Cancer is a severe affection nowadays, leading to death in many situations. Among the liver tumors, the most often met is Hepatocellular Carcinoma (HCC), being present for 75% of the primary liver cancer patients. Despite its’ invasive character, the most reliable cancer diagnosis method is biopsy. For performing both non-invasive and accurate disease assessment, computerized techniques are required. Tumor semantic segmentation is useful in this context, achieving tumor detection, localization, and extension estimation at the same time. In our current research, we developed and comparatively assessed high performance methods for segmenting liver tumor, based on Convolutional Neural Networks (CNN), as UNet, UNet++ and DeepLabV3+, involving two CT image datasets in our experiments. The assessment was performed by considering both the polar and cartesian image representations. At the end, a maximum DICE value of 80.92%, a maximum IoU value of 69.55%, respectively a maximum accuracy value of 99.82% resulted.
Compared with teenagers in cities, unique groups such as teenagers living in mountainous areas have unique cultural experiences and language differences in using words to express emotions. This difference is reflected in aspects such as the use of local slang or dialects in emails. This article presents an improved BERT-based algorithmic model for detecting specific emotions in emails written by these teenagers and providing relevant feedback and emotional support. We classify adolescent emotions into: happiness, anger, surprise, sadness, fear and disgust. It was divided into training, validation and testing by analyzing the emails of 30 teenagers in the mountains. A combination of SVM, LSTM, and BERT algorithms was employed to enhance the emotion classification model's accuracy. The evaluation demonstrated that the BERT-based model notably outperformed others in both accuracy and F1-score.
In the domain of few-shot learning, where the scarcity of training data poses a significant challenge, this paper introduces an innovative approach. We present a few-shot classification algorithm that utilizes the Two-Stream Frequency Domain Information Network. This algorithm delves deep into the efficacy of training different frequency components of images in the context of few-shot classification. It integrates filtered and enhanced image frequency domain information as supplementary data to be adaptively fused with the original image data, enhancing and augmenting the available information. Experimental results establish that the few-shot classification model, employing the Two-Stream Frequency Domain Information Network, outperforms leading algorithms in the field of few-shot learning. This enhancement is evidenced by an accuracy improvement of 1.06% and 0.93% on the miniImageNet and tiredImageNet datasets, respectively, when compared to the Meta-Baseline, illustrating the efficacy of this specialized approach.
The key work of China’s high-speed railway has gradually shifted from "large-scale construction" to "large-scale operation", and the quality of high-speed railway operation has become the core focus in daily work. How to efficiently and reasonably evaluate the effectiveness of high-speed railway work has become a key research issue. This research focuses on the evaluation of train service plans. It analyzes the connotation of efficiency and effectiveness in train service plan evaluation and constructs an evaluation indices system from the following four perspectives: economic benefit, transportation efficiency, service effect, and passenger potential. Some key technologies including the management of data with complex logic and the display mode of different types of indices for the evaluation have been researched. Finally, the system has been developed and implemented based on the above research, which can provide intuitive and efficient auxiliary tools for evaluating and adjusting train service plans.
The aim of this research is to implement and evaluate a smart agriculture system based on big data analytics, which provides a new way to improve the efficiency of agricultural production in the context of glocalization and climate change. Our research utilizes advanced information technology and big data analytics to optimize the agricultural production process. The article begins by introducing the concept of smart agriculture and its importance in modern agriculture. Then, we describe the architecture of the smart agriculture system, including the key aspects of data collection, processing and analysis. The system collects farm data through a network of sensors and uses cloud computing technology for processing and analysis. For the evaluation of the system, we focus on both ease of use and performance. Through collaboration with agricultural practitioners, we collected feedback on the system's user interface, understanding of its functionality, and impact on the agricultural production process. In addition, system performance was assessed through indicators such as processing speed, data accuracy, and system stability. Test results show that the system performs well in terms of ease of use, with users being able to learn quickly and operate effectively. In terms of performance, the system demonstrated efficient data processing capability and stable performance. These results indicate that our smart agriculture system is not only technologically advanced but also user-friendly and can effectively assist in agricultural production decisions.
