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This paper primarily studies the path planning problem for UAV formations guided by semantic map information. Our aim is to integrate prior information from semantic maps to provide initial information on task points for UAV formations, thereby planning formation paths that meet practical requirements. Firstly, a semantic segmentation network model based on multi-scale feature extraction and fusion is employed to obtain UAV aerial semantic maps containing environmental information. Secondly, based on the semantic maps, a three-point optimization model for the optimal UAV trajectory is established, and a general formula for calculating the heading angle is proposed to approximately decouple the triangular equation of the optimal trajectory. For large-scale formations and task points, an improved fuzzy clustering algorithm is proposed to classify task points that meet distance constraints by clusters, thereby reducing the computational scale of single samples without changing the sample size and improving the allocation efficiency of the UAV formation path planning model. Experimental data show that the UAV cluster path planning method using angle-optimized fuzzy clustering achieves an 8.6% improvement in total flight range compared to other algorithms and a 17.4% reduction in the number of large-angle turns.

Accurate individual-tree segmentation is essential for precision forestry. In previous studies, the canopy height model-based method was convenient to process, but its performance was limited owing to the loss of 3D information, and point-based methods usually had high computational costs. Although some hybrid methods have been proposed to solve the above problems, most canopy height model-based methods are used to detect subdominant trees in one coarse crown and disregard the over-segmentation and accurate segmentation of the crown boundaries. This study introduces a combined approach, tested for the first time, for treetop detection and tree crown segmentation using UAV–LiDAR data. First, a multiscale adaptive local maximum filter was proposed to detect treetops accurately, and a Dalponte region-growing method was introduced to achieve crown delineation. Then, based on the coarse-crown result, the mean-shift voxelization and supervoxel-weighted fuzzy c-means clustering method were used to identify the constrained region of each tree. Finally, accurate individual-tree point clouds were obtained. The experiment was conducted using a synthetic uncrewed aerial vehicle (UAV)–LiDAR dataset with 21 approximately 30 × 30 m plots and an actual UAV–LiDAR dataset. To evaluate the performance of the proposed method, the accuracy of the remotely sensed biophysical observations and retrieval frameworks was determined using the tree location, tree height, and crown area. The results show that the proposed method was efficient and outperformed other existing methods.

PurposeSubway systems are highly susceptible to external disturbances from emergencies, triggering a series of consequences such as the paralysis of the internal network transportation functions, causing significant economic and safety losses to cities. Therefore, it is necessary to analyze the factors affecting the resilience of the subway system to reduce the impact of disaster incidents.Design/methodology/approachUsing the interval type-2 fuzzy linguistic term set and the K-medoids clustering algorithm, this paper improves the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to construct a subway resilience factor analysis model for emergencies. Through comparative analysis, this study confirms the superior performance of the proposed approach in enhancing the precision of the DEMATEL method.Findings The results indicate that the operation and management level of emergency command organizations is the key resilience factors of subway operations in China. Furthermore, based on real case analyses, the corresponding suggestions and measures are put forward to improve the overall operation resilience level of the subway.Originality/value This paper identifies four emergency scenarios and 15 resilience factors affecting subway operations through literature review and expert consultation. The improved fuzzy DEMATEL method is applied to explore the levels of influence and causal mechanisms among the resilience factors of the subway system under the four emergency scenarios.

Estimation of suspended sediments carried by natural rivers is essential for projects related to water resource planning and management. This study proposes a dynamic evolving neural fuzzy inference system (DENFIS) as an alternative tool to estimate the suspended sediment load based on previous values of streamflow and sediment. Several input scenarios of daily streamflow and suspended sediment load measured at two locations of China—Guangyuan and Beibei—were tried to assess the ability of this new method and its results were compared with those of the other two common methods, adaptive neural fuzzy inference system with fuzzy c-means clustering (ANFIS-FCM) and multivariate adaptive regression splines (MARS) based on three commonly utilized statistical indices, root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). The data period covers 01/04/2007–12/31/2015 for the both stations. A comparison of the methods indicated that the DENFIS-based models improved the accuracy of the ANFIS-FCM and MARS-based models with respect to RMSE by 33% (32%) and 31% (36%) for the Guangyuan (Beibei) station, respectively. The NSE accuracy for ANFIS-FCM and MARS-based models were increased by 4% (36%) and 15% (19%) using DENFIS for the Guangyuan (Beibei) station, respectively. It was found that the suspended sediment load can be accurately estimated by DENFIS-based models using only previous streamflow data.

