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检索条件"丛书名=Communications in Computer and Information Science"

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Automated Segmentation of Ischemic Stroke Lesions in Non-contrast Computed Tomography Images for Enhanced Treatment and Prognosis 1st

Automated Segmentation of Ischemic Stroke Lesions in Non-con...

引用

1st MICCAI Meets Africa Workshop

作者： Musah, Toufiq Adjei, Prince Ebenezer Otoo, Kojo Obed Kwame Nkrumah Univ Sci & Technol Dept Comp Engn Kumasi Ghana Kumasi Ctr Collaborat Res Trop Med Kumasi Ghana Komfo Anokye Teaching Hosp Dept Radiol Kumasi Ghana

ISBN: (纸本)9783031791024;9783031791031

Stroke is the second leading cause of death worldwide, and is increasingly prevalent in low- and middle-income countries (LMICs). Timely interventions can significantly influence stroke survivability and the quality of life after treatment. However, the standard and most widely available imaging method for confirming strokes and their sub-types, the NCCT, is more challenging and time-consuming to employ in cases of ischemic stroke. For this reason, we developed an automated method for ischemic stroke lesion segmentation in NCCTs using the nnU-Net framework, aimed at enhancing early treatment and improving the prognosis of ischemic stroke patients. We achieved Dice scores of 0.596 and Intersection over Union (IoU) scores of 0.501 on the sampled dataset. After adjusting for outliers, these scores improved to 0.752 for the Dice score and 0.643 for the IoU. Proper delineation of the region of infarction can help clinicians better assess the potential impact of the infarction, and guide treatment procedures.

关键词： Ischemic Stroke Acute infarcts Non-Contrast CT nnU-Net

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A New Efficient Genetic Algorithm for Solving the Kidney Exchange Problem 1

引用

4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Ortega-Bedoya, Juan Felipe Otero-Villamarin, Juan Felipe Morillo-Torres, Daniel Gatica, Gustavo Pontificia Univ Javeriana Cali Dept Civil & Ind Engn Fac Engn Cali Colombia Univ Andres Bello Fac Engn Santiago Chile

ISBN: (数字)9783031832079

ISBN: (纸本)9783031832062;9783031832079

In the United States, renal complications rank eighth as the most common cause of death, creating waiting lists approaching 100 000 people, with only around 25 000 accessing kidney transplants. Faced with the challenge of meeting the total demand for necessary kidneys, renal exchange programs involving donors, recipients, and altruists have emerged, aiming to establish exchanges with chains or shortened cycles for logistical reasons. With the increasing prevalence of these exchanges and programs in both the United States and Europe, this paper presents a genetic algorithm designed to address them. This strategy is conceived to tackle larger instances, overcoming the limitations of conventional mathematical models reported in the literature. The application of this metaheuristic provides an effective and scalable solution, improving kidney allocation in the context of high demand and logistical constraints, offering responses to instances that cannot be addressed exactly.

关键词： Metaheuristic Genetic Algorithm Chains and cycles Donors and Altruists Kidney Exchange Problem

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EnigmaArt: Dual Image Encryption and Compression via Autoencoding and Pixel Conversion 4th

EnigmaArt: Dual Image Encryption and Compression via Autoenc...

引用

4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Tiwari, Naveen Kumar Rajput, Shyam Singh Yadav, Aditya Natl Inst Technol Patna Patna Bihar India Rajkiya Engn Coll Kannauj Kannauj UP India

ISBN: (纸本)9783031837890;9783031837906

Securing sensitive visual data is essential in the digital age. Our research presents a sophisticated model of image encryption that combines RNA pixel conversion with an autoencoder. A convolutional neural network (CNN) is used for efficient feature extraction. Convolutional encoding and RNA pixel conversion, which map pixels to RNA sequences (Adenine, Cytosine, Guanine, Uracil) enabling stronger encryption, are the model's primary innovations. By guaranteeing that every encryption instance is distinct, this dynamic technique strengthens security. Our approach ensures security and picture integrity by demonstrating robust encryption capabilities with minimum reconstruction loss (0.0617), as tested on the MNIST dataset. Furthermore, the encryption procedure is optimized by a fractional order optimizer, FGL Adam, which incorporates weight decay, learning rate modifications, and a security improvement factor.

关键词： Image Encryption Autoencoder Fractional Optimizer

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Semantic Abstractions for Multi-label Classification 4th

Semantic Abstractions for Multi-label Classification

引用

4th International Conference on Artificial Intelligence Logic and Applications

作者： Wang, Xiaomei Xuan, Xiaohua Xu, Qing Cai, Hua Shen, Weilin UniDT Shanghai Peoples R China

ISBN: (纸本)9789819603534;9789819603541

Recent advances in multi-label classification (MLC) have achieved promising performance based on visual representations from convolutional neural networks. Since images for MLC usually contain complicated and abundant content, utilizing only visual information is insufficient and may deviate from global context. In this paper, we integrate textual representations, denoted as semantic abstractions, with visual features to comprehensively characterize the image content. With the multi-modal information, a novel Visual-Semantic (Vise) Transformer architecture is designed for investigating the ability of the proposed abstractive semantic representations. Furthermore, we inject global context information into each local visual features, which attempts to capture the significant and concrete expressions of regions under the premise of the whole image. Empirically, extensive experiments and comparisons are conducted on the challenging benchmarks of MSCOCO and NUS-WIDE datasets. The results demonstrate the superiority of our proposed method compared with prior works with significant performance improvement for multi-label classification task.

关键词： Multi-label classification Semantic abstractions

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Cost-Effective Tension Testing System for Plastics Industries 1

引用

4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Guerra, Michael Abedrabbo, Faruk Moya, Viviana Quito, Angelica Mosquera, Guillermo Pablo Vasconez, Juan Univ Int Ecuador UIDE Fac Ciencias Tecn Quito 170411 Ecuador Univ Andres Bello Energy Transformat Ctr Fac Engn Santiago 7500971 Chile

ISBN: (数字)9783031832079

ISBN: (纸本)9783031832062;9783031832079

To determine mechanical properties or other relevant parameters of plastic materials, devices such as tensile testing machines are widely used. However, these procedures are performed with relatively expensive equipment that is not easily available in educational institutions or companies. For this reason, the following work presents the development and validation of a cost-effective machine capable of performing tensile tests with uniaxial loads on plastic materials. The main characteristics regarding the mechanical, electronic, and software design that have been developed and implemented for the correct operation of the machine are briefly and clearly disclosed. Likewise, the calibration process was performed using a known spring elastic constant. Additionally, the application and validation of the machine were carried out by comparing tensile tests done on the plastic straps with tests done in another machine. The results obtained by the developed machine are similar to those estimated by the validation machine in terms of mass and strain, obtaining an average accuracy of 2.46% and 2.17%, respectively. Finally, the machine sale price was estimated at $1,908 USD, a reduction cost of 68.2% compared with other similar machines in the industry.

关键词： Tensile test cost-effective machine plastic materials mechatronics design

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From Zero to Hero: When a Simple Line Can Make All the Difference The Case of Progress Bars in Educational Online Courses 8th

From Zero to Hero: When a Simple Line Can Make All the Diffe...

引用

8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Marquardt, Kai Kia, Elias Koziolek, Anne Happe, Lucia Karlsruhe Inst Technol KIT Karlsruhe Germany

ISBN: (纸本)9783031838446;9783031838453

Online platforms with automated feedback mechanisms offer a solution, ensuring all students receive timely and scalable feedback. Despite their potential, studies specifically assessing the impact of different feedback types in online learning environments are limited. This work-in-progress study addresses this gap by exploring the use of progress bars as a feedback tool in educational online courses. Through a pilot case-control study involving 21 students, we aim to evaluate the potential of progress bars to improve student motivation and engagement. Our findings indicate the potential of progress bars in educational online courses to enhance the learning experience by providing a clear visualization of progress, fostering a sense of accomplishment, and motivating students to complete tasks. These insights underscore the importance of integrating progress bars into online learning platforms, highlighting their untapped potential to enrich educational outcomes. By laying the groundwork for further exploration, this research advocates for the broader adoption of simple yet effective feedback mechanisms, ultimately aiming to enhance online education and benefit both students and educators.

关键词： Progress bar Gamification Digital education Online Learning visualization

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DDoS Attack Detection Using Explainable AI in Machine Learning 4th

DDoS Attack Detection Using Explainable AI in Machine Learni...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Jena, Sudhansu Kumar Ranjan, Ashish Singh, Vibhav Prakash MNNIT Allahabad Prayagraj India IIIT Sonepat Khewra Haryana India

ISBN: (纸本)9783031837951;9783031837968

In network security, Distributed Denial of Service (DDoS) attacks disrupt critical online services and are becoming increasingly sophisticated. Machine learning is essential for detecting and classifying these attacks, using novel datasets featuring modern attack types. As DDoS attacks target crucial platforms like banking and social networks, intrusion detection systems (IDS) integrated with machine learning are vital. Achieving 100% accuracy in attack detection remains challenging due to the lack of comprehensive datasets including newer attack types such as UDP-flood, SIDDoS, HTTP-flood, and Smurf. Machine learning classifies network traffic based on various features, distinguishing normal from DDoS traffic. Explainable AI (XAI) methods, including LIME and SHAP, enhance model interpretability and transparency, offering insights into feature contributions for attack detection. Related works highlight the importance of new datasets and information theory-based techniques, such as information entropy and correlation methods, for DDoS detection. Hybrid approaches and SDN implementations also show promise in improving accuracy. As DDoS attacks evolve and IoT devices increase, this paper aims to enhance network security and protect critical online servies.

关键词： DDos Attack LIME SHAP Explainable AI

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SF-SARnet: Custom SAR Image Generation for Specific Locations 5th

SF-SARnet: Custom SAR Image Generation for Specific Location...

引用

5th China Intelligent Robotics Academic Conference

作者： Ju, Moran Li, Mulin Jin, Sinian Fang, Da Niu, Buniu Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116026 Liaoning Peoples R China State Grid Liaoning Elect Co Ltd Dalian Shahekou Dist Power Supply Branch Dalian Peoples R China

ISBN: (纸本)9789819616138;9789819616145

The scarcity of SAR images and their time-consuming and laborious labeling is a pressing issue in the development of SAR detection algorithms. In addition, the existing generation algorithms do not consider the complex context of real SAR images, resulting in low-quality of the results. To solve these difficulties, we introduce a SAR image generation model consisting of a scene fusion module, generator and discriminator called SF-SARnet. The model accepts target location information as input and generates a mask map, which is then fed to the network for generation. Our main contributions include the following: (1) We design a generator based on the traditional encoder-decoder structure, on top of which we add jump connections at each odd layer. (2) To increase the quality of the fake generation results, we design a loss function that incorporates perceptual loss. (3) To verify the model's performance, comparative verification was performed on the HRSID dataset. The results indicate that our model outperforms the others. Moreover, the generated images can be conveniently used for other related tasks.

关键词： Generative Adversarial Networks Image Generation Synthetic Aperture Radar

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On Numerical Solution of Block-Structured Discrete Systems 2nd

On Numerical Solution of Block-Structured Discrete Systems

引用

2nd International Conference on information Technologies and Their Applications

作者： Asadova, Jamila Azerbaijan Univ Architecture & Construct Inst Control Syst Baku Azerbaijan

ISBN: (纸本)9783031734199;9783031734205

The paper presents a numerical methodology for solving high-dimensional discrete dynamical systems characterized by a block structure with boundary conditions that are non-separated between blocks. Direct application of classical methods of transferring boundary conditions is inefficient, since the block structure of the conditions, as for many other classes of problems, allows to speed up their solution considerably. In this approach the decomposition of complex processes into simpler sub-processes with known mathematical models is used, and the decomposition of a complex process is performed in such a way that the intermediate states of sub-processes do not affect each other, i.e. they are independent, and the connection between sub-processes is implemented only through their input and output states. In this case, as a rule, the conditions determining the links between sub-processes are characterized by poorly occupied Jacobi matrices. In the paper formulas that perform transferring of non-separated boundary conditions are presented and practical results demonstrating the efficacy of the proposed approach on a test problem are provided.

关键词： Discrete Dynamic Models Decomposition of Complex Objects Technique for Transferring Boundary Conditions Poorly Occupied Jacobi Matrices Discrete Systems with Concentrated or Distributed Parameters

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An Out-of-Sample Clustering Ensemble Method for Defect Detection and Classification in Metal Additive Manufacturing 5th

An Out-of-Sample Clustering Ensemble Method for Defect Detec...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Chabanet, Sylvain Orta, Adil Han Kersemans, Mathias Univ Ghent Dept Mat Text & Chem Engn Mech Mat & Struct Technol Pk 46 B-9052 Zwijnaarde Belgium SIM STREAM Technol Pk 48 B-9052 Zwijnaarde Belgium

ISBN: (纸本)9783031807596;9783031807602

Unsupervised learning methods, and in particular clustering algorithms, have found many applications in manufacturing, ranging from customer segmentation to quality monitoring. It has, however, been demonstrated that no clustering algorithm can be suitable for all applications and data structures. Ensembles of clustering algorithms have emerged as a partial answer to this limitation, aiming at increasing the robustness of clustering algorithms by aggregating partitions discovered by many models. This robustness, however, comes at the cost of increased computational requirements to generate and aggregate partitions. The ability to quickly predict a cluster for new, out-of-sample data points without having to recompute the whole clustering algorithm from scratch is, therefore, a desirable property for many real-world applications. Such out-of-sample methods, however, are not straightforward in the context of clustering ensemble, and few models include one. As a step toward filling this gap, this article proposes a novel out-of-sample method for clustering ensemble algorithms following the median consensus framework. An application of this method is proposed for the detection and classification of defects in metal parts produced by additive manufacturing processes. The proposed method is compared with state-of-the-art algorithms on both artificial and experimental datasets, demonstrating its high performance and robustness.

关键词： Clustering ensemble Resonance testing Additive manufacturing Out-of-sample

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