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In this paper, we describe the design and implementation of a tool called the Lexical Dificulty Filter (LDF) intended to help learners or teachers in selecting sentences appropriate to the level of students for English vocabulary learning. The LDF serves as a bridge between learner and information resource bank. It selects suitable sentences from the output of standard English corpus concordancers. The core technology in the design of LDF is a f u u y expert system. The results obtained are very encouraging in this pioneering work. We achieve 94.33% accuracy rate in imitating the judgments of a human expert in determining the degree of dificulty of a sentence for a given learner level.

In recent times, water level monitoring stations have been established on major rivers to detect changes in water levels. When the water level reaches a certain threshold, relevant units are alerted, and appropriate measures, such as the closure of bridges and roads, are taken. However, with the significant advancements in the field of AI image recognition, researchers are exploring the use of image recognition through CCTV images to monitor water levels more intuitively and potentially replace traditional water levels gauges, such as radar or pressure-based gauges.This study employs deep learning techniques, specifically the current image segmentation of deep image learning, to segment water surface images captured by CCTV cameras. A virtual water ruler, drawn by the user, is then used to calculate the water level height by converting the intersection of the water surface and the virtual water ruler. The Deeplab V3 algorithm provided by Google was used to obtain 500 images in the demonstration area during morning, noon, and evening periods, with water level recognition performed every minute.The accuracy rate of the water level detection by this study was calculated separately for morning, afternoon, and evening periods, resulting in an overall accuracy rate of 83.5%. These findings demonstrate that the image recognition method is a viable and effective replacement for traditional IoT devices. The sponsored water level recognition project, located next to the Lansheng Bridge in Wulai District, New Taipei City, was used as an example to showcase the potential applications of this technology in the field of water resource management.