In this paper, an improved Type2-NPCM clustering algorithm based on improved adaptive particle swarm optimization called Type2-NPCM-IAPSO is proposed. First, a new clustering algorithm called Type2-NPCM is proposed. The Type2-NPCM algorithm can solve the problems encountered by the algorithms FCM, G-K, PCM and NPCM (sensitivity to noise or aberrant points and local minimal sensitivity), etc. Second, we combined our Type2-NPCM algorithm with the improved adaptive particle swarm optimization IAPSO algorithm to ensure proper convergence to a local minimum of the objective function. The effectiveness of the proposed Type2-NPCM-IAPSO algorithm was tested on the electro-hydraulic system, convection system and other nonlinear systems described by differential equation.

Plant diseases can damage specific parts of leaves for better readability during the farming process. Plants refer to various types of crops, including fruits and vegetables. During the production phase of healthy crops, plant diseases often begin by infecting the leaves. Leaves, being exposed, are more vulnerable to disease than other plant parts. When affected by disease, crop yield decreases, leading to economic loss. Hence, early disease identification model is required and deployed in an automated computerized way. The analysis have shown that multiple approaches were executed to detect the disease, still it suffers from pitfalls like inadequate feature extraction, handcrafted features, computation burden, complexities and so on. To improve the process, an efficient method is developed for detecting various plant diseases by different learning method. Firstly, the different plant leaf data were gathered from UCI, Kaggle web sources and benchmarks. The unwanted noise in the input leaf images are pre-processed by using median filter. Subsequently, the affected or abnormal region was segmented by the adaptive hybrid K-means with fuzzy C-means clustering (AHKM-FCM); the parameter tuning is also done by improved random variable-based water strider algorithm (IRV-WSA). Finally, the segmented region was subjected into the Transfer Learning Network that was processed with Efficient-net, ResNet and Densenet, in which fine tuning of weight was accomplished by using the IRV-WSA. The model was analyzed and computed across divergent measurements. Classification output results from the proposed IRV-WSA-ETLNet model include 94.853% accuracy, 94.750% sensitivity, 94.888% specificity, and 96.068% F1 Score. Additionally, this system uses less computing time 24.378 ms. Compared to previous methods, the findings of the proposed model demonstrate improved classification rates and help the farmer to increase crop production.

In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from different classes when producing synthetic samples. As a result, there can be more class overlap and more noise. To avoid this problem, this work presented an innovative technique called Adaptive Synthetic Data-Based Multi-label Classification (ASDMLC). Adaptive Synthetic (ADASYN) sampling is a sampling strategy for learning from unbalanced data sets. ADASYN weights minority class instances by learning difficulty. For hard-to-learn minority class cases, synthetic data are created. Their numerical variables are normalized with the help of the Min-Max technique to standardize the magnitude of each variable’s impact on the outcomes. The values of the attribute in this work are changed to a new range, from 0 to 1, using the normalization approach. To raise the accuracy of multi-label classification, Velocity-Equalized Particle Swarm Optimization (VPSO) is utilized for feature selection. In the proposed approach, to overcome the premature convergence problem, standard PSO has been improved by equalizing the velocity with each dimension of the problem. To expose the inherent label dependencies, the multi-label classification ensemble of Adaptive Neuro-Fuzzy Inference System (ANFIS), Probabilistic Neural Network (PNN), and Clustering-Based Decision tree methods will be processed based on an averaging method. The following criteria, including precision, recall, accuracy, and error rate, are used to assess performance. The suggested model’s multi-label classification accuracy is 90.88%, better than previous techniques, which is PCT, HOMER, and ML-Forest is 65.57%, 70.66%, and 82.29%, respectively.

