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检索条件"任意字段=2017 International Symposium on Computer Science and Artificial Intelligence（ISCSAI 2017）"

共 421 条记录，以下是1-10 订阅

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GLCFANet: Liver Tumor Segmentation with Global and Local Information Cross-Fusion Attention 24

GLCFANet: Liver Tumor Segmentation with Global and Local Inf...

引用

5th international symposium on artificial intelligence for Medicine science, ISAIMS 2024

作者： Wang, Ge Jiang, Huiqin Ma, Ling Yang, Lei Gao, Jianbo Jiang, Yaojun The School of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou China The School of Electrical and Information Engineering Zhengzhou University Henan Zhengzhou China The First Affiliated Hospital Zhengzhou University Henan Zhengzhou China

ISBN: (纸本)9798400717826

Liver tumour edges are fuzzy and difficult to distinguish, making it challenging to clearly define the boundary. The traditional deep learning-based liver tumour segmentation method has limited segmentation accuracy due to its inability to comprehensively extract the tumour’s feature information. In this paper, we propose a liver tumor segmentation approach based on Global and Local Information Cross-Fusion Attention(GLCFA). Firstly, a bi-feature extractor module(BEM) is designed, which introduces dilated convolution combined with transformer to extract global features. At the same time, a CNN-based gated convolution block is designed to extract local features. Secondly, a bi-feature cross fusion module(BCFM) is proposed to cross-merge global and local information. This module adopts feature grouping and cross-splicing strategies, and then utilizes a channel global and local convolutional attention mechanism to dynamically calculate their feature importance weight coefficients, thereby effectively fusing the tumour features. Finally, a U-shaped segmentation architecture is employed to complete liver tumour segmentation. The proposed method is evaluated on the LITS2017 dataset and the 3DIRCADb-01 dataset, with the Dice coefficients improved by 3.54% and 5.35%, respectively. This proves the superiority of the method in segmenting liver tumours with ambiguous boundaries and is expected to provide a quantitative basis for accurate diagnosis of liver tumours. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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Multi-threshold segmentation of liver tumor images based on deep learning and improved whale optimization algorithm 24

Multi-threshold segmentation of liver tumor images based on ...

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5th international symposium on artificial intelligence for Medicine science, ISAIMS 2024

作者： Qiao, Xin Long, Xueling Li, Yongqi Liu, Taihui College of Computer Science and Technology Beihua University Jilin Jilin China

ISBN: (纸本)9798400717826

Liver tumors represent one of the most prevalent forms of malignant neoplasms globally, and their early diagnosis and treatment are essential for enhancing patient survival rates. However, existing multi-threshold segmentation methods for liver tumors exhibit sensitivity to noise, high computational complexity, and an inability to identify the optimal threshold. To solve these problems, this paper proposes a multi-threshold segmentation method for liver tumors based on deep learning combined with optimization algorithm. Initially, the 3DU-Net network model is employed to preprocess three-dimensional human volumetric data in order to mitigate the impact of noise or surrounding tissues on the segmentation threshold. Subsequently, we improve the position update formula of the whale optimization algorithm by optimizing its parameters, significantly enhancing convergence speed. The method then utilizes this refined whale optimization algorithm to maximize inter-class variance as its optimization objective and determines the optimal threshold accordingly. Finally, liver tumor segmentation is performed based on this optimal threshold. Furthermore, experiments were conducted using the public dataset from the Liver Segmentation Challenge (LITS2017). Aiming at maximizing inter-class variance, we compared our proposed method’s performance in segmenting liver tumors against four other established techniques. Experimental results demonstrate that our method outperforms these comparison techniques in terms of both convergence rate and segmentation quality. © 2024 Copyright held by the owner/author(s).

关键词： Patient treatment

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An Automatic Model for Lesson Plans Generation Based on Logical Chains and Prompt Tuning 23rd

An Automatic Model for Lesson Plans Generation Based on Logi...

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23rd international Conference on Web-Based Learning, ICWL 2024 was held in conjunction with the 9th international symposium on Emerging Technologies for Education, SETE 2024

作者： Ye, Feihong Cui, Huacong Ke, Chenhao Zhong, Weisen Zhang, Simin Zhang, Li School of Chemistry South China Normal University Guangzhou China Aberdeen Institute of Data Science and Artificial Intelligence South China Normal University Guangzhou China School of Mathematics Sun Yat-sen University Zhuhai China School of Entrepreneurship South China Normal University Guangzhou China Shuntu Management Consulting Co. Ltd. Guangzhou China Guangzhou China

ISBN: (纸本)9789819644063

With the swift advancement of artificial intelligence technology, large-scale language models have demonstrated substantial potential in the teaching plan generation of education field. Nonetheless, current models still encounter challenges such as insufficient granularity of content, lack of expertise, and a low degree of structure in the task of generating lesson plans. This research introduces a fine-tuned EDU-GPT model based on logical chains and prompts, employing a dual-track parallel enhancement mechanism to amalgamate a high-quality knowledge base, thereby achieving high-quality and one-time generation of lesson plans. Experiment results reveal that the EDU-GPT significantly surpasses the GPT-4 baseline model in terms of granularity (8.90 points), accuracy (8.84 points), and structure (8.70 points) with a statistical significance (p © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge engineering

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User Needs and Wishes in Smart Homes What can artificial intelligence contribute? 14

User Needs and Wishes in Smart Homes What can Artificial Int...

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14th international symposium on Pervasive Systems, Algorithms and Networks (I-SPAN) / 11th international Conference on Frontier of computer science and Technology (FCST) / 3rd international symposium of Creative Computing (ISCC)

作者： Zimmermann, Gottfried Ableitner, Tobias Strobbe, Christophe Stuttgart Media Univ Respons Media Experience Res Grp Stuttgart Germany

ISBN: (纸本)9781538608401

Smart homes are an application domain that is inherently connected to explicit and implicit user interaction. Following a user-centred design approach, we have to look at the user's needs and preferences when designing systems and components for smart homes. In this paper, we provide an overview on what users in Germany want and wish from technology in their homes, based on former studies. We focus on older persons and persons with severe physical disabilities and their needs, as identified by recent studies and our own work. We specify possible contributions from artificial intelligence to fulfil these needs and wishes in future smart home systems.

关键词： barrier-free smart home AI user needs older users users with physical disabilities

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A Fast and Simple Computational Model to Reach the Global Optimum in the Field of Experimental sciences

A Fast and Simple Computational Model to Reach the Global Op...

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2017 international artificial intelligence and Data Processing symposium (IDAP)

作者： Akben, Selahaddin Batuhan Osmaniye Korkut Ata Univ Bahce Vocat Sch Osmaniye Turkey

ISBN: (纸本)9781538618806

In the field of experimental science, costly and time consuming experiments are performed, especially in the food engineering. In most of these experiments, interactions between the variables and experimental results are tested to detect the desired experimental results. For these types of experiments, optimization methods are needed to achieve the desired optimal experimental result rapidly. Moreover, these methods should be able to simply apply without computer support. Since the existing complex mathematical optimization methods cannot satisfy these needs, a simple new method was proposed in this study, especially for the food engineering. The proposed method divides the sampling space into 4(the number of variables) segments in each iteration. Then determines the center of one segment as the coordinates of the global optimum. Moreover, each iteration only requires the (1+(2xthe number of variables)) sampling. The proposed method was tested on a sample food engineering experiment previously performed using Response Surface Methodology (RSM). Then, it's superiority and usability was demonstrated comparatively. Finally, a simple and effective optimization method that does not require mathematical operations was proposed.

关键词： Computational Model Optimization Experimental science Food Engineering Segmented Experimental Design

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Static Analysis: 24th international symposium, SAS 2017 New York, NY, USA, august 30 - september 1, 2017 proceedings 24th

Static Analysis: 24th international symposium, SAS 2017 New ...

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24th international symposium on Static Analysis, SAS 2017

作者： Ranzato, Francesco Dipartimento di Matematica University of Padova Padua Italy

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Twitter data analysis and visualizations using the R language on top of the Hadoop platform 15

Twitter data analysis and visualizations using the R languag...

