This article presents a survey of unmanned aerial vehicle (UAV) applications in smart cities, emphasizing integration challenges. Smart cities leverage innovative technologies, including the Internet of Things (IoT) and UAVs, to enhance residents’ quality of life. The study highlights UAV applications, challenges, limitations, and future perspectives of smart city development. Advanced control methods for maximizing UAV benefits are discussed. Control theory challenges and issues for the deployment of UAVs are addressed. By concentrating on challenges, potential applications, and advanced control techniques, this paper offers insights into UAVs’ role in shaping the future of smart cities.

In the framework of axiomatic fuzzy sets theory, we first study how to impersonally and automatically determine the membership functions for fuzzy sets according to original data and facts, and a new algorithmic framework of determining membership functions and their logic operations for fuzzy sets has been proposed. Then, we apply the proposed algorithmic framework to give a new clustering algorithm and show that the algorithm is feasible. A number of illustrative examples show that this approach offers a far more flexible and effective means for the intelligent systems in real-world applications. Compared with popular fuzzy clustering algorithms, such as c-means fuzzy algorithm and k-nearest-neighbor fuzzy algorithm, the new fuzzy clustering algorithm is more simple and understandable, the data types of the attributes can be various data types or subpreference relations, even descriptions of human intuition, and the distance function and the class number need not be given beforehand.

Wang–Mendel (WM) fuzzy system is an effective and interpretable model for solving tabular data classification problem. However, original WM fuzzy system is weak in handling dataset with high dimensionality or large volume. Meanwhile, its capability of characterizing data is narrow, which results from lacking hierarchical transformation of features like deep learning-based models. In this article, we propose a deep fuzzy rule-based classification system (DFRBCS) based on improved WM method, in which fuzzy technique and deep learning strategy are combined to make a desirable tradeoff between model’s interpretability and prediction accuracy. We first redefine the consequent part of fuzzy rule in WM fuzzy system with class probability vector, which endows the improved WM fuzzy system with capacity for serving as building block of deep model. The model structure of DFRBCS is designed in layer-by-layer manner, where raw features can be transformed hierarchically. For every layer in DFRBCS, it contains many improved WM fuzzy systems whose input spaces are generated by shuffling and sliding window operation on concatenated outputs of fuzzy systems in previous layer. Comparative experiments are conducted on 45 real-world datasets with various sizes and dimensionality between our method, five baseline models, and the other deep fuzzy classifiers (D-TSK-FC, HID-TSK-FC, FCCI-TSK, DSA-FC, and MEEFIS). The experimental results show that DFRBCS is competitive in classification performance and promising in model’s interpretability.