In this paper, an improved Type2-PCM clustering algorithm based on improved adaptive particle swarm optimization called Type2-PCM-IAPSO is proposed. Firstly, a new clustering algorithm called Type2-PCM is proposed. The Type2-PCM algorithm can solve the problems encountered by fuzzy c-means algorithm (FCM), Gustafson-Kessel algorithm (G-K), possibilistic c-means algorithm (PCM) and NPCM (sensitivity to noise or aberrant points and local minimal sensitivity). . . etc. Secondly, we combined our Type2-PCM algorithm with the improved adaptive particle swarm optimization algorithm (IAPSO) to ensure proper convergence to a local minimum of the objective function. The effectiveness of the two proposed algorithms Type2-PCM and Type2-PCM-IAPSO was tested on a system described by a different equation, Box-Jenkins gas furnace, dryer system and the convection system. The validation tests used showed good performance of these algorithms. However, their average square error test (MSE) shows a better behaviour of the Type2-PCM-IAPSO algorithm compared to the FCM, G-K, PCM, FCM-PSO, Type2-PCM-PSO, RKPFCM and RKPFCM-PSO algorithms.

The rationality of product module partition is crucial to the success of modular design. The correlations between components of complex products are complex, increasing the difficulty of module partition. Thus, many existing methods of module partition have difficulty realizing this process effectively for complex products with a large number of components. This paper proposes a module partition method for complex products based on stable overlapping community detection and overlapping component allocation. The correlations between components are analyzed to obtain a comprehensive correlation strength matrix. The undirected weighted network is used to represent components and the correlations between them. A stable overlapping community detection algorithm based on the improved judgement of within-community Shapley values is proposed to generate multiple preliminary schemes of module partition. Overlapping components among modules are allocated to the most suitable modules by adopting a genetic algorithm (GA). The scheme with the largest modularity measure Q is selected as the final scheme of module partition. The proposed method is applied to a computer numerical control (CNC) grinding machine. The proposed module partition method for complex products is demonstrated to be superior to other effective methods.

There has been a growing interest in using Internet of Things (IoT) for speech translation in recent years, focusing on practical language applications rather than traditional grammar-based training. The language translation method engages language learners by placing them in effective learning environments. The purpose of English language conversion is primarily to teach non-native speakers’ native languages by providing them with a simple and easy-to-understand vocabulary to introduce them to a new language. This study proposes a framework for collaborative privacy management in smart classroom networks based on fuzzy logic decision-making. We used the data sensitivity and confidence values as input variables for the fuzzy system in our framework. Our framework also incorporates trust values between users, because it calculates the trust loss and gain to determine the reputation value. This study investigated the development of an English translation learning system using big data analysis and IoT to enhance students’ comprehension of the English language and improve their ability, effectiveness, and proficiency in translation. For this purpose, a client/server ( C / S ) learning system for English translation on Android devices is proposed. A detailed description is provided of the functionality of each module and database of the system, which is designed using a model–view–controller architecture. Moreover, the system utilizes a three-layer IoT architecture to facilitate wireless connections among Android devices, network translation servers, and speech synthesis servers, thereby providing users with intelligent services. Subsequently, the study applied an enhanced fuzzy c -means clustering algorithm to analyze English translation learning data. In addition to filtering out irrelevant information from the original dataset, the relief algorithm produces index weights that can be used in the improved fuzzy c -means clustering procedure. The enhanced fuzzy c -means clustering algorithm is combined with a threshold to create an objective function. Moreover, a unique insight into the English translation learning mode by classifying and clustering English translation resource data is discussed. It was demonstrated that the proposed method achieves high data classification accuracy and significantly enhances the effectiveness and learning outcomes of English translation education.