution, which only transmits model parameters, breaking through the bottleneck of medical data sharing, protecting data security, and avoiding economic losses. Meanwhile, the neural architecture search (NAS) has become a popular method to automatically search the optimal neural architecture for solving complex practical problems. However, few papers have combined the FL and NAS for simultaneous privacy protection and model architecture selection. Convolutional neural network (CNN) has outstanding performance in the image recognition field. Combining CNN and fuzzy rough sets can effectively improve the interpretability of deep neural networks. This article aims to develop a multiobjective convolutional interval type-2 fuzzy rough FL model based on NAS (CIT2FR-FL-NAS) for medical data security with an improved multiobjective evolutionary algorithm. We test the proposed framework on the LC25000 lung and colon histopathological image dataset. Experimental verification demonstrates that the designed multiobjective CIT2FR-FL-NAS framework can achieve high accuracy superior to state-of-the-art models and reduce network complexity under the condition of protecting medical data security.

This paper presents an improved fuzzy c-means algorithm to solve the manufacturing cell formation problems. The proposed algorithm, which integrates the subtractive algorithm (to produce an initial solution), the fuzzy c-means (FCM) algorithm and a solution selecting procedure (to identify the best solution), remedies the major weaknesses of original FCM clustering. We test the performance of the proposed algorithm with 20 data sets from open literature and 60 generated data sets. Our experiments show that the proposed approach performs much better than the original FCM and the solutions are consistent with the best solutions found in references or the control solutions.