## 2 New Technologies and Applications
With the development of Internet technology and social network services, large amounts of text data and media are created. As a result, research is undertaken to analyze the latest social issues and consumer trends and detect spatiotemporal event sentences. However, documents contain not only important spatiotemporal events required for event analysis but also events that are not important for event analysis. For example, newspaper articles contain many events; thus, only the key events necessary for analysis must be extracted to improve the accuracy of event analysis. Therefore, the authorsof another paper [8] define important representative spatiotemporal event documents for the core topic of a document and propose a BiLSTM-based document classification model to classify representative spatiotemporal event documents. BiLSTM is a method of processing the input order of LSTM in a chronological order. The problem of converging results based on previous patterns is solved by learning in both directions, by connecting the forward hidden layer and the reverse hidden layer to the input layer and output layer. Moreover, the A method is used to calculate the output value and derive the final output value. The experimental results showed that the BiLSTM model improved the F1 score by 2.6% and accuracy by 4.5% compared with the baseline CNN model. Recently, the number of people turning to agriculture through urban farming and smart farms is increasing. As smart farm technology develops, farming has become easier, but support for basic agricultural technologies is still limited. Accordingly, deep learning-based crop disease diagnosis solutions have been researched, but the focus is on CNN-based disease detection; thus, the severity and characteristics of disease symptoms cannot be known. For a fundamental solution, the characteristics of disease symptoms necessary to prevent the spread of crop diseases must be identified and responded to in advance. Therefore, in another paper [9], the authors propose a crop disease diagnosis solution that applies deep learning-based image captioning and object detection technology to provide easy and practical help, even to novice farmers. The image caption model presents accurate disease names and generates accurate and meaningful sentences for understanding. In the object detection model, infected areas are detected, and damaged parts are recognized. As a result of the experiment, the average BLEU score of the image captioning model was 64.96%, which shows that sentence generation performance was high, and the mAP50 of the object detection model was 0.382, indicating that further improvement was needed. Wireless sensors and IoT devices are ubiquitous worldwide, and their applications and uses have expanded considerably. In addition, with the introduction of stable high-speed internet, such as 5G and 6G, various new services and applications are emerging in the fields of virtual/augmented reality, facial recognition, video games and video streaming, e-health, vehicle networks, and AI. However, these services require improved computing capacity and energy efficiency, which are not possible due to limited computing and battery capacity. This can alleviate limitations by offloading mobile edge computing, but because IoT-intensive tasks can be sent to the edge server, there is a limit to performance improvement due to overload. Accordingly, in addition to integrating task offloading and load balancing, another paper [10] solves the resource allocation problem of a multitier UAV-assisted MEC system. It designed a load-balancing algorithm to optimize the load between MEC servers and solves collaborative offloading, load balancing, and resource allocation as an integer problem. The authors also proposed a deep reinforcement learning-based algorithm for efficient task off-reading. Experimental results show that the proposed model quickly and remarkably reduces system cost (i.e., approximately 41.9%, 44.2%, and 11%) compared with local execution, global offloading policy, and task offloading.