One of the most important variables that leads to effective individual and group tours is the tourism route planning approach, which enables tourists to engage with tourism with ease, speed, and safety. However, current methods of designing tourist routes have some glitches, such as relying only on external objectives to find the best route. In this paper, a novel urban tourism path planning method based on a multiobjective genetic algorithm is proposed. The main goal of this paper is to enhance the accuracy of the genetic algorithm (GA) by adopting new parameters and selecting the optimal tourism path by combining external and internal tourist site potentials. Moreover, the GA and analytical hierarchy process (AHP) were used in our proposed approach to evaluate urban tourism route planning under multiple conflicting objectives. To visualize and execute the proposed approach, the geographic information system (GIS) environment was used. Our suggested approach has been applied to develop the tourist road network of Chengdu City in China. Compared with existing tourism path planning approaches, our proposed approach is more accurate and straightforward than other approaches used to choose routes.

Social media platforms play a vital role in determining valuable tourist objectives, which greatly aids in optimizing tourist path planning. As data classification and analysis methods have advanced, machine learning (ML) algorithms such as the k-means algorithm have emerged as powerful tools for sorting through data collected from social media platforms. However, traditional k-means algorithms have drawbacks, including challenges in determining initial seed values. This paper presents a novel approach to enhance the k-means algorithm based on survey and social media tourism data for tourism path recommendations. The main contribution of this paper is enhancing the traditional k-means algorithm by employing the genetic algorithm (GA) to determine the number of clusters (k), select the initial seeds, and recommend the best tourism path based on social media tourism data. The GA enhances the k-means algorithm by using a binary string to represent initial centers and to apply GA operators. To assess its effectiveness, we applied this approach to recommend the optimal tourism path in the Red Sea State, Sudan. The results clearly indicate the superiority of our approach, with an algorithm optimization time of 0.01 s. In contrast, traditional k-means and hierarchical cluster algorithms required 0.27 and 0.7 s, respectively.

The expansion of the tourism industry has led to the development of various methods to find optimal tourism paths. However, planning tourism paths in hilly areas remains complex and has specific challenges. Different algorithms have been used to plan tourism paths in flat and hilly terrains, including the traditional Ant Colony Optimization (ACO). Although widely used, this algorithm faces a number of limitations due to its slow implementation and pheromone update rules. This paper introduces a new approach to overcome these limitations. It presents a method for efficiently optimizing tourism paths in hilly areas based on an improved version of the ACO algorithm. The limitations of the traditional ACO and the Genetic Algorithm (GA) are addressed by improving pheromone updating techniques and implementing new initialization parameters. This approach provides a comprehensive and efficient method for planning hiking trails in hilly regions, considering dynamic tourism objectives such as temperature, atmospheric pressure, and health status. The proposed method is implemented to develop tourist routes in the hilly Jebel Marra region in Western Sudan. A comparison is provided between the effectiveness of this approach and the GA and traditional ACO algorithms. The advantage of the proposed approach is illustrated by results showing an optimization time of 0 points and 27 s compared to 0 points and 45 s and 0 points and 40 s for GA and ACO, respectively.

Hyperspectral image (HSI) classification is pivotal for remote sensing applications, including environmental monitoring, precision agriculture, and urban land-use analysis. However, its accuracy is often limited by scarce labeled data, class imbalance, and domain discrepancies between standard RGB and HSI imagery. Although recent deep learning approaches, such as 3D convolutional neural networks (3D-CNNs), transformers, and generative adversarial networks (GANs), show promise, they struggle with spectral fidelity, computational efficiency, and cross-domain adaptation in label-scarce scenarios. To address these challenges, we propose the Transformer–Graph Convolutional Network–Diffusion with Hybrid Domain Adaptation (TGDHTL) framework. This framework integrates domain-adaptive alignment of RGB and HSI data, efficient synthetic data generation, and multi-scale spectral–spatial modeling. Specifically, a lightweight transformer, guided by Maximum Mean Discrepancy (MMD) loss, aligns feature distributions across domains. A class-conditional diffusion model generates high-quality samples for underrepresented classes in only 15 inference steps, reducing labeled data needs by approximately 25% and computational costs by up to 80% compared to traditional 1000-step diffusion models. Additionally, a Multi-Scale Stripe Attention (MSSA) mechanism, combined with a Graph Convolutional Network (GCN), enhances pixel-level spatial coherence. Evaluated on six benchmark datasets including HJ-1A and WHU-OHS, TGDHTL consistently achieves high overall accuracy (e.g., 97.89% on University of Pavia) with just 11.9 GFLOPs, surpassing state-of-the-art methods. This framework provides a scalable, data-efficient solution for HSI classification under domain shifts and resource constraints.

Tourism path planning is complex in hilly areas due to a variety of challenges including steep hills, narrow roads, and limited accessibility. Furthermore, when planning tourism paths, hilly areas frequently have distinctive natural potential that must be carefully taken into account such as dynamic objectives. Temperature, pressure, and health state are three of the most important dynamic objectives. In this paper, we proposed a dynamic objectives approach to developing tourism path planning in hilly areas. The main contribution of this approach was the improved Ant Colony Optimization algorithm ACO to establish a new multi-dynamic objectives approach and apply this approach in hilly areas. We have improved ACO by comparing construction tour and updating pheromone methods. This approach has been applied to develop tourist routes in Jebel Marra hilly in western Sudan. Our approach was compared with the genetic algorithm GA and the traditional ACO. Results show the advantage of our approach due to the optimizing time being 0.27 seconds, 0.45 seconds for GA, and 0.40 seconds for traditional ACO.

Routing a medical-delivery UAV to multiple hospitals must jointly account for distance, flight time, payload, and clinical priority. We present a reproducible GIS-based framework for multi-stop UAV emergency medical routing in Khartoum State, Sudan, using a common obstacle-aware 3-D leg model with grid-based A* routing around no-fly zones and payload-dependent climb, cruise, and descent. We show that a linear priority term in a composite tour cost is order-invariant and cannot distinguish visiting sequences. We therefore use a priority-weighted completion-time objective with hard deadlines for Critical hospitals, with criterion weights derived from health professionals using the Analytic Hierarchy Process. On a 14-hospital network, exact Held–Karp and label-setting dynamic programming provide proven optima for benchmarking classical, metaheuristic, and Advantage Actor–Critic sequencing methods (A2C-SP). The revised objective reduces optimal priority-weighted completion time by 14% with only a 0.2% increase in distance. A2C-SP is 14.7% above the optimum across 47 unseen instances while generating routes in approximately 2 ms. Without retraining, it also plans capacity- and range-feasible multi-sortie missions on a 59-hospital city network, achieving a priority-weighted completion time of 110 min versus 137 min for the published toolbox route. At city scale, a single UAV cannot serve more than 5 of 16 Critical hospitals within 15 min, highlighting fleet sizing as a key constraint for meeting such deadlines.