Study on the evolutionary characteristics of the spatial association network of tourism eco-efficiency and its formation effect in China.

Against the backdrop of global sustainable development and ecological civilization construction, new quality productivity and high-quality development have become key drivers of industrial upgrading. Tourism eco-efficiency (TEE) serves as both a core indicator of the green and sustainable development of the tourism industry and a crucial lever for promoting high-quality development. Emphasizing technological innovation and resource optimization, new quality productivity enhances TEE levels and facilitates the green transformation of the industry.

Improved sports image classification using deep neural network and novel tuna swarm optimization.

Sports image classification is a complex undertaking that necessitates the utilization of precise and robust techniques to differentiate between various sports activities. This study introduces a novel approach that combines the deep neural network (DNN) with a modified metaheuristic algorithm known as novel tuna swarm optimization (NTSO) for the purpose of sports image classification. The DNN is a potent technique capable of extracting high-level features from raw images, while the NTSO algorithm optimizes the hyperparameters of the DNN, including the number of layers, neurons, and activation functions.