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15th IEEE international symposium on Applied Machine intelligence and Informatics (SAMI)

作者： Sarnovsky, Martin Butka, Peter Huzvarova, Andrea Tech Univ Kosice Dept Cybernet & Artificial Intelligence Fac Elect Engn & Informat Letna 9-A Kosice 04001 Slovakia

ISBN: (纸本)9781509056552

The main objective of the work presented within this paper was to design and implement the system for twitter data analysis and visualization in R environment using the big data processing technologies. Our focus was to leverage existing big data processing frameworks with its storage and computational capabilities to support the analytical functions implemented in R language. We decided to build the backend on top of the Apache Hadoop framework including the Hadoop HDFS as a distributed filesystem and MapReduce as a distributed computation paradigm. RHadoop packages were then used to connect the R environment to the processing layer and to design and implement the analytical functions in a distributed manner. Visualizations were implemented on top of the solution as a RShiny application.

关键词： Data visualization Twitter Data analysis Tagging computer architecture Big Data Sparks

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Knowledge and Systems sciences 1st ed. 2017

引用

丛书名： Communications in computer and Information science

2017年

作者： Jian Chen Thanaruk Theeramunkong Thepachai Supnithi Xijin Tang

ISBN: (数字)9789811069895

ISBN: (纸本)9789811069888

This book constitutes the refereed proceedings of the 18th international symposium, KSS 2017, held in Bangkok, Thailand, in November 2017. The21 revised full papers presented were carefully reviewed and selected from 63 submissions. This year KSS 2017 provides opportunities for presenting interesting new research results, facilitating interdisciplinary discussions, and leading to knowledge transfer under the theme of "artificial intelligence and Information Systems for Knowledge, Technology and Service Management".

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A retinal vessel detection approach using convolution neural network

A retinal vessel detection approach using convolution neural...

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2017 international artificial intelligence and Data Processing symposium, IDAP 2017

作者： Şengür, Abdulkadir Guo, Yanhui Budak, Ümit Vespa, Lucas J. Firat University Technology Faculty Electrical and Electronics Engineering Dept. Elaziǧ Turkey University of Illinois at Springfield Department of Computer Science IL United States Bitlis Eren University Electrical and Electronics Engineering Dept. Elaziǧ Turkey

ISBN: (纸本)9781538618806

computer-aided detection (CAD) provides an efficient way to assist doctors to interpret fundus images. In a CAD system, retinal vessel (RV) detection is an important step to identify the retinal disease regions automatically and accurately. However, RV detection is still a challenging problem due to variations in morphology of the vessels on a noisy background. In this paper, we formulate the detection task as a classification problem and solve it using a convolutional neural network (CNN) as a two-class classifier. The proposed model has 2 convolution layers, 2 pooling layers, 1 dropout layer and 1 loss layer. The proposed CNN achieves better performance and significantly outperforms the state-of-the-art for automatic retinal vessel segmentation on the DRIVE dataset with 91.78% accuracy and 0.96743 AUC score. We further compare our result with several state of the art methods based on AUC values. The comparison shows that our proposal yields the second best AUC value. This demonstrates the efficiency of the proposed method which has no pre-processing steps. © 2017 IEEE.

关键词： Image segmentation

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Smart home futures: Algorithmic challenges and opportunities 14

Smart home futures: Algorithmic challenges and opportunities

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14th international symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2017, 11th international Conference on Frontier of computer science and Technology, FCST 2017 and 3rd international symposium of Creative Computing, ISCC 2017

作者： Kazmi, Hussain Amayri, Manar Mehmood, Fahad ELECTA and Data Science Group KU Leuven and Enervalis Belgium G-SCOP Lab Grenoble Institute of Technology France Pakistan

ISBN: (纸本)9781538608401

Humans are increasingly spending their time indoors. This, along with higher wealth levels and rise of internet of things, has provided designers and planners the opportunity to reimagine living spaces. The smart homes that have arisen out of this reimagining come in many different shapes;but to gain widespread acceptance they have to increase the utility of building occupants in some meaningful way while not being intrusive. The most straightforward way of achieving this end goal is assumed to be through artificial intelligence. In this paper, we take a critical look at some algorithmic approaches that have been formulated to do so and the opportunities they will create in the short term. We also present some key challenges that must be overcome before these opportunities can be realized in practice. © 2017 IEEE.

关键词： Automation

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