Application analysis of ai technology combined with spiral CT scanning in early lung cancer screening

At present, the incidence and fatality rate of lung cancer in China rank first among all malignant tumors. Despite the continuous development and improvement of China's medical level, the overall 5-year survival rate of lung cancer patients is still lower than 20% and is staged. A number of studies have confirmed that early diagnosis and treatment of early stage lung cancer is of great significance to improve the prognosis of patients. In recent years, artificial intelligence technology has gradually begun to be applied in oncology. ai is used in cancer screening, clinical diagnosis, radiation therapy (image acquisition, at-risk organ segmentation, image calibration and delivery) and other aspects of rapid development. However, whether medical ai can be socialized depends on the public's attitude and acceptance to a certain extent. However, at present, there are few studies on the diagnosis of early lung cancer by AI technology combined with SCT scanning. In view of this, this study applied the combined method in early lung cancer screening, aiming to find a safe and efficient screening mode and provide a reference for clinical diagnosis and treatment.Comment: This article was accepted by Frontiers in Computing and Intelligent Systems https://drpress.org/ojs/index.php/fcis/article/view/15781. arXiv admin note: text overlap with arXiv:nlin/0508031 by other author

Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation

The process of transforming input images into corresponding textual explanations stands as a crucial and complex endeavor within the domains of computer vision and natural language processing. In this paper, we propose an innovative ensemble approach that harnesses the capabilities of Contrastive Language-Image Pretraining models

Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific AttentionPooling

The objective of this study is to improve automated feedback tools designed for English Language Learners (ELLs) through the utilization of data science techniques encompassing machine learning, natural language processing, and educational data analytics. Automated essay scoring (AES) research has made strides in evaluating written essays, but it often overlooks the specific needs of English Language Learners (ELLs) in language development. This study explores the application of BERT-related techniques to enhance the assessment of ELLs' writing proficiency within AES. To address the specific needs of ELLs, we propose the use of DeBERTa, a state-of-the-art neural language model, for improving automated feedback tools. DeBERTa, pretrained on large text corpora using self-supervised learning, learns universal language representations adaptable to various natural language understanding tasks. The model incorporates several innovative techniques, including adversarial training through Adversarial Weights Perturbation (AWP) and Metric-specific AttentionPooling (6 kinds of AP) for each label in the competition. The primary focus of this research is to investigate the impact of hyperparameters, particularly the adversarial learning rate, on the performance of the model. By fine-tuning the hyperparameter tuning process, including the influence of 6AP and AWP, the resulting models can provide more accurate evaluations of language proficiency and support tailored learning tasks for ELLs. This work has the potential to significantly benefit ELLs by improving their English language proficiency and facilitating their educational journey.Comment: This article was accepted by 2023 International Conference on Information Network and Computer Communications(INCC

Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method

In the realm of patent document analysis, assessing semantic similarity between phrases presents a significant challenge, notably amplifying the inherent complexities of Cooperative Patent Classification (CPC) research. Firstly, this study addresses these challenges, recognizing early CPC work while acknowledging past struggles with language barriers and document intricacy. Secondly, it underscores the persisting difficulties of CPC research. To overcome these challenges and bolster the CPC system, This paper presents two key innovations. Firstly, it introduces an ensemble approach that incorporates four BERT-related models, enhancing semantic similarity accuracy through weighted averaging. Secondly, a novel text preprocessing method tailored for patent documents is introduced, featuring a distinctive input structure with token scoring that aids in capturing semantic relationships during CPC context training, utilizing BCELoss. Our experimental findings conclusively establish the effectiveness of both our Ensemble Model and novel text processing strategies when deployed on the U.S. Patent Phrase to Phrase Matching dataset.Comment: It accepted by The 6th International Conference on Machine Learning and Machine Intelligence (MLMI 2023

Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms

This paper focuses on the analysis of the application effectiveness of the integration of deep learning and computer vision technologies. Deep learning achieves a historic breakthrough by constructing hierarchical neural networks, enabling end-to-end feature learning and semantic understanding of images. The successful experiences in the field of computer vision provide strong support for training deep learning algorithms. The tight integration of these two fields has given rise to a new generation of advanced computer vision systems, significantly surpassing traditional methods in tasks such as machine vision image classification and object detection. In this paper, typical image classification cases are combined to analyze the superior performance of deep neural network models while also pointing out their limitations in generalization and interpretability, proposing directions for future improvements. Overall, the efficient integration and development trend of deep learning with massive visual data will continue to drive technological breakthroughs and application expansion in the field of computer vision, making it possible to build truly intelligent machine vision systems. This deepening fusion paradigm will powerfully promote unprecedented tasks and functions in computer vision, providing stronger development momentum for related disciplines and industries

Bifacial dye-sensitized solar cells : a strategy to enhance overall efficiency based on transparent polyaniline electrode

Dye-sensitized solar cell (DSSC) is a promising solution to global energy and environmental problems because of its clean, low-cost, high efficiency, good durability, and easy fabrication. However, enhancing the efficiency of the DSSC still is an important issue. Here we devise a bifacial DSSC based on a transparent polyaniline (PANI) counter electrode (CE). Owing to the sunlight irradiation simultaneously from the front and the rear sides, more dye molecules are excited and more carriers are generated, which results in the enhancement of short-circuit current density and therefore overall conversion efficiency. The photoelectric properties of PANI can be improved by modifying with 4-aminothiophenol (4-ATP). The bifacial DSSC with 4-ATP/PANI CE achieves a light-to-electric energy conversion efficiency of 8.35%, which is increased by ,24.6% compared to the DSSC irradiated from the front only. This new concept along with promising results provides a new approach for enhancing the photovoltaic performances of solar cells.The authors acknowledge the financial joint support by the National High Technology Research and Development Program of China (No. 2009AA03Z217), the National Natural Science Foundation of China (nos. 90922028, U1205112, 51002053, 61306077), Seed Fund from Ocean University of China, and Fundamental Research Funds for the Central Universities (201313001)

Synthesis, characterization and properties of polyaniline/expanded vermiculite intercalated nanocomposite

The synthesis characterization and conductivities of polyaniline/expanded vermiculite intercalated nanocomposite are presented in this paper. The conductive emeraldine salt form of polyaniline is inserted into the interlayer of expanded vermiculite to produce the nanocomposite with high conductivity. The structures and properties are characterized by transmission electron microscopy x-ray diffraction spectroscopy fourier transform infrared spectroscopy thermogravimetry analysis and by the measurements of conductivity and stability. The results show that an intercalated nanocomposite with high conductivity and stability is obtained. The synthesis conditions are optimized to obtain the highest conductivity which is 6.80 S cm−1

Phylogenomics Resolves the Phylogeny of Theaceae by Using Low-Copy and Multi-Copy Nuclear Gene Makers and Uncovers a Fast Radiation Event Contributing to Tea Plants Diversity

Tea is one of the three most popular nonalcoholic beverages globally and has extremely high economic and cultural value. Currently, the classification, taxonomy, and evolutionary history of the tea family are largely elusive, including phylogeny, divergence, speciation, and diversity. For understanding the evolutionary history and dynamics of species diversity in Theaceae, a robust phylogenetic framework based on 1785 low-copy and 79,103 multi-copy nuclear genes from 91 tea plant genomes and transcriptome datasets had been reconstructed. Our results maximumly supported that the tribes Stewartieae and Gordonieae are successive sister groups to the tribe Theeae from both coalescent and super matrix ML tree analyses. Moreover, in the most evolved tribe, Theeae, the monophyletic genera Pyrenaria, Apterosperma, and Polyspora are the successive sister groups of Camellia. We also yield a well-resolved relationship of Camellia, which contains the vast majority of Theaceae species richness. Molecular dating suggests that Theaceae originated in the late L-Cretaceous, with subsequent early radiation under the Early Eocene Climatic Optimal (EECO) for the three tribes. A diversification rate shift was detected in the common ancestors of Camellia with subsequent acceleration in speciation rate under the climate optimum in the early Miocene. These results provide a phylogenetic framework and new insights into factors that likely have contributed to the survival of Theaceae, especially a successful radiation event of genus Camellia members to subtropic/tropic regions. These novel findings will facilitate the efficient conservation and utilization of germplasm resources for breeding cultivated tea and oil-tea. Collectively, these results provide a foundation for further morphological and functional evolutionary analyses across Theaceae