The Visual Question Answering (VQA) system is the process of finding useful information from images related to the question to answer the question correctly. It can be widely used in the fields of visual assistance, automated security surveillance, and intelligent interaction between robots and humans. However, the accuracy of VQA has not been ideal, and the main difficulty in its research is that the image features cannot well represent the scene and object information, and the text information cannot be fully represented. This paper used multi-scale feature extraction and fusion methods in the image feature characterization and text information representation sections of the VQA system, respectively to improve its accuracy. Firstly, aiming at the image feature representation problem, multi-scale feature extraction and fusion method were adopted, and the image features output of different network layers were extracted by a pre-trained deep neural network, and the optimal scheme of feature fusion method was found through experiments. Secondly, for the representation of sentences, a multi-scale feature method was introduced to characterize and fuse the word-level, phrase-level, and sentence-level features of sentences. Finally, the VQA model was improved using the multi-scale feature extraction and fusion method. The results show that the addition of multi-scale feature extraction and fusion improves the accuracy of the VQA model.

Nowadays, cities meet numerous sustainable development challenges in facing growing urban populations and expanding urban areas. The monitoring and simulation of land use and land-cover change have become essential tools for understanding and managing urbanization. This paper interprets and predicts the expansion of seven different land use types in the study area, using the PLUS model, which combines the Land use Expansion Analysis Strategy (LEAS) and the CA model, based on the multi-class random patch seed (CARS) model. By choosing a variety of driving factors, the PLUS model simulates urban expansion in the metropolitan area of Hangzhou. The accuracy of the simulation, manifested as the kappa coefficient of urban land, increased to more than 84%, and the kappa coefficient of other land use types was more than 90%. To a certain extent, the PLUS model used in this study solves the CA model’s deficiencies in conversion rule mining strategy and landscape dynamic change simulation strategy. The results show that various types of land use changes obtained using this method have a high degree of accuracy and can be used to simulate urban expansion, especially over short periods.

The process of computationally identifying and categorizing opinions expressed in a piece of text is of great importance to support better understanding and services to online users in the digital environment. However, accurate and fast multi-label automatic classification is still insufficient. By considering not only individual in-sentence features but also the features in the adjacent sentences and the full text of the tweet, this study adjusted the Multi-label K-Nearest Neighbors (MLkNN) classifier to allow iterative corrections of the multi-label emotion classification. It applies the new method to improve both the accuracy and speed of emotion classification for short texts on Twitter. By carrying out three groups of experiments on the Twitter corpus, this study compares the performance of the base classifier of MLkNN, the sample-based MLkNN (S-MLkNN), and the label-based MLkNN (L-MLkNN). The results show that the improved MLkNN algorithm can effectively improve the accuracy of emotion classification of short texts, especially when the value of K in the MLkNN base classifier is 8, and the value of α is 0.7, and the improved L-MLkNN algorithm outperforms the other methods in the overall performance and the recall rate reaches 0.8019. This study attempts to obtain an efficient classifier with smaller training samples and lower training costs for sentiment analysis. It is suggested that future studies should pay more attention to balancing the efficiency of the model with smaller training sample sizes and the completeness of the model to cover various scenarios.

In this current research, Twin-Delayed DDPG (TD3) algorithm has been used to solve the most challenging virtual Artificial Intelligence application by training a 4-ant-legged robot as an Intelligent Agent to run across a field. Twin-Delayed DDPG (TD3) is an incredibly smart AI model of a Deep Reinforcement Learning which combines the state-of-the-art methods in Artificial Intelligence. These includes Policy gradient, Actor-Critics, and continuous Double Deep Q-Learning. These Deep Reinforcement Learning approaches trained an Intelligent agent to interact with an environment with automatic feature engineering, that is, necessitating minimal domain knowledge. For the implementation of the TD3, we used a two-layer feedforward neural network of 400 and 300 hidden nodes respectively, with Rectified Linear Units (ReLU) as an activation function between each layer for both the Actor and Critics. We, then added a final tanh unit after the output of the Actor. The Critic receives both the state and action as input to the first layer. Both the network parameters were updated using Adam optimizer. The idea behind the Twin-Delayed DDPG (TD3) is to reduce overestimation bias in Deep Q-Learning with discrete actions which are ineffective in an Actor-Critic domain setting. Based on the Maximum Average Reward over the evaluation time-step, our model achieved an approximate maximum of 2364. Therefore, we can truly say that, TD3 has obviously improved on both the learning speed and performance of the Deep Deterministic Policy Gradient (DDPG) in a challenging environment in a continuous control domain.

Change detection of natural lake boundaries is one of the important tasks in remote sensing image interpretation. In an ordinary fully connected network, or CNN, the signal of neurons in each layer can only be propagated to the upper layer, and the processing of samples is independent at each moment. However, for time-series data with transferability, the learned change information needs to be recorded and utilized. To solve the above problems, we propose a lake boundary change prediction model combining U-Net and LSTM. The ensemble of LSTMs helps to improve the overall accuracy and robustness of the model by capturing the spatial and temporal nuances in the data, resulting in more precise predictions. This study selected Lake Urmia as the research area and used the annual panoramic remote sensing images from 1996 to 2014 (Lat: 37°00′ N to 38°15′ N, Lon: 46°10′ E to 44°50′ E) obtained by Google Earth Professional Edition 7.3 software as the research data set. This model uses the U-Net network to extract multi-level change features and analyze the change trend of lake boundaries. The LSTM module is introduced after U-Net to optimize the predictive model using historical data storage and forgetting as well as current input data. This method enables the model to automatically fit the trend of time series data and mine the deep information of lake boundary changes. Through experimental verification, the model’s prediction accuracy for lake boundary changes after training can reach 89.43%. Comparative experiments with the existing U-Net-STN model show that the U-Net-LSTM model used in this study has higher prediction accuracy and lower mean